Separate model reasoning selections and reduce agent notification noise

app/src/ai/agent_management/agent_management_model.rs

@@ -249,7 +249,7 @@ impl AgentNotificationsModel {
             terminal_surface_id,
             conversation_id,
             // We shouldn't trigger toasts when restoring conversations on startup.
-            update: ConversationStatusUpdate::Changed { .. },
+            update: ConversationStatusUpdate::Changed { prev_status },
             ..
         } = event
         else {
@@ -266,6 +266,21 @@ impl AgentNotificationsModel {
         }
 
         let status = updated_conversation.status().clone();
+        if &status == prev_status {
+            return;
+        }
+        // Workers report their results to the orchestrator. Only a worker that
+        // needs user input should interrupt the user independently.
+        if updated_conversation.is_child_agent_conversation()
+            && !status.should_notify_for_agent(true)
+        {
+            self.pending_artifacts.remove(conversation_id);
+            self.remove_notification_by_source(
+                NotificationOrigin::Conversation(*conversation_id),
+                ctx,
+            );
+            return;
+        }
         let latest_query = updated_conversation.latest_user_query();
         if FeatureFlag::HOANotifications.is_enabled() {
             self.handle_history_event_for_mailbox(
@@ -283,6 +298,12 @@ impl AgentNotificationsModel {
             return;
         }
 
+        if matches!(status, ConversationStatus::Success)
+            && QueuedQueryModel::as_ref(ctx).has_autofireable_prompt(*conversation_id)
+        {
+            return;
+        }
+
         if is_terminal_view_visible(*terminal_surface_id, ctx) {
             return;
         }
@@ -354,18 +375,10 @@ impl AgentNotificationsModel {
                 );
             }
             ConversationStatus::Cancelled => {
-                let artifacts = self.flush_pending_artifacts(conversation_id);
+                // Steering and replacing a turn cancels the old generation;
-                self.add_notification(
+                // that is not a separate task-completion notification.
-                    title,
+                self.pending_artifacts.remove(&conversation_id);
-                    "Task was cancelled.".to_owned(),
+                self.remove_notification_by_source(origin, ctx);
-                    NotificationCategory::Complete,
-                    oz_agent,
-                    origin,
-                    terminal_view_id,
-                    artifacts,
-                    metadata.branch,
-                    ctx,
-                );
             }
             ConversationStatus::Blocked { blocked_action } => {
                 self.add_notification(
@@ -484,6 +497,10 @@ pub enum AgentManagementEvent {
 }
 
 impl ConversationStatus {
+    pub(crate) fn should_notify_for_agent(&self, is_child: bool) -> bool {
+        self.should_trigger_notification() && (!is_child || self.is_blocked())
+    }
+
     /// Returns true if the updating the conversation with this status should trigger some
     /// notification to the user.
     ///

app/src/ai/agent_management/agent_management_model_tests.rs

@@ -297,6 +297,36 @@ fn should_trigger_notification_returns_false_for_cancelled() {
     assert!(!ConversationStatus::Cancelled.should_trigger_notification());
 }
 
+#[test]
+fn worker_results_do_not_interrupt_the_orchestrator() {
+    for status in [ConversationStatus::Success, ConversationStatus::Error] {
+        assert!(!status.should_notify_for_agent(true));
+        assert!(status.should_notify_for_agent(false));
+    }
+}
+
+#[test]
+fn worker_permission_requests_still_notify() {
+    let status = ConversationStatus::Blocked {
+        blocked_action: "Approve deployment".to_owned(),
+    };
+    assert!(status.should_notify_for_agent(true));
+    assert!(status.should_notify_for_agent(false));
+}
+
+#[test]
+fn running_recovering_and_cancelled_agents_do_not_notify() {
+    for status in [
+        ConversationStatus::InProgress,
+        ConversationStatus::TransientError,
+        ConversationStatus::WaitingForEvents,
+        ConversationStatus::Cancelled,
+    ] {
+        assert!(!status.should_notify_for_agent(true));
+        assert!(!status.should_notify_for_agent(false));
+    }
+}
+
 // Mailbox suppression for non-terminal status updates. In App::test the
 // `is_conversation_open` gate always returns false, so the
 // WaitingForEvents and InProgress arms both clear stale notifications

app/src/ai/crosscheck/reviewer.rs

@@ -268,7 +268,7 @@ impl CrosscheckReviewer {
 
         let effective_model = config.model.unwrap_or(model_id);
 
-        let request_body = serde_json::json!({
+        let mut request_body = serde_json::json!({
             "model": effective_model,
             "messages": [
                 {
@@ -286,6 +286,10 @@ impl CrosscheckReviewer {
             "stream": false
         });
 
+        if let Some(effort) = config.reasoning_effort {
+            request_body["reasoning_effort"] = effort.into();
+        }
+
         // Use a non-streaming request by calling the same endpoint but with stream: false
         let url = format!("{}/chat/completions", config.base_url.trim_end_matches('/'));
 

app/src/ai/execution_profiles/editor/mod.rs

@@ -19,7 +19,9 @@ use warpui::{
 };
 
 use crate::ai::blocklist::BlocklistAIPermissions;
-use crate::ai::execution_profiles::model_menu_items::available_model_menu_items;
+use crate::ai::execution_profiles::model_menu_items::{
+    available_model_menu_items, collapse_model_reasoning_choices, reasoning_choices_for_model,
+};
 use crate::ai::execution_profiles::profiles::{
     AIExecutionProfilesModel, AIExecutionProfilesModelEvent, ClientProfileId,
 };
@@ -34,6 +36,7 @@ use crate::cloud_object::model::generic_string_model::StringModel;
 use crate::editor::{
     EditorView, Event as EditorEvent, InteractionState, SingleLineEditorOptions, TextOptions,
 };
+use crate::menu::{MenuItem, MenuItemFields, MenuTooltipPosition};
 use crate::pane_group::focus_state::PaneFocusHandle;
 use crate::pane_group::pane::view;
 use crate::pane_group::{BackingView, PaneConfiguration, PaneEvent};
@@ -132,6 +135,13 @@ pub enum ExecutionProfileEditorViewEvent {
     Pane(PaneEvent),
 }
 
+#[derive(Debug, Clone, Copy, PartialEq)]
+pub enum ProfileModelRole {
+    Base,
+    FullTerminalUse,
+    ComputerUse,
+}
+
 #[derive(Debug, Clone, PartialEq)]
 pub enum ExecutionProfileEditorViewAction {
     Save,
@@ -139,6 +149,10 @@ pub enum ExecutionProfileEditorViewAction {
     SetBaseModel {
         id: LLMId,
     },
+    SetModelReasoning {
+        role: ProfileModelRole,
+        id: LLMId,
+    },
     /// Fired continuously while the user drags the context window slider.
     ContextWindowSliderDragged {
         value: u32,
@@ -198,6 +212,10 @@ pub struct ExecutionProfileEditorView {
     full_terminal_use_model_dropdown:
         ViewHandle<FilterableDropdown<ExecutionProfileEditorViewAction>>,
     computer_use_model_dropdown: ViewHandle<FilterableDropdown<ExecutionProfileEditorViewAction>>,
+    base_reasoning_dropdown: ViewHandle<Dropdown<ExecutionProfileEditorViewAction>>,
+    full_terminal_reasoning_dropdown: ViewHandle<Dropdown<ExecutionProfileEditorViewAction>>,
+    computer_use_reasoning_dropdown: ViewHandle<Dropdown<ExecutionProfileEditorViewAction>>,
+    reasoning_roles: Vec<ProfileModelRole>,
     permission_mode_dropdown: ViewHandle<Dropdown<ExecutionProfileEditorViewAction>>,
     command_denylist_editor: ViewHandle<SubmittableTextInput>,
     command_allowlist_editor: ViewHandle<SubmittableTextInput>,
@@ -301,6 +319,9 @@ impl ExecutionProfileEditorView {
             dropdown.set_menu_width(MODEL_MENU_WIDTH, ctx);
             dropdown
         });
+        let base_reasoning_dropdown = ctx.add_typed_action_view(Dropdown::new);
+        let full_terminal_reasoning_dropdown = ctx.add_typed_action_view(Dropdown::new);
+        let computer_use_reasoning_dropdown = ctx.add_typed_action_view(Dropdown::new);
         let command_allowlist_editor = ctx.add_typed_action_view(|ctx| {
             let mut input = SubmittableTextInput::new(ctx).validate_on_edit(|s| Regex::new(s).is_ok());
             input.set_placeholder_text("e.g. ^cargo test$", ctx);
@@ -361,6 +382,10 @@ impl ExecutionProfileEditorView {
             coding_model_dropdown,
             full_terminal_use_model_dropdown,
             computer_use_model_dropdown,
+            base_reasoning_dropdown,
+            full_terminal_reasoning_dropdown,
+            computer_use_reasoning_dropdown,
+            reasoning_roles: Vec::new(),
             permission_mode_dropdown,
             command_denylist_editor,
             command_allowlist_editor,
@@ -482,6 +507,7 @@ impl ExecutionProfileEditorView {
                     );
                 }
             }
+            me.refresh_reasoning_dropdowns(ctx);
         });
 
         // Refresh model dropdowns when BYO API keys update so key icons reflect current state.
@@ -506,6 +532,7 @@ impl ExecutionProfileEditorView {
                     ctx,
                 );
                 me.sync_context_window_editor(ctx, false);
+                me.refresh_reasoning_dropdowns(ctx);
                 ctx.notify();
             },
         );
@@ -647,6 +674,62 @@ impl ExecutionProfileEditorView {
 
