fix: stream reasoning_content in LM Studio provider

src/api/providers/__tests__/lmstudio.spec.ts

@@ -112,6 +112,59 @@ describe("LmStudioHandler", () => {
 			expect(textChunks[0].text).toBe("Test response")
 		})
 
+		it("streams reasoning chunks from delta.reasoning_content", async () => {
+			// Regression: Qwen3 / DeepSeek-R1 style models served by LM Studio emit
+			// thinking via reasoning_content, not <think> tags inside content.
+			mockCreate.mockImplementationOnce(async () =>
+				asyncStreamFrom([
+					{ choices: [{ delta: { reasoning_content: "thinking..." }, index: 0 }] },
+					{ choices: [{ delta: { content: "answer" }, index: 0 }] },
+					{
+						choices: [{ delta: {}, index: 0 }],
+						usage: { prompt_tokens: 1, completion_tokens: 1, total_tokens: 2 },
+					},
+				]),
+			)
+
+			const chunks = await collectStream(handler.createMessage(systemPrompt, messages))
+
+			expect(chunks).toContainEqual({ type: "reasoning", text: "thinking..." })
+			expect(chunks).toContainEqual({ type: "text", text: "answer" })
+		})
+
+		it("falls back to delta.reasoning when reasoning_content is absent", async () => {
+			mockCreate.mockImplementationOnce(async () =>
+				asyncStreamFrom([
+					{ choices: [{ delta: { reasoning: "router-style thought" }, index: 0 }] },
+					{
+						choices: [{ delta: {}, index: 0 }],
+						usage: { prompt_tokens: 1, completion_tokens: 1, total_tokens: 2 },
+					},
+				]),
+			)
+
+			const chunks = await collectStream(handler.createMessage(systemPrompt, messages))
+
+			expect(chunks).toContainEqual({ type: "reasoning", text: "router-style thought" })
+		})
+
+		it("still parses <think> tags embedded in content", async () => {
+			mockCreate.mockImplementationOnce(async () =>
+				asyncStreamFrom([
+					{ choices: [{ delta: { content: "<think>tagged thought</think>visible" }, index: 0 }] },
+					{
+						choices: [{ delta: {}, index: 0 }],
+						usage: { prompt_tokens: 1, completion_tokens: 1, total_tokens: 2 },
+					},
+				]),
+			)
+
+			const chunks = await collectStream(handler.createMessage(systemPrompt, messages))
+
+			expect(chunks).toContainEqual({ type: "reasoning", text: "tagged thought" })
+			expect(chunks).toContainEqual({ type: "text", text: "visible" })
+		})
+
 		it("should handle API errors", async () => {
 			mockCreate.mockRejectedValueOnce(new Error("API Error"))
 

src/api/providers/lm-studio.ts

@@ -16,6 +16,7 @@ import { BaseProvider } from "./base-provider"
 import type { SingleCompletionHandler, ApiHandlerCreateMessageMetadata, CompletePromptOptions } from "../index"
 import { getModelsFromCache } from "./fetchers/modelCache"
 import { handleOpenAIError } from "./utils/error-handler"
+import { extractReasoningFromDelta } from "./utils/extract-reasoning"
 
 export class LmStudioHandler extends BaseProvider implements SingleCompletionHandler {
 	protected options: ApiHandlerOptions
@@ -80,6 +81,7 @@ export class LmStudioHandler extends BaseProvider implements SingleCompletionHan
 		}
 
 		let assistantText = ""
+		let reasoningOutput = ""
 
 		try {
 			const params: OpenAI.Chat.ChatCompletionCreateParamsStreaming & { draft_model?: string } = {
@@ -123,6 +125,15 @@ export class LmStudioHandler extends BaseProvider implements SingleCompletionHan
 					}
 				}
 
+				// Reasoning models served by LM Studio (Qwen3, DeepSeek-R1, QwQ, ...) stream
+				// their thinking in a dedicated `reasoning_content`/`reasoning` delta field
+				// rather than as <think> tags inside `content`, so TagMatcher never sees it.
+				const reasoningText = extractReasoningFromDelta(delta)
+				if (reasoningText) {
+					reasoningOutput += reasoningText
+					yield { type: "reasoning", text: reasoningText }
+				}
+
 				// Handle tool calls in stream - emit partial chunks for NativeToolCallParser
 				if (delta?.tool_calls) {
 					for (const toolCall of delta.tool_calls) {
@@ -151,7 +162,9 @@ export class LmStudioHandler extends BaseProvider implements SingleCompletionHan
 
 			let outputTokens = 0
 			try {
-				outputTokens = await this.countTokens([{ type: "text", text: assistantText }])
+				// Reasoning tokens are billed as output, so count them alongside the
+				// visible text — otherwise thinking models under-report usage entirely.
+				outputTokens = await this.countTokens([{ type: "text", text: reasoningOutput + assistantText }])
 			} catch (err) {
 				console.error("[LmStudio] Failed to count output tokens:", err)
 				outputTokens = 0