         Self::update_profile_name_editor(&self.profile_name_editor, &current_permissions, ctx);
         self.sync_context_window_editor(ctx, false);
+        self.refresh_reasoning_dropdowns(ctx);
+    }
+
+    fn refresh_reasoning_dropdowns(&mut self, ctx: &mut ViewContext<Self>) {
+        let profile = BlocklistAIPermissions::as_ref(ctx)
+            .permissions_profile_for_id(ctx, self.profile_id);
+        self.reasoning_roles.clear();
+        for role in [ProfileModelRole::Base, ProfileModelRole::FullTerminalUse, ProfileModelRole::ComputerUse] {
+            let prefs = LLMPreferences::as_ref(ctx);
+            let (id, choices, dropdown) = match role {
+                ProfileModelRole::Base => (
+                    profile.base_model.clone().unwrap_or_else(|| prefs.get_default_base_model().id.clone()),
+                    prefs.get_base_llm_choices_for_agent_mode(ctx).collect_vec(),
+                    &self.base_reasoning_dropdown,
+                ),
+                ProfileModelRole::FullTerminalUse => (
+                    profile.cli_agent_model.clone().unwrap_or_else(|| prefs.get_default_cli_agent_model().id.clone()),
+                    prefs.get_cli_agent_llm_choices(ctx).collect_vec(),
+                    &self.full_terminal_reasoning_dropdown,
+                ),
+                ProfileModelRole::ComputerUse => (
+                    profile.computer_use_model.clone().unwrap_or_else(|| prefs.get_default_computer_use_model().id.clone()),
+                    prefs.get_computer_use_llm_choices(ctx).collect_vec(),
+                    &self.computer_use_reasoning_dropdown,
+                ),
+            };
+            let id = prefs.get_llm_info(&id).map_or(id.clone(), |model| model.id.clone());
+            let reasoning_choices = reasoning_choices_for_model(&id, &choices);
+            let items: Vec<MenuItem<DropdownAction>> = reasoning_choices.iter().map(|model| {
+                let mut fields = MenuItemFields::new(
+                    model.reasoning_level.clone().unwrap_or_else(|| "Provider default".to_owned()),
+                )
+                .with_on_select_action(DropdownAction::select_action_and_close(
+                    ExecutionProfileEditorViewAction::SetModelReasoning { role, id: model.id.clone() },
+                ))
+                .with_disabled(model.disable_reason.is_some());
+                if let Some(reason) = &model.disable_reason {
+                    fields = fields.with_tooltip(reason.tooltip_text())
+                        .with_tooltip_position(MenuTooltipPosition::Above);
+                }
+                fields.into_item()
+            }).collect();
+            if !reasoning_choices.is_empty() {
+                self.reasoning_roles.push(role);
+            }
+            dropdown.update(ctx, |dropdown, ctx| {
+                dropdown.set_rich_items(items, ctx);
+                dropdown.set_selected_by_action(ExecutionProfileEditorViewAction::SetModelReasoning { role, id }, ctx);
+                if AISettings::as_ref(ctx).is_any_ai_enabled(ctx) {
+                    dropdown.set_enabled(ctx);
+                } else {
+                    dropdown.set_disabled(ctx);
+                }
+            });
+        }
+        ctx.notify();
     }
 
     fn refresh_filterable_model_dropdown<G, A, D>(
@@ -673,7 +756,10 @@ impl ExecutionProfileEditorView {
 
             let llm_prefs = LLMPreferences::handle(ctx);
             let llm_prefs = llm_prefs.as_ref(ctx);
-            let choices = get_choices(llm_prefs, ctx);
+            let model_to_select = profile_model.unwrap_or_else(|| get_default_id(llm_prefs));
+            let model_to_select = llm_prefs.get_llm_info(&model_to_select)
+                .map_or(model_to_select.clone(), |model| model.id.clone());
+            let choices = collapse_model_reasoning_choices(get_choices(llm_prefs, ctx), Some(&model_to_select));
 
             let has_upgrade_gated_models = choices
                 .iter()
@@ -685,7 +771,7 @@ impl ExecutionProfileEditorView {
                 None,
                 None,
                 false,
-                false,
+                true,
                 ctx,
             );
             dropdown.set_rich_items(items, ctx);
@@ -700,9 +786,6 @@ impl ExecutionProfileEditorView {
                 dropdown.clear_footer(ctx);
             }
 
-            let llm_prefs = LLMPreferences::handle(ctx);
-            let llm_prefs = llm_prefs.as_ref(ctx);
-            let model_to_select = profile_model.unwrap_or_else(|| get_default_id(llm_prefs));
             dropdown.set_selected_by_action(create_action(model_to_select), ctx);
             ctx.notify();
         });
@@ -1036,6 +1119,16 @@ impl TypedActionView for ExecutionProfileEditorView {
                 self.sync_context_window_editor(ctx, true);
                 ctx.notify();
             }
+            ExecutionProfileEditorViewAction::SetModelReasoning { role, id } => {
+                AIExecutionProfilesModel::handle(ctx).update(ctx, |profiles_model, ctx| {
+                    match role {
+                        ProfileModelRole::Base => profiles_model.set_base_model(self.profile_id, Some(id.clone()), ctx),
+                        ProfileModelRole::FullTerminalUse => profiles_model.set_cli_agent_model(self.profile_id, Some(id.clone()), ctx),
+                        ProfileModelRole::ComputerUse => profiles_model.set_computer_use_model(self.profile_id, Some(id.clone()), ctx),
+                    }
+                });
+                ctx.notify();
+            }
             ExecutionProfileEditorViewAction::ContextWindowSliderDragged { value } => {
                 if !AISettings::as_ref(ctx).is_any_ai_enabled(ctx) {
                     self.sync_context_window_editor(ctx, true);

app/src/ai/execution_profiles/editor/mod_tests.rs

@@ -5,6 +5,9 @@ use warpui::App;
 
 use super::ui_helpers::context_window_snap_values;
 use crate::ai::execution_profiles::profiles::AIExecutionProfilesModel;
+use crate::ai::execution_profiles::model_menu_items::{
+    collapse_model_reasoning_choices, has_reasoning_variants, reasoning_choices_for_model,
+};
 use crate::ai::execution_profiles::{
     has_configurable_context_window, should_show_long_context_pricing_warning, AIExecutionProfile,
     AIExecutionProfileAppExt as _,
@@ -316,3 +319,53 @@ fn non_openai_configurable_context_ignores_gpt_flag_and_does_not_show_openai_war
         false
     ));
 }
+
+fn reasoning_model(id: &str, provider: &str, effort: Option<&str>) -> LLMInfo {
+    let mut model = configurable_model(LLMProvider::LiteLLM);
+    model.id = id.into();
+    model.base_model_name = "Same model".to_owned();
+    model.description = Some(provider.to_owned());
+    model.reasoning_level = effort.map(str::to_owned);
+    model
+}
+
+#[test]
+fn reasoning_dropdown_supports_one_advertised_level_and_provider_default() {
+    let base = reasoning_model("model", "Connection A", None);
+    let high = reasoning_model("model::reasoning::high", "Connection A", Some("high"));
+    let choices = vec![&base, &high];
+    assert!(has_reasoning_variants(&base, &choices));
+    let levels = reasoning_choices_for_model(&base.id, &choices);
+    assert_eq!(levels.len(), 2);
+    assert_eq!(levels[0].reasoning_level, None);
+    assert_eq!(levels[1].reasoning_level.as_deref(), Some("high"));
+}
+
+#[test]
+fn reasoning_choices_without_discovery_remain_unchanged() {
+    let base = reasoning_model("model", "Connection A", None);
+    let choices = vec![&base];
+    assert!(!has_reasoning_variants(&base, &choices));
+    assert!(reasoning_choices_for_model(&base.id, &choices).is_empty());
+    assert_eq!(collapse_model_reasoning_choices(choices, Some(&base.id))[0].id, base.id);
+}
+
+#[test]
+fn collapsed_models_preserve_selected_reasoning_and_provider_identity() {
+    let base = reasoning_model("model", "Connection A", None);
+    let high = reasoning_model("model::reasoning::high", "Connection A", Some("high"));
+    let other = reasoning_model("model::connection::other", "Connection B", None);
+    let choices = collapse_model_reasoning_choices(vec![&base, &high, &other], Some(&high.id));
+    assert_eq!(choices.len(), 2);
+    assert_eq!(choices[0].id, high.id);
+    assert_eq!(choices[1].id, other.id);
+    assert!(reasoning_choices_for_model(&other.id, &[&base, &high, &other]).is_empty());
+}
+
+#[test]
+fn reasoning_dropdown_does_not_invent_provider_default() {
+    let high = reasoning_model("model::reasoning::high", "Connection A", Some("high"));
+    let levels = reasoning_choices_for_model(&high.id, &[&high]);
+    assert_eq!(levels.len(), 1);
+    assert_eq!(levels[0].id, high.id);
+}

app/src/ai/execution_profiles/editor/ui_helpers.rs

@@ -12,7 +12,7 @@ use warpui::ui_components::components::{Coords, UiComponent, UiComponentStyles};
 use warpui::ui_components::text::Span;
 use warpui::{AppContext, Element, SingletonEntity, ViewHandle};
 
-use super::{ExecutionProfileEditorView, ExecutionProfileEditorViewAction};
+use super::{ExecutionProfileEditorView, ExecutionProfileEditorViewAction, ProfileModelRole};
 use crate::ai::blocklist::BlocklistAIPermissions;
 use crate::ai::execution_profiles::{
     long_context_pricing_warning_title, AIExecutionProfile, AIExecutionProfileAppExt as _,
@@ -247,6 +247,29 @@ fn render_permission_row<T: DropdownItemAction>(
         .finish()
 }
 
+fn render_reasoning_dropdown_row(
+    appearance: &Appearance,
+    dropdown: &ViewHandle<Dropdown<ExecutionProfileEditorViewAction>>,
+) -> Box<dyn Element> {
+    Container::new(
+        Flex::row()
+            .with_cross_axis_alignment(CrossAxisAlignment::Center)
+            .with_child(
+                Container::new(
+                    Text::new("Reasoning level", appearance.ui_font_family(), 12.)
+                        .with_color(appearance.theme().active_ui_text_color().into())
+                        .finish(),
+                )
+                .with_margin_right(12.)
+                .finish(),
+            )
+            .with_child(ChildView::new(dropdown).finish())
+            .finish(),
+    )
+    .with_margin_bottom(16.)
+    .finish()
+}
+
 pub fn render_models_section(
     appearance: &Appearance,
     view: &ExecutionProfileEditorView,
@@ -262,6 +285,10 @@ pub fn render_models_section(
             &view.base_model_dropdown,
         ));
 
+    if view.reasoning_roles.contains(&ProfileModelRole::Base) {
+        column.add_child(render_reasoning_dropdown_row(appearance, &view.base_reasoning_dropdown));
+    }
+
     if let Some(row) = render_context_window_row(appearance, view, app) {
         column.add_child(row);
     }
@@ -273,6 +300,10 @@ pub fn render_models_section(
         &view.full_terminal_use_model_dropdown,
     ));
 
+    if view.reasoning_roles.contains(&ProfileModelRole::FullTerminalUse) {
+        column.add_child(render_reasoning_dropdown_row(appearance, &view.full_terminal_reasoning_dropdown));
+    }
+
     if FeatureFlag::LocalComputerUse.is_enabled() {
         column.add_child(render_filterable_dropdown_row(
             appearance,
@@ -280,6 +311,9 @@ pub fn render_models_section(
             "The model used when the agent takes control of your computer to interact with graphical applications through mouse movements, clicks, and keyboard input.",
             &view.computer_use_model_dropdown,
         ));
+        if view.reasoning_roles.contains(&ProfileModelRole::ComputerUse) {
+            column.add_child(render_reasoning_dropdown_row(appearance, &view.computer_use_reasoning_dropdown));
+        }
     }
 
     Container::new(column.finish())

app/src/ai/execution_profiles/model_menu_items.rs

@@ -1,3 +1,4 @@
+use std::collections::HashSet;
 use std::sync::Arc;
 
 use galaxy_core::ui::Icon;
@@ -21,15 +22,55 @@ pub fn is_auto(llm: &LLMInfo) -> bool {
         || llm.id.to_string().to_lowercase().contains("auto")
 }
 
-/// Returns true if the given model has other variants with different reasoning levels.
+/// Returns true when a model's configured connection advertises reasoning choices.
 pub fn has_reasoning_variants(llm: &LLMInfo, all_models: &[&LLMInfo]) -> bool {
-    if !llm.has_reasoning_level() {
+    all_models.iter().any(|other| {
-        return false;
+        other.reasoning_group_key() == llm.reasoning_group_key() && other.has_reasoning_level()
+    })
 }
-    all_models
+
-        .iter()
+/// Keep one visible model entry per connection while preserving the selected
-        .filter(|other| other.reasoning_group_key() == llm.reasoning_group_key() && other.id != llm.id)
+/// reasoning variant as the entry's action. Unknown reasoning metadata stays unchanged.
-        .any(|other| other.has_reasoning_level())
+pub fn collapse_model_reasoning_choices<'a>(
+    choices: Vec<&'a LLMInfo>,
+    selected_id: Option<&LLMId>,
+) -> Vec<&'a LLMInfo> {
+    let mut groups = HashSet::new();
+    let mut result = Vec::new();
+    for model in &choices {
+        if !has_reasoning_variants(model, &choices) {
+            result.push(*model);
+            continue;
+        }
+        let key = model.reasoning_group_key();
+        if !groups.insert(key.clone()) {
+            continue;
+        }
+        let matching = || choices.iter().copied().filter(|other| other.reasoning_group_key() == key);
+        let representative = matching()
+            .find(|other| Some(&other.id) == selected_id)
+            .or_else(|| matching().find(|other| !other.has_reasoning_level()))
+            .unwrap_or(model);
+        result.push(representative);
+    }
+    result
+}
+
+/// Includes the provider-default entry only when that entry is actually available.
+pub fn reasoning_choices_for_model<'a>(
+    selected_id: &LLMId,
+    choices: &[&'a LLMInfo],
+) -> Vec<&'a LLMInfo> {
+    let Some(selected) = choices.iter().find(|model| &model.id == selected_id) else {
+        return Vec::new();
+    };
+    if !has_reasoning_variants(selected, choices) {
+        return Vec::new();
+    }
+    choices.iter().copied()
+        .filter(|model| model.reasoning_group_key() == selected.reasoning_group_key())
+        .unique_by(|model| model.reasoning_level.clone())
+        .collect()
 }
 
 fn with_cost_and_profile_info<A: Action + Clone>(

app/src/ai/llms.rs

@@ -386,6 +386,8 @@ pub struct AvailableLLMs {
     /// The Warp "default" LLM.
     default_id: LLMId,
     choices: Vec<LLMInfo>,
+    #[serde(skip)]
+    legacy_reasoning_aliases: HashMap<LLMId, LLMId>,
 
     #[serde(default)]
     preferred_codex_model_id: Option<LLMId>,
@@ -423,12 +425,16 @@ impl AvailableLLMs {
         Ok(Self {
             default_id,
             choices: choices.into_iter().collect(),
+            legacy_reasoning_aliases: HashMap::new(),
             preferred_codex_model_id,
         })
     }
 
     fn info_for_id(&self, id: &LLMId) -> Option<&LLMInfo> {
-        self.choices.iter().find(|info| info.id == *id)
+        self.choices.iter().find(|info| info.id == *id).or_else(|| {
+            let resolved = self.legacy_reasoning_aliases.get(id)?;
+            self.choices.iter().find(|info| info.id == *resolved)
+        })
     }
 
     /// Returns the info for the given id only if the model is usable (present
@@ -516,6 +522,7 @@ impl ModelsByFeature {
 /// Used both in `ModelsByFeature::default()` and as a fallback in `get_computer_use_available()`.
 fn default_computer_use_llms() -> AvailableLLMs {
     AvailableLLMs {
+        legacy_reasoning_aliases: HashMap::new(),
         default_id: "computer-use-agent-auto".to_owned().into(),
         choices: vec![LLMInfo {
             display_name: "auto".to_owned(),
@@ -546,6 +553,7 @@ impl Default for ModelsByFeature {
     /// `inject_bedrock_models` once real models are loaded.
     fn default() -> Self {
         let placeholder = || AvailableLLMs {
+            legacy_reasoning_aliases: HashMap::new(),
             default_id: "placeholder".to_owned().into(),
             choices: vec![LLMInfo {
                 display_name: "No models configured".to_owned(),
@@ -1131,6 +1139,11 @@ impl LLMPreferences {
             cli.choices.retain(|m| m.provider != LLMProvider::LiteLLM);
         }
         self.openai_provider_routing.clear();
+        self.models_by_feature.agent_mode.legacy_reasoning_aliases.clear();
+        self.models_by_feature.coding.legacy_reasoning_aliases.clear();
+        if let Some(cli) = &mut self.models_by_feature.cli_agent {
+            cli.legacy_reasoning_aliases.clear();
+        }
 
         let settings = AISettings::as_ref(ctx);
         self.inject_acp_models(ctx);
@@ -1241,6 +1254,8 @@ impl LLMPreferences {
 
         let mut total_injected = 0;
         let mut seen_model_ids: HashSet<String> = HashSet::new();
+        let mut legacy_reserved_model_ids = HashSet::new();
+        let mut legacy_reasoning_aliases = HashMap::new();
         let mut seen_connections = HashSet::new();
         for (
             provider_name,
@@ -1263,32 +1278,34 @@ impl LLMPreferences {
                     continue;
                 }
 
-                let reasoning_efforts: Vec<Option<&String>> =
+                // All providers may advertise discrete effort choices. Keep the
-                    if provider_kind == OpenAIProviderKind::ChatGPTSubscription {
+                // default model key stable and route explicit choices separately.
-                        // Keep the base model as the provider-default mode, then expose each
+                let reasoning_efforts = std::iter::once(None)
-                        // explicitly supported effort as a separate selectable variant.
+                    .chain(model.reasoning_efforts.iter().map(Some));
-                        std::iter::once(None)
-                            .chain(model.reasoning_efforts.iter().map(Some))
-                            .collect()
-                    } else {
-                        vec![None]
-                    };
 
+                // Choose a namespace once for the whole family. Otherwise a
+                // colliding base with unique efforts would split one connection
+                // into multiple groups in the model/reasoning selectors.
+                let connection_suffix = if seen_model_ids.contains(&model.model_id) {
+                    use sha2::{Digest, Sha256};
+                    Some(format!("{:x}", Sha256::digest(connection_identity.as_bytes())))
+                } else {
+                    None
+                };
                 for reasoning_effort in reasoning_efforts {
                     let reasoning_effort = reasoning_effort.cloned();
                     let mut model_key = reasoning_effort.as_deref().map_or_else(
                         || model.model_id.clone(),
                         |effort| openai_model_variant_id(&model.model_id, effort),
                     );
-                    if !seen_model_ids.insert(model_key.clone()) {
+                    let legacy_key = model_key.clone();
-                        use sha2::{Digest, Sha256};
+                    let legacy_unclaimed = legacy_reserved_model_ids.insert(legacy_key.clone());
-                        let connection_hash =
+                    if let Some(suffix) = &connection_suffix {
-                            format!("{:x}", Sha256::digest(connection_identity.as_bytes()));
+                        model_key = format!("{model_key}::connection::{suffix}");
-                        model_key = format!("{model_key}::connection::{connection_hash}");
+                    }
                     if !seen_model_ids.insert(model_key.clone()) {
                         continue;
                     }
-                    }
                     // Disabled entries still reserve their selection keys so
                     // toggling a connection cannot silently reroute another
                     // connection's saved model selection.
@@ -1296,6 +1313,9 @@ impl LLMPreferences {
                         continue;
                     }
 
+                    if legacy_unclaimed && reasoning_effort.is_some() && legacy_key != model_key {
+                        legacy_reasoning_aliases.insert(LLMId::from(legacy_key), LLMId::from(model_key.clone()));
+                    }
                     // Register the routing entry. Reasoning variants keep the provider's
                     // actual model ID while using their synthetic key only for selection.
                     let client_config = OpenAIClientConfig {
@@ -1354,6 +1374,18 @@ impl LLMPreferences {
             }
         }
 
+        // An exact key, including one reserved by a disabled model, always wins.
+        legacy_reasoning_aliases.retain(|legacy_id, _| !seen_model_ids.contains(legacy_id.as_str()));
+        for (legacy_id, canonical_id) in &legacy_reasoning_aliases {
+            if let Some(config) = self.openai_provider_routing.get(canonical_id.as_str()).cloned() {
+                self.openai_provider_routing.entry(legacy_id.to_string()).or_insert(config);
+            }
+        }
+        self.models_by_feature.agent_mode.legacy_reasoning_aliases = legacy_reasoning_aliases.clone();
+        self.models_by_feature.coding.legacy_reasoning_aliases = legacy_reasoning_aliases.clone();
+        if let Some(cli) = &mut self.models_by_feature.cli_agent {
+            cli.legacy_reasoning_aliases = legacy_reasoning_aliases;
+        }
         log::info!("[openai/litellm] Injected {total_injected} model(s) into available choices");
     }
 
@@ -1998,7 +2030,7 @@ impl LLMPreferences {
                 provider: None,
                 supports_system_messages: Some(true),
                 capability_overrides: std::collections::HashMap::new(),
-                reasoning_efforts: Vec::new(),
+                reasoning_efforts: model.reasoning_efforts,
                 enabled: true,
             })
             .collect()
@@ -2974,6 +3006,36 @@ fn u32_from_json_any(value: &serde_json::Value, keys: &[&str]) -> Option<u32> {
         .and_then(|value| u32::try_from(value).ok())
 }
 
+/// Read enumerated effort support, never infer choices from model names or a
+/// generic supports_reasoning flag. Explicit lists take precedence over flags.
+#[cfg(not(target_family = "wasm"))]
+fn advertised_reasoning_efforts(value: &serde_json::Value) -> Vec<String> {
+    for key in [
+        "reasoning_effort_levels",
+        "supported_reasoning_efforts",
+        "reasoning_efforts",
+        "supported_reasoning_levels",
+    ] {
+        if let Some(levels) = value[key].as_array() {
+            let mut result = Vec::new();
+            for level in levels {
+                if let Some(effort) = level.as_str().or_else(|| level["effort"].as_str()) {
+                    let effort = effort.trim();
+                    if !effort.is_empty() && !result.iter().any(|entry| entry == effort) {
+                        result.push(effort.to_string());
+                    }
+                }
+            }
+            return result;
+        }
+    }
+    ["none", "minimal", "low", "medium", "high", "xhigh", "max", "ultra"]
+        .into_iter()
+        .filter(|level| value[format!("supports_{level}_reasoning_effort")].as_bool() == Some(true))
+        .map(str::to_string)
+        .collect()
+}
+
 #[cfg(not(target_family = "wasm"))]
 fn normalize_codex_release_version(version: &str) -> Option<String> {
     let version = version.trim();
@@ -3125,17 +3187,7 @@ fn chatgpt_models_from_codex_response(body: &serde_json::Value) -> Vec<OpenAIMod
                         .any(|modality| modality.as_str() == Some("image"))
                 })
                 .unwrap_or(true);
-            let reasoning_efforts = model["supported_reasoning_levels"]
+            let reasoning_efforts = advertised_reasoning_efforts(model);
-                .as_array()
-                .map(|levels| {
-                    levels
-                        .iter()
-                        .filter_map(|level| level["effort"].as_str())
-                        .filter(|effort| !effort.trim().is_empty())
-                        .map(str::to_string)
-                        .collect::<Vec<_>>()
-                })
-                .unwrap_or_default();
 
             Some(OpenAIModelConfig {
                 model_id: model_id.to_string(),
@@ -3283,7 +3335,7 @@ async fn fetch_from_litellm_model_info(
                     model_info["supports_system_messages"].as_bool()
                 },
                 capability_overrides: std::collections::HashMap::new(),
-                reasoning_efforts: Vec::new(),
+                reasoning_efforts: advertised_reasoning_efforts(model_info),
                 enabled: true,
             })
         })
@@ -3322,6 +3374,12 @@ async fn fetch_litellm_catalog(
                 .iter()
                 .find(|info| info.model_id == model.model_id)
                 .cloned()
+                .map(|mut info| {
+                    if info.reasoning_efforts.is_empty() {
+                        info.reasoning_efforts = model.reasoning_efforts.clone();
+                    }
+                    info
+                })
                 .unwrap_or(model)
         })
         .collect())
@@ -3442,7 +3500,7 @@ async fn fetch_from_openai_models(
                     m["supports_system_messages"].as_bool()
                 },
                 capability_overrides: std::collections::HashMap::new(),
-                reasoning_efforts: Vec::new(),
+                reasoning_efforts: advertised_reasoning_efforts(m),
                 enabled: true,
             })
         })

app/src/ai/llms_tests.rs

@@ -256,6 +256,7 @@ fn computer_use_picker_includes_vision_models_without_duplicate_entries() {
         text_only.display_name = "Text-only model".to_owned();
         preferences.models_by_feature.agent_mode.choices = vec![visual.clone(), text_only];
         preferences.models_by_feature.computer_use = Some(AvailableLLMs {
+            legacy_reasoning_aliases: HashMap::new(),
             default_id: "computer-use-agent-auto".into(),
             choices: vec![auto, visual],
             preferred_codex_model_id: None,
@@ -1276,3 +1277,105 @@ fn model_selector_labels_and_reasoning_groups_include_configured_provider() {
     other.id = LLMId::from("model-b::connection::second-connection");
     assert_ne!(first.reasoning_group_key(), other.reasoning_group_key());
 }
+
+#[test]
+fn reasoning_discovery_uses_explicit_lists_and_supported_flags_only() {
+    assert_eq!(
+        advertised_reasoning_efforts(&serde_json::json!({
+            "reasoning_effort_levels": [" low ", "high", "low", "", null],
+            "supports_max_reasoning_effort": true
+        })),
+        ["low", "high"]
+    );
+    assert_eq!(
+        advertised_reasoning_efforts(&serde_json::json!({
+            "supported_reasoning_levels": [{"effort": "minimal"}, {"effort": "high"}]
+        })),
+        ["minimal", "high"]
+    );
+    assert_eq!(
+        advertised_reasoning_efforts(&serde_json::json!({
+            "supports_reasoning": true,
+            "supports_low_reasoning_effort": true,
+            "supports_high_reasoning_effort": false,
+            "supports_xhigh_reasoning_effort": true
+        })),
+        ["low", "xhigh"]
+    );
+    assert!(advertised_reasoning_efforts(&serde_json::json!({
+        "id": "gpt-test-high", "supports_reasoning": true
+    })).is_empty());
+    assert!(advertised_reasoning_efforts(&serde_json::json!({
+        "reasoning_effort_levels": [], "supports_low_reasoning_effort": true
+    })).is_empty());
+}
+
+#[test]
+fn overlapping_connections_keep_all_reasoning_variants_in_one_family() {
+    App::test((), |mut app| async move {
+        initialize_settings_for_tests(&mut app);
+        AISettings::handle(&app).update(&mut app, |settings, ctx| {
+            settings.openai_enabled.set_value(true, ctx).unwrap();
+            let mut reasoning_model = openai_model("shared-model");
+            reasoning_model.reasoning_efforts = vec!["low".to_owned(), "high".to_owned()];
+            settings.openai_providers.set_value(vec![
+                OpenAIProviderConfig {
+                    kind: OpenAIProviderKind::LiteLLM,
+                    name: "First connection".to_owned(),
+                    base_url: "https://first.test/v1".to_owned(),
+                    models: vec![openai_model("shared-model")],
+                    enabled: true,
+                    api_key: None,
+                    project_id: None,
+                    location: None,
+                },
+                OpenAIProviderConfig {
+                    kind: OpenAIProviderKind::LiteLLM,
+                    name: "Second connection".to_owned(),
+                    base_url: "https://second.test/v1".to_owned(),
+                    models: vec![reasoning_model],
+                    enabled: true,
+                    api_key: None,
+                    project_id: None,
+                    location: None,
+                },
+            ], ctx).unwrap();
+        });
+        let mut preferences = empty_preferences();
+        app.read(|ctx| preferences.inject_openai_models(ctx));
+        let choices: Vec<_> = preferences.models_by_feature.agent_mode.choices.iter()
+            .filter(|model| model.description.as_deref() == Some("Second connection"))
+            .collect();
+        assert_eq!(choices.len(), 3);
+        let base = choices.iter().find(|model| model.reasoning_level.is_none()).unwrap();
+        assert!(base.id.as_str().contains("::connection::"));
+        let group_key = base.reasoning_group_key();
+        for model in choices {
+            assert_eq!(model.reasoning_group_key(), group_key);
+            let route = preferences.openai_client_config_for_model(model.id.as_str()).unwrap();
+            assert_eq!(route.model.as_deref(), Some("shared-model"));
+            assert_eq!(route.reasoning_effort, model.reasoning_level);
+        }
+        let old_id = LLMId::from("shared-model::reasoning::low");
+        let canonical = preferences.get_llm_info(&old_id).unwrap();
+        assert_eq!(canonical.description.as_deref(), Some("Second connection"));
+        assert!(canonical.id.as_str().contains("::connection::"));
+        assert_eq!(preferences.openai_client_config_for_model(old_id.as_str()).unwrap().reasoning_effort.as_deref(), Some("low"));
+
+        AISettings::handle(&app).update(&mut app, |settings, ctx| {
+            let mut providers = settings.openai_providers.value().clone();
+            providers[1].enabled = false;
+            let mut third = providers[1].clone();
+            third.enabled = true;
+            third.name = "Third connection".to_owned();
+            third.base_url = "https://third.test/v1".to_owned();
+            providers.push(third);
+            settings.openai_providers.set_value(providers, ctx).unwrap();
+        });
+        app.read(|ctx| preferences.inject_openai_models(ctx));
+        // A disabled second connection still reserves its legacy IDs: they
+        // must not silently migrate to the third connection.
+        assert!(preferences.get_llm_info(&old_id).is_none());
+        assert!(preferences.openai_client_config_for_model(old_id.as_str()).is_none());
+    });
+}

app/src/ai/openai/client.rs

@@ -21,6 +21,7 @@ pub struct OpenAIClientConfig {
 }
 
 pub struct OpenAIClient {
+    reasoning_effort: Option<String>,
     http: reqwest::Client,
     base_url: String,
     api_key: Option<String>,
@@ -54,6 +55,7 @@ impl OpenAIClient {
     pub fn from_config(config: OpenAIClientConfig) -> Self {
         let http = reqwest::Client::new();
         Self {
+            reasoning_effort: config.reasoning_effort,
             http,
             base_url: config.base_url,
             api_key: config.api_key,
@@ -62,9 +64,12 @@ impl OpenAIClient {
 
     pub async fn chat_completions_stream(
         &self,
-        request_body: serde_json::Value,
+        mut request_body: serde_json::Value,
     ) -> Result<impl Stream<Item = Result<Bytes, reqwest::Error>>, OpenAIError> {
         let url = format!("{}/chat/completions", self.base_url.trim_end_matches('/'));
+        if let Some(effort) = &self.reasoning_effort {
+            request_body["reasoning_effort"] = effort.clone().into();
+        }
 
         let mut headers = HeaderMap::new();
         headers.insert(CONTENT_TYPE, HeaderValue::from_static("application/json"));

app/src/ai/runtime/rig.rs

@@ -307,7 +307,8 @@ pub(crate) async fn provider_runtime_for_request(
                     model,
                     max_output_tokens: config.max_output_tokens.map(u64::from),
                     supports_system_messages: config.supports_system_messages,
-                }),
+                })
+                .with_reasoning_effort(config.reasoning_effort),
             ),
             OpenAIProviderKind::ChatGPTSubscription => Arc::new(ChatGPTSubscriptionRuntime::new(
                 ChatGPTSubscriptionRuntimeConfig {
@@ -324,7 +325,8 @@ pub(crate) async fn provider_runtime_for_request(
                     api_key: credential.clone(),
                     model,
                     max_output_tokens: config.max_output_tokens.map(u64::from),
-                });
+                })
+                .with_reasoning_effort(config.reasoning_effort);
                 if stored {
                     runtime = runtime.with_credential_resolver(Arc::new(move || {
                         Box::pin(crate::ai::claude_oauth::load_credential(credential.clone()))
@@ -332,17 +334,23 @@ pub(crate) async fn provider_runtime_for_request(
                 }
                 Arc::new(runtime)
             }
-            OpenAIProviderKind::Gemini => Arc::new(GeminiRuntime::new(GeminiRuntimeConfig {
+            OpenAIProviderKind::Gemini => Arc::new(
+                GeminiRuntime::new(GeminiRuntimeConfig {
                     api_key: config.api_key.unwrap_or_default(),
                     model,
                     max_output_tokens: config.max_output_tokens.map(u64::from),
-            })),
+                })
-            OpenAIProviderKind::VertexAI => Arc::new(VertexAiRuntime::new(VertexAiRuntimeConfig {
+                .with_reasoning_effort(config.reasoning_effort),
+            ),
+            OpenAIProviderKind::VertexAI => Arc::new(
+                VertexAiRuntime::new(VertexAiRuntimeConfig {
                     project_id: config.project_id.unwrap_or_default(),
                     location: config.location.unwrap_or_else(|| "global".to_string()),
                     model,
                     max_output_tokens: config.max_output_tokens.map(u64::from),
-            })),
+                })
+                .with_reasoning_effort(config.reasoning_effort),
+            ),
         },
         crate::ai::provider::ProviderConfig::Bedrock(config) => {
             let max_output_tokens = Some(bedrock_max_output_tokens(&model));

app/src/settings/ai.rs

@@ -904,7 +904,7 @@ pub struct OpenAIModelConfig {
     pub capability_overrides: HashMap<String, ModelCapabilityOverride>,
     #[serde(default)]
     #[schemars(
-        description = "Reasoning effort modes supported by this model when using the ChatGPT subscription provider."
+        description = "Discrete reasoning effort modes explicitly supported by this model and provider."
     )]
     pub reasoning_efforts: Vec<String>,
     #[serde(default = "default_enabled")]

app/src/terminal/input/models/data_source.rs

@@ -24,7 +24,7 @@ use super::model_spec_scores::{
     MODEL_SPECS_DESCRIPTION, MODEL_SPECS_TITLE, REASONING_LEVEL_DESCRIPTION, REASONING_LEVEL_TITLE,
 };
 use crate::ai::custom_model_routers::is_custom_router_id;
-use crate::ai::execution_profiles::model_menu_items::is_auto;
+use crate::ai::execution_profiles::model_menu_items::{collapse_model_reasoning_choices, is_auto};
 use crate::ai::llms::{
     is_using_api_key_for_provider, should_show_bedrock_icon_for_model, DisableReason, LLMId,
     LLMInfo, LLMPreferences, LLMProvider, LLMSpec,
@@ -47,6 +47,14 @@ use crate::workspaces::user_workspaces::UserWorkspaces;
 
 const AUTO_BEDROCK_TOOLTIP: &str = "Warp uses Bedrock when the model Auto selects supports it; otherwise it may use Warp-hosted inference.";
 
+fn model_picker_label(llm: &LLMInfo) -> String {
+    if llm.has_reasoning_level() && !is_custom_router_id(llm.id.as_str()) {
+        llm.label_with_provider(llm.base_model_name())
+    } else {
+        llm.menu_display_name()
+    }
+}
+
 #[derive(Clone, Debug)]
 pub struct AcceptModel {
     pub id: LLMId,
@@ -228,6 +236,7 @@ impl SyncDataSource for ModelSelectorDataSource {
                 })
                 .collect_vec()
         };
+        let choices = collapse_model_reasoning_choices(choices, Some(&active_llm_id));
         let choices = Self::order_model_choices(llm_preferences, choices);
 
         let query_text = query.text.trim().to_lowercase();
@@ -243,7 +252,7 @@ impl SyncDataSource for ModelSelectorDataSource {
             .into_iter()
             .filter_map(|llm| {
                 let match_result = match_indices_case_insensitive(
-                    llm.menu_display_name().to_lowercase().as_str(),
+                    model_picker_label(llm).to_lowercase().as_str(),
                     query_text.as_str(),
                 )?;
 
@@ -327,7 +336,7 @@ impl ModelSearchItem {
             spec: llm.spec.clone(),
             leading_icon,
             credential_icon,
-            display_text: llm.menu_display_name(),
+            display_text: model_picker_label(llm),
             is_selected: &llm.id == active_llm_id,
             is_custom_endpoint,
             is_custom_router,

app/src/terminal/profile_model_selector.rs

@@ -1012,12 +1012,13 @@ impl ProfileModelSelector {
 
         // Group reasoning variants by model and configured provider.
         // Use "auto" as the key for all auto models so they collapse together.
-        // Only group models that have reasoning levels - others stay separate.
+        // Include the provider-default entry in its reasoning group, while
+        // leaving models without advertised reasoning choices unchanged.
         let mut groups: IndexMap<String, Vec<&LLMInfo>> = IndexMap::new();
         for llm in &server_choices {
             let key = if is_auto(llm) {
                 "auto".to_string()
-            } else if llm.has_reasoning_level() {
+            } else if has_reasoning_variants(llm, &server_choices) {
                 llm.reasoning_group_key()
             } else {
                 llm.id.to_string()
@@ -1155,7 +1156,7 @@ impl ProfileModelSelector {
                         llm.display_name.clone()
                     };
 
-                    Self::make_sidecar_item(label, &llm.id, is_selected)
+                    Self::make_sidecar_item(label, llm, is_selected)
                 })
                 .collect(),
             ModelSpecSidecarKind::Reasoning => {
@@ -1186,11 +1187,13 @@ impl ProfileModelSelector {
         let items: Vec<MenuItem<ProfileModelSelectorAction>> = self
             .all_model_choices
             .iter()
-            .filter(|llm| llm.reasoning_group_key() == base_name && llm.has_reasoning_level())
+            .filter(|llm| llm.reasoning_group_key() == base_name)
             .map(|llm| {
                 let is_selected = llm.id == active_llm_id;
-                let label = llm.reasoning_level().unwrap_or_default();
+                let label = llm
-                Self::make_sidecar_item(label, &llm.id, is_selected)
+                    .reasoning_level()
+                    .unwrap_or_else(|| "Provider default".to_owned());
+                Self::make_sidecar_item(label, llm, is_selected)
             })
             .collect();
 
@@ -1208,12 +1211,18 @@ impl ProfileModelSelector {
 
     fn make_sidecar_item(
         label: String,
-        llm_id: &LLMId,
+        llm: &LLMInfo,
         is_selected: bool,
     ) -> MenuItem<ProfileModelSelectorAction> {
         let mut fields = MenuItemFields::new(label)
             .with_font_size_override(14.)
-            .with_on_select_action(ProfileModelSelectorAction::SelectModel(llm_id.clone()));
+            .with_on_select_action(ProfileModelSelectorAction::SelectModel(llm.id.clone()));
+
+        if let Some(reason) = &llm.disable_reason {
+            fields = fields
+                .with_disabled(true)
+                .with_tooltip(reason.tooltip_text());
+        }
 
         if is_selected {
             fields = fields.with_icon(Icon::Check);
@@ -1275,12 +1284,11 @@ impl ProfileModelSelector {
                             item.item_on_select_action(),
                             Some(ProfileModelSelectorAction::SelectAutoModel)
                         )
-                    } else if active_llm.has_reasoning_level() {
+                    } else if let Some(ProfileModelSelectorAction::SelectReasoningModel(name)) =
+                        item.item_on_select_action()
+                    {
                         // Match the selected model and its configured provider.
-                        matches!(
+                        *name == active_llm.reasoning_group_key()
-                            item.item_on_select_action(),
-                            Some(ProfileModelSelectorAction::SelectReasoningModel(name)) if *name == active_llm.reasoning_group_key()
-                        )
                     } else {
                         let item_model_id = item
                             .item_on_select_action()
@@ -1334,12 +1342,17 @@ impl ProfileModelSelector {
                                 .cloned()
                         }
                         ProfileModelSelectorAction::SelectReasoningModel(base_name) => {
-                            // Get the first reasoning variant for this base model
+                            // Preserve the current effort when hovering its model.
+                            // For another model, prefer the provider-default entry.
+                            let preferences = LLMPreferences::as_ref(ctx);
+                            let active = preferences.get_active_base_model(ctx, Some(self.terminal_view_id));
+                            if active.reasoning_group_key() == *base_name {
+                                return Some(active.clone());
+                            }
                             self.all_model_choices
                                 .iter()
-                                .find(|llm| {
+                                .filter(|llm| llm.reasoning_group_key() == *base_name)
-                                    llm.reasoning_group_key() == *base_name && llm.has_reasoning_level()
+                                .min_by_key(|llm| llm.has_reasoning_level())
-                                })
                                 .cloned()
                         }
                         _ => None,

app/src/terminal/view.rs

@@ -2742,6 +2742,7 @@ pub struct TerminalView {
     pending_user_query_kind: Option<PendingUserQueryKind>,
     queued_prompt_callback: Option<ConversationFinishedCallback>,
     last_observed_conversation_status: HashMap<AIConversationId, ConversationStatus>,
+    last_desktop_notification_status: HashMap<AIConversationId, ConversationStatus>,
     last_observed_active_subagent: HashMap<AIConversationId, bool>,
     usage_footer_view_ids: HashMap<EntityId, EntityId>,
 
@@ -4415,6 +4416,7 @@ impl TerminalView {
             pending_user_query_kind: None,
             queued_prompt_callback: None,
             last_observed_conversation_status: Default::default(),
+            last_desktop_notification_status: Default::default(),
             last_observed_active_subagent: Default::default(),
             usage_footer_view_ids: Default::default(),
             block_onboarding_active: false,
@@ -6466,6 +6468,9 @@ impl TerminalView {
                         ConversationStatusUpdate::Restored => None,
                     });
 
+                if !new_status.should_trigger_notification() {
+                    self.last_desktop_notification_status.remove(conversation_id);
+                }
                 // Don't send notifications or insert ambient agent session ended tombstone
                 // if we're restoring this conversation on startup.
                 if matches!(update, ConversationStatusUpdate::Restored) {
@@ -6579,6 +6584,7 @@ impl TerminalView {
                 // The singleton's own history subscriber drops queue state for cleared
                 // conversations, so no `clear_all` call is needed here.
                 self.last_observed_conversation_status.clear();
+                self.last_desktop_notification_status.clear();
                 self.last_observed_active_subagent.clear();
                 if let Some(active_conversation_id) = active_conversation_id {
                     self.ai_controller.update(ctx, |controller, ctx| {
@@ -6645,6 +6651,7 @@ impl TerminalView {
                 // needed here.
                 self.last_observed_conversation_status
                     .remove(conversation_id);
+                self.last_desktop_notification_status.remove(conversation_id);
                 self.last_observed_active_subagent.remove(conversation_id);
             }
             BlocklistAIHistoryEvent::CreatedSubtask {
@@ -13719,6 +13726,11 @@ impl TerminalView {
         // Desktop notifications — only when navigated away and not in-progress.
         if !self.is_navigated_away_from_window(ctx)
             || matches!(status, CLIAgentSessionStatus::InProgress)
+            // Linked workers use conversation notifications, which suppress
+            // routine completions and retain actionable permission requests.
+            || self
+                .child_conversation_id_for_cli_status_updates(ctx)
+                .is_some()
         {
             return;
         }
@@ -16128,7 +16140,17 @@ impl TerminalView {
             return;
         };
         if conversation.is_entirely_passive()
-            || !conversation.status().should_trigger_notification()
+            || !conversation
+                .status()
+                .should_notify_for_agent(conversation.is_child_agent_conversation())
+            || self.last_desktop_notification_status.get(conversation_id)
+                == Some(conversation.status())
+        {
+            return;
+        }
+
+        if matches!(conversation.status(), ConversationStatus::Success)
+            && QueuedQueryModel::as_ref(ctx).has_autofireable_prompt(*conversation_id)
         {
             return;
         }
@@ -16142,6 +16164,8 @@ impl TerminalView {
         } else {
             NotificationsTrigger::AgentTaskCompleted(block_summary.success)
         };
+        self.last_desktop_notification_status
+            .insert(*conversation_id, conversation.status().clone());
         self.send_agent_desktop_notification_or_show_banner(
             trigger,
             block_summary.title,

crates/galaxy_agent_rig/src/native.rs

@@ -2,15 +2,15 @@ use std::collections::HashSet;
 use std::sync::{Arc, OnceLock};
 
 use async_trait::async_trait;
-use futures::StreamExt;
 use futures::future::BoxFuture;
+use futures::StreamExt;
 use galaxy_agent_core::{
     AgentError, AgentErrorKind, AgentEventStream, AgentRuntime, RuntimeCapabilities,
     RuntimeDescriptor, RuntimeKind, TurnControl, TurnRequest,
 };
 use rig_core::client::{CompletionClient, ModelListingClient};
-use rig_core::providers::gemini;
 use rig_core::http_client::{self, HttpClientExt};
+use rig_core::providers::gemini;
 
 use crate::anthropic_auth::{anthropic_client, prepare_subscription_request, restore_tool_names};
 use crate::request::build_completion_request;
@@ -22,6 +22,7 @@ pub struct RigModelInfo {
     pub display_name: String,
     pub context_size: Option<u32>,
     pub max_output_tokens: Option<u32>,
+    pub reasoning_efforts: Vec<String>,
 }
 
 pub async fn discover_anthropic_models(api_key: &str) -> Result<Vec<RigModelInfo>, String> {
@@ -30,30 +31,48 @@ pub async fn discover_anthropic_models(api_key: &str) -> Result<Vec<RigModelInfo
     let mut cursors = HashSet::new();
     let mut path = "/v1/models?limit=1000".to_string();
     loop {
-        let request = client.get(&path).and_then(|request| Ok(request.body(http_client::NoBody)?))
+        let request = client
+            .get(&path)
+            .and_then(|request| Ok(request.body(http_client::NoBody)?))
             .map_err(|_| "Could not build the Anthropic models request.".to_string())?;
-        let response = client.send::<_, Vec<u8>>(request).await
+        let response = client
+            .send::<_, Vec<u8>>(request)
+            .await
             .map_err(|_| "Could not retrieve Anthropic models.".to_string())?;
         if !response.status().is_success() {
-            return Err(format!("Anthropic model discovery returned HTTP {}.", response.status()));
+            return Err(format!(
+                "Anthropic model discovery returned HTTP {}.",
+                response.status()
+            ));
         }
-        let body = response.into_body().await
+        let body = response
+            .into_body()
+            .await
             .map_err(|_| "Could not read the Anthropic models response.".to_string())?;
         let page: serde_json::Value = serde_json::from_slice(&body)
             .map_err(|_| "Invalid Anthropic models response.".to_string())?;
-        let entries = page["data"].as_array()
+        let entries = page["data"]
+            .as_array()
             .ok_or_else(|| "Anthropic models response is missing its model list.".to_string())?;
         for entry in entries {
             models.push(anthropic_model_info(entry)?);
         }
         match page["has_more"].as_bool() {
             Some(false) => return Ok(models),
-            Some(true) => {},
+            Some(true) => {}
-            None => return Err("Anthropic models response is missing pagination state.".to_string()),
+            None => {
+                return Err("Anthropic models response is missing pagination state.".to_string())
             }
-        let cursor = page["last_id"].as_str().filter(|cursor| {
+        }
-            !cursor.is_empty() && cursor.bytes().all(|byte| byte.is_ascii_alphanumeric() || matches!(byte, b'-' | b'_' | b'.'))
+        let cursor = page["last_id"]
-        }).ok_or_else(|| "Invalid Anthropic model pagination cursor.".to_string())?;
+            .as_str()
+            .filter(|cursor| {
+                !cursor.is_empty()
+                    && cursor.bytes().all(|byte| {
+                        byte.is_ascii_alphanumeric() || matches!(byte, b'-' | b'_' | b'.')
+                    })
+            })
+            .ok_or_else(|| "Invalid Anthropic model pagination cursor.".to_string())?;
         if !cursors.insert(cursor.to_string()) {
             return Err("Anthropic model pagination did not advance.".to_string());
         }
@@ -62,17 +81,24 @@ pub async fn discover_anthropic_models(api_key: &str) -> Result<Vec<RigModelInfo
 }
 
 fn positive_token_limit(value: &serde_json::Value) -> Option<u32> {
-    value.as_u64().and_then(|tokens| u32::try_from(tokens).ok()).filter(|tokens| *tokens > 0)
+    value
+        .as_u64()
+        .and_then(|tokens| u32::try_from(tokens).ok())
+        .filter(|tokens| *tokens > 0)
 }
 
 fn anthropic_model_info(entry: &serde_json::Value) -> Result<RigModelInfo, String> {
-    let id = entry["id"].as_str().filter(|id| !id.is_empty())
+    let id = entry["id"]
+        .as_str()
+        .filter(|id| !id.is_empty())
         .ok_or_else(|| "Anthropic returned a model without an ID.".to_string())?;
     // Read live limits first. Rig's generic model lister drops these fields.
     // The models.dev snapshot is metadata only, never an availability allowlist.
     static LIMITS: OnceLock<serde_json::Value> = OnceLock::new();
-    let limits = LIMITS.get_or_init(|| serde_json::from_str(include_str!("../data/anthropic_limits.json"))
+    let limits = LIMITS.get_or_init(|| {
-        .expect("bundled Anthropic limits must be valid JSON"));
+        serde_json::from_str(include_str!("../data/anthropic_limits.json"))
+            .expect("bundled Anthropic limits must be valid JSON")
+    });
     let fallback = &limits["models"][id];
     Ok(RigModelInfo {
         id: id.to_string(),
@@ -81,9 +107,22 @@ fn anthropic_model_info(entry: &serde_json::Value) -> Result<RigModelInfo, Strin
             .or_else(|| positive_token_limit(&fallback["context"])),
         max_output_tokens: positive_token_limit(&entry["max_tokens"])
             .or_else(|| positive_token_limit(&fallback["output"])),
+        reasoning_efforts: anthropic_reasoning_efforts(entry),
     })
 }
 
+fn anthropic_reasoning_efforts(entry: &serde_json::Value) -> Vec<String> {
+    let effort = &entry["capabilities"]["effort"];
+    if effort["supported"].as_bool() != Some(true) {
+        return Vec::new();
+    }
+    ["low", "medium", "high", "xhigh", "max"]
+        .into_iter()
+        .filter(|level| effort[*level]["supported"].as_bool() == Some(true))
+        .map(str::to_string)
+        .collect()
+}
+
 pub async fn discover_gemini_models(api_key: &str) -> Result<Vec<RigModelInfo>, String> {
     let client = gemini::Client::new(api_key).map_err(|error| error.to_string())?;
     let models = client
@@ -98,6 +137,7 @@ pub async fn discover_gemini_models(api_key: &str) -> Result<Vec<RigModelInfo>,
             id: model.id,
             context_size: model.context_length,
             max_output_tokens: None,
+            reasoning_efforts: Vec::new(),
         })
         .collect())
 }
@@ -125,6 +165,7 @@ pub fn vertex_ai_model_catalog() -> Vec<RigModelInfo> {
         display_name: display_name.to_string(),
         context_size: None,
         max_output_tokens: None,
+        reasoning_efforts: Vec::new(),
     })
     .collect()
 }
@@ -168,6 +209,7 @@ pub struct AnthropicRuntime {
     config: AnthropicRuntimeConfig,
     descriptor: RuntimeDescriptor,
     credential_resolver: Option<AnthropicCredentialResolver>,
+    reasoning_effort: Option<String>,
 }
 
 impl std::fmt::Debug for AnthropicRuntime {
@@ -181,12 +223,18 @@ impl std::fmt::Debug for AnthropicRuntime {
 }
 
 impl AnthropicRuntime {
+    pub fn with_reasoning_effort(mut self, effort: Option<String>) -> Self {
+        self.reasoning_effort = effort;
+        self
+    }
+
     pub fn new(config: AnthropicRuntimeConfig) -> Self {
         let descriptor = native_descriptor("anthropic", &config.model);
         Self {
             config,
             descriptor,
             credential_resolver: None,
+            reasoning_effort: None,
         }
     }
 
@@ -224,8 +272,15 @@ impl AgentRuntime for AnthropicRuntime {
             .map_err(|error| AgentError::new(AgentErrorKind::Configuration, error.to_string()))?;
         let mut model = client.completion_model(&self.config.model);
         let max_output_tokens = request.max_output_tokens.or(self.config.max_output_tokens);
-        let mut completion_request =
+        let mut completion_request = build_completion_request(
-            build_completion_request(request, max_output_tokens, true, true, None)?;
+            request,
+            max_output_tokens,
+            true,
+            true,
+            self.reasoning_effort
+                .as_deref()
+                .map(anthropic_reasoning_params),
+        )?;
         let aliases = if crate::is_claude_subscription_token(&credential) {
             model = model.with_prompt_caching();
             Some(prepare_subscription_request(&mut completion_request)?)
@@ -256,14 +311,24 @@ pub struct GeminiRuntimeConfig {
 
 #[derive(Clone, Debug)]
 pub struct GeminiRuntime {
+    reasoning_effort: Option<String>,
     config: GeminiRuntimeConfig,
     descriptor: RuntimeDescriptor,
 }
 
 impl GeminiRuntime {
+    pub fn with_reasoning_effort(mut self, effort: Option<String>) -> Self {
+        self.reasoning_effort = effort;
+        self
+    }
+
     pub fn new(config: GeminiRuntimeConfig) -> Self {
         let descriptor = native_descriptor("gemini", &config.model);
-        Self { config, descriptor }
+        Self {
+            config,
+            descriptor,
+            reasoning_effort: None,
+        }
     }
 }
 
@@ -288,8 +353,15 @@ impl AgentRuntime for GeminiRuntime {
             .map_err(|error| AgentError::new(AgentErrorKind::Configuration, error.to_string()))?;
         let model = client.completion_model(&self.config.model);
         let max_output_tokens = request.max_output_tokens.or(self.config.max_output_tokens);
-        let completion_request =
+        let completion_request = build_completion_request(
-            build_completion_request(request, max_output_tokens, true, true, None)?;
+            request,
+            max_output_tokens,
+            true,
+            true,
+            self.reasoning_effort
+                .as_deref()
+                .map(gemini_reasoning_params),
+        )?;
         start_model_turn(model, completion_request, control, max_output_tokens).await
     }
 }
@@ -304,14 +376,24 @@ pub struct VertexAiRuntimeConfig {
 
 #[derive(Clone, Debug)]
 pub struct VertexAiRuntime {
+    reasoning_effort: Option<String>,
     config: VertexAiRuntimeConfig,
     descriptor: RuntimeDescriptor,
 }
 
 impl VertexAiRuntime {
+    pub fn with_reasoning_effort(mut self, effort: Option<String>) -> Self {
+        self.reasoning_effort = effort;
+        self
+    }
+
     pub fn new(config: VertexAiRuntimeConfig) -> Self {
         let descriptor = native_descriptor("vertex-ai", &config.model);
-        Self { config, descriptor }
+        Self {
+            config,
+            descriptor,
+            reasoning_effort: None,
+        }
     }
 }
 
@@ -343,12 +425,27 @@ impl AgentRuntime for VertexAiRuntime {
             .map_err(|error| AgentError::new(AgentErrorKind::Configuration, error.to_string()))?;
         let model = client.completion_model(&self.config.model);
         let max_output_tokens = request.max_output_tokens.or(self.config.max_output_tokens);
-        let completion_request =
+        let completion_request = build_completion_request(
-            build_completion_request(request, max_output_tokens, true, true, None)?;
+            request,
+            max_output_tokens,
+            true,
+            true,
+            self.reasoning_effort
+                .as_deref()
+                .map(gemini_reasoning_params),
+        )?;
         start_model_completion(model, completion_request, control, max_output_tokens).await
     }
 }
 
+fn anthropic_reasoning_params(effort: &str) -> serde_json::Value {
+    serde_json::json!({ "output_config": { "effort": effort } })
+}
+
+fn gemini_reasoning_params(effort: &str) -> serde_json::Value {
+    serde_json::json!({ "generationConfig": { "thinkingConfig": { "thinkingLevel": effort } } })
+}
+
 fn native_descriptor(provider: &str, model: &str) -> RuntimeDescriptor {
     RuntimeDescriptor {
         id: format!("rig-{provider}:{model}"),

crates/galaxy_agent_rig/src/native_tests.rs

@@ -21,10 +21,67 @@ fn anthropic_discovery_prefers_live_limits_over_catalog_defaults() {
 fn anthropic_discovery_uses_exact_catalog_limits_when_api_omits_them() {
     let model = super::anthropic_model_info(&serde_json::json!({
         "id": "claude-opus-4-6", "max_input_tokens": null, "max_tokens": null
-    })).unwrap();
+    }))
+    .unwrap();
     assert_eq!(model.context_size, Some(1000000));
     assert_eq!(model.max_output_tokens, Some(128000));
-    let unknown = super::anthropic_model_info(&serde_json::json!({"id": "claude-unknown"})).unwrap();
+    let unknown =
+        super::anthropic_model_info(&serde_json::json!({"id": "claude-unknown"})).unwrap();
     assert_eq!(unknown.context_size, None);
     assert_eq!(unknown.max_output_tokens, None);
 }
+
+#[test]
+fn anthropic_discovery_reads_only_supported_effort_capabilities() {
+    let model = super::anthropic_model_info(&serde_json::json!({
+        "id": "claude-future", "capabilities": {"effort": {
+            "supported": true, "low": {"supported": true},
+            "medium": {"supported": false}, "high": {"supported": true},
+            "xhigh": {"supported": true}, "max": {"supported": false}
+        }}
+    }))
+    .unwrap();
+    assert_eq!(model.reasoning_efforts, ["low", "high", "xhigh"]);
+    assert!(super::anthropic_reasoning_efforts(&serde_json::json!({
+        "capabilities": {"effort": {"supported": false, "low": {"supported": true}}}
+    }))
+    .is_empty());
+    assert!(super::anthropic_reasoning_efforts(&serde_json::json!({
+        "id": "claude-opus-4-6"
+    }))
+    .is_empty());
+}
+
+#[test]
+fn native_reasoning_selections_use_provider_request_fields() {
+    assert_eq!(
+        super::anthropic_reasoning_params("high"),
+        serde_json::json!({
+            "output_config": {"effort": "high"}
+        })
+    );
+    assert_eq!(
+        super::gemini_reasoning_params("low"),
+        serde_json::json!({
+            "generationConfig": {"thinkingConfig": {"thinkingLevel": "low"}}
+        })
+    );
+}
+
+#[test]
+fn gemini_reasoning_parameters_survive_rig_typed_conversion() {
+    use rig_core::providers::gemini::completion::gemini_api_types::{
+        AdditionalParameters, ThinkingLevel,
+    };
+    let params: AdditionalParameters =
+        serde_json::from_value(super::gemini_reasoning_params("medium")).unwrap();
+    assert_eq!(
+        params
+            .generation_config
+            .unwrap()
+            .thinking_config
+            .unwrap()
+            .thinking_level,
+        Some(ThinkingLevel::Medium)
+    );
+}

crates/galaxy_agent_rig/src/openai_compatible.rs

@@ -23,9 +23,15 @@ pub struct OpenAICompatibleRuntimeConfig {
 pub struct OpenAICompatibleRuntime {
     config: OpenAICompatibleRuntimeConfig,
     descriptor: RuntimeDescriptor,
+    reasoning_effort: Option<String>,
 }
 
 impl OpenAICompatibleRuntime {
+    pub fn with_reasoning_effort(mut self, effort: Option<String>) -> Self {
+        self.reasoning_effort = effort;
+        self
+    }
+
     pub fn new(config: OpenAICompatibleRuntimeConfig) -> Self {
         let descriptor = RuntimeDescriptor {
             id: format!("rig-openai-compatible:{}", config.model),
@@ -33,7 +39,11 @@ impl OpenAICompatibleRuntime {
             kind: RuntimeKind::Provider,
             capabilities: RuntimeCapabilities::provider(),
         };
-        Self { config, descriptor }
+        Self {
+            config,
+            descriptor,
+            reasoning_effort: None,
+        }
     }
 }
 
@@ -62,6 +72,7 @@ impl AgentRuntime for OpenAICompatibleRuntime {
             control,
             self.config.max_output_tokens,
             self.config.supports_system_messages,
+            self.reasoning_effort.as_deref(),
         )
         .await
     }
@@ -73,6 +84,7 @@ async fn start_model_turn<M>(
     control: TurnControl,
     configured_max_output_tokens: Option<u64>,
     supports_system_messages: bool,
+    reasoning_effort: Option<&str>,
 ) -> Result<AgentEventStream, AgentError>
 where
     M: CompletionModel + Send + Sync + 'static,
@@ -82,6 +94,7 @@ where
         request,
         configured_max_output_tokens,
         supports_system_messages,
+        reasoning_effort,
     )?;
     start_provider_model_turn(model, completion_request, control, max_output_tokens).await
 }
@@ -90,15 +103,18 @@ fn build_completion_request(
     request: TurnRequest,
     configured_max_output_tokens: Option<u64>,
     supports_system_messages: bool,
+    reasoning_effort: Option<&str>,
 ) -> Result<CompletionRequest, AgentError> {
+    let mut params = serde_json::json!({ "stream_options": { "include_usage": true } });
+    if let Some(effort) = reasoning_effort {
+        params["reasoning_effort"] = effort.into();
+    }
     build_provider_completion_request(
         request,
         configured_max_output_tokens,
         supports_system_messages,
         true,
-        Some(serde_json::json!({
+        Some(params),
-            "stream_options": { "include_usage": true }
-        })),
     )
 }
 

crates/galaxy_agent_rig/src/openai_compatible_tests.rs

@@ -49,7 +49,7 @@ async fn rig_stream_maps_reasoning_text_usage_and_stop() {
     let model = client.completion_model("test-model");
     let (_, control) = galaxy_agent_core::turn_control();
 
-    let events = start_model_turn(model, text_request(), control, None, true)
+    let events = start_model_turn(model, text_request(), control, None, true, None)
         .await
         .unwrap()
         .collect::<Vec<_>>()
@@ -104,7 +104,7 @@ async fn rig_stream_ignores_empty_reasoning_heartbeats() {
     let model = client.completion_model("test-model");
     let (_, control) = galaxy_agent_core::turn_control();
 
-    let events = start_model_turn(model, text_request(), control, None, true)
+    let events = start_model_turn(model, text_request(), control, None, true, None)
         .await
         .unwrap()
         .collect::<Vec<_>>()
@@ -118,11 +118,9 @@ async fn rig_stream_ignores_empty_reasoning_heartbeats() {
         AgentEvent::ReasoningCompleted { text, signature }
             if text.is_empty() && signature.is_none()
     )));
-    assert!(
+    assert!(events
-        events
         .iter()
-            .any(|event| matches!(event, AgentEvent::TextDelta { text } if text == "done"))
+        .any(|event| matches!(event, AgentEvent::TextDelta { text } if text == "done")));
-    );
 }
 
 #[tokio::test]
@@ -176,7 +174,7 @@ async fn usage_at_the_requested_limit_maps_to_max_tokens() {
     let mut request = text_request();
     request.max_output_tokens = Some(6);
 
-    let events = start_model_turn(model, request, control, None, true)
+    let events = start_model_turn(model, request, control, None, true, None)
         .await
         .unwrap()
         .collect::<Vec<_>>()
@@ -219,7 +217,7 @@ async fn rig_stream_maps_complete_tool_call_without_executing_it() {
         input_schema: serde_json::json!({"type": "object"}),
     });
 
-    let events = start_model_turn(model, request, control, None, true)
+    let events = start_model_turn(model, request, control, None, true, None)
         .await
         .unwrap()
         .collect::<Vec<_>>()
@@ -274,7 +272,7 @@ fn request_conversion_preserves_history_tools_and_limits() {
         }),
     });
 
-    let converted = build_completion_request(request, Some(999), true).unwrap();
+    let converted = build_completion_request(request, Some(999), true, None).unwrap();
 
     assert_eq!(converted.max_tokens, Some(123));
     assert_eq!(converted.tools.len(), 1);
@@ -318,7 +316,7 @@ fn request_conversion_preserves_tool_call_and_denied_result_for_the_next_turn()
         ],
     );
 
-    let converted = build_completion_request(request, None, true).unwrap();
+    let converted = build_completion_request(request, None, true, None).unwrap();
     let messages = converted.chat_history.iter().collect::<Vec<_>>();
     let Message::Assistant { content, .. } = messages[0] else {
         panic!("expected assistant tool call");
@@ -352,7 +350,7 @@ fn request_conversion_places_system_prompt_in_user_message_when_system_role_is_u
     let mut request = text_request();
     request.system_prompt = Some("Be useful".to_string());
 
-    let converted = build_completion_request(request, None, false).unwrap();
+    let converted = build_completion_request(request, None, false, None).unwrap();
     let messages = converted.chat_history.iter().collect::<Vec<_>>();
 
     assert_eq!(messages.len(), 2);
@@ -371,3 +369,34 @@ fn request_conversion_places_system_prompt_in_user_message_when_system_role_is_u
         0
     );
 }
+
+#[test]
+fn selected_reasoning_effort_is_sent_without_changing_the_model() {
+    let request = build_completion_request(text_request(), None, true, Some("high")).unwrap();
+    assert_eq!(
+        request.additional_params,
+        Some(serde_json::json!({
+            "stream_options": {"include_usage": true}, "reasoning_effort": "high"
+        }))
+    );
+    let default = build_completion_request(text_request(), None, true, None).unwrap();
+    assert!(default
+        .additional_params
+        .unwrap()
+        .get("reasoning_effort")
+        .is_none());
+}
+
+#[test]
+fn reasoning_effort_survives_openai_request_serialization() {
+    let request = build_completion_request(text_request(), None, true, Some("low")).unwrap();
+    let wire = rig_core::providers::openai::completion::CompletionRequest::try_from((
+        "test-model".to_string(),
+        request,
+    ))
+    .unwrap();
+    let json = serde_json::to_value(wire).unwrap();
+    assert_eq!(json["model"], "test-model");
+    assert_eq!(json["reasoning_effort"], "low");
+    assert_eq!(json["stream_options"]["include_usage"], true);
+}