diff --git a/docs/mistral-devstral2-performance.md b/docs/mistral-devstral2-performance.md
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+# Mistral Devstral 2 Performance and Thinking Tokens
+
+## Overview
+
+This document explains how Roo Code handles Mistral Devstral 2 models, including thinking token support and performance considerations.
+
+## Thinking Token Support
+
+### What are Thinking Tokens?
+
+Mistral Devstral 2 models support a "thinking" mode where the model's reasoning process is streamed separately from the final response. This allows users to see the model's thought process in real-time, similar to how the Mistral Vibe CLI displays "thinking" indicators.
+
+### Implementation Status
+
+**✅ Thinking tokens are fully supported** in Roo Code's Mistral handler.
+
+The implementation is located in [`src/api/providers/mistral.ts`](../src/api/providers/mistral.ts):
+
+```typescript
+// Lines 122-129
+if (chunk.type === "thinking" && chunk.thinking) {
+    // Handle thinking content as reasoning chunks
+    for (const thinkingPart of chunk.thinking) {
+        if (thinkingPart.type === "text" && thinkingPart.text) {
+            yield { type: "reasoning", text: thinkingPart.text }
+        }
+    }
+}
+```
+
+### How It Works
+
+1. **Streaming**: When Devstral 2 generates a response, it streams two types of content:
+
+    - `"thinking"` chunks: The model's reasoning process
+    - `"text"` chunks: The final response
+
+2. **Display**: Thinking chunks are yielded as `{ type: "reasoning", text: ... }` which the UI displays separately from the final response, allowing users to see the model's thought process.
+
+3. **SDK Support**: The Mistral SDK v1.9.18+ includes `ThinkChunk` support for handling thinking tokens.
+
+## Performance Considerations
+
+### Current Performance
+
+The user reported that "performance is suboptimal" when using Devstral 2 models. Here are the key factors:
+
+1. **Thinking Tokens Add Latency**: When the model generates thinking tokens, it takes additional time before the final response begins. This is expected behavior and shows the model is working through the problem.
+
+2. **Streaming is Enabled**: The handler properly streams both thinking and text tokens, so users see progress in real-time rather than waiting for the complete response.
+
+3. **Temperature Setting**: PR #9957 set the default temperature to 0.2 based on Mistral's recommendations for optimal performance.
+
+### Prompt Caching
+
+**Current Status**: Mistral models currently have `supportsPromptCache: false` in the model definitions.
+
+**Investigation Needed**:
+
+- The Mistral API documentation does not clearly indicate whether prompt caching is supported for Devstral 2 models
+- Unlike Anthropic or OpenAI, Mistral's API documentation doesn't provide explicit caching endpoints or parameters
+- Further investigation with Mistral's API team would be needed to determine if caching is available
+
+### Recommendations for Users
+
+1. **Expect Thinking Time**: When using Devstral 2, the "thinking" phase is normal and indicates the model is reasoning through the problem. This is a feature, not a bug.
+
+2. **Temperature**: The default temperature of 0.2 is optimized for code generation tasks. Users can adjust this in settings if needed.
+
+3. **Model Selection**:
+
+    - Use `devstral-latest` or `devstral-2512` for complex reasoning tasks where thinking tokens are valuable
+    - Use `devstral-small-latest` or `labs-devstral-small-2512` for faster responses on simpler tasks
+
+4. **Monitor the UI**: The thinking tokens should be visible in the UI, showing the model's reasoning process. If they're not appearing, this may indicate a UI rendering issue rather than an API issue.
+
+## Technical Details
+
+### Mistral SDK Version
+
+Roo Code uses `@mistralai/mistralai` version `^1.9.18`, which includes support for thinking chunks.
+
+### Content Chunk Types
+
+The Mistral API returns content in different formats:
+
+```typescript
+type ContentChunkWithThinking = {
+	type: string // "thinking" or "text"
+	text?: string // For text chunks
+	thinking?: Array<{
+		// For thinking chunks
+		type: string
+		text?: string
+	}>
+}
+```
+
+### Streaming Flow
+
+1. API request is made with streaming enabled
+2. Server streams back chunks as they're generated
+3. Thinking chunks are processed and yielded as `reasoning` type
+4. Text chunks are processed and yielded as `text` type
+5. UI displays both types appropriately
+
+## Future Improvements
+
+### Potential Optimizations
+
+1. **Prompt Caching**: If Mistral adds prompt caching support, we can:
+
+    - Cache system prompts across requests
+    - Cache conversation history
+    - Reduce latency for follow-up requests
+
+2. **Batch Processing**: For multiple requests, investigate if Mistral supports batch APIs
+
+3. **Connection Pooling**: Ensure HTTP connections are properly pooled and reused
+
+### Monitoring
+
+To help users understand performance:
+
+1. **Token Metrics**: Display thinking token count vs. response token count
+2. **Timing Metrics**: Show time spent in thinking phase vs. response phase
+3. **Progress Indicators**: Enhance UI to better show when model is thinking
+
+## References
+
+- [Mistral Devstral 2 Documentation](https://docs.mistral.ai/models/devstral-2-25-12)
+- [Mistral Vibe CLI](https://github.com/mistralai/mistral-vibe)
+- [Mistral SDK](https://github.com/mistralai/client-ts)
+- [Issue #9951](https://github.com/RooCodeInc/Roo-Code/issues/9951)
+- [PR #9957](https://github.com/RooCodeInc/Roo-Code/pull/9957)
+
+## Questions for Mistral Team
+
+To further optimize performance, we need clarification from Mistral on:
+
+1. Does the Mistral API support prompt caching for Devstral 2 models?
+2. Are there any API parameters to control thinking token generation?
+3. What are the recommended best practices for minimizing latency?
+4. Are there any batch or concurrent request optimizations available?
diff --git a/src/api/providers/mistral.ts b/src/api/providers/mistral.ts
index 96d2c33255..a146f7684f 100644
--- a/src/api/providers/mistral.ts
+++ b/src/api/providers/mistral.ts
@@ -12,8 +12,18 @@ import { ApiStream } from "../transform/stream"
 import { BaseProvider } from "./base-provider"
 import type { SingleCompletionHandler, ApiHandlerCreateMessageMetadata } from "../index"
 
-// Type helper to handle thinking chunks from Mistral API
-// The SDK includes ThinkChunk but TypeScript has trouble with the discriminated union
+/**
+ * Type helper to handle thinking chunks from Mistral API.
+ *
+ * Mistral Devstral 2 models support "thinking" mode where the model's reasoning process
+ * is streamed separately from the final response. This allows users to see the model's
+ * thought process in real-time.
+ *
+ * The SDK includes ThinkChunk but TypeScript has trouble with the discriminated union,
+ * so we define our own type here.
+ *
+ * @see https://docs.mistral.ai/models/devstral-2-25-12
+ */
 type ContentChunkWithThinking = {
 	type: string
 	text?: string
@@ -106,8 +116,23 @@ export class MistralHandler extends BaseProvider implements SingleCompletionHand
 					// Handle string content as text
 					yield { type: "text", text: delta.content }
 				} else if (Array.isArray(delta.content)) {
-					// Handle array of content chunks
-					// The SDK v1.9.18 supports ThinkChunk with type "thinking"
+					/**
+					 * Handle array of content chunks from Mistral API.
+					 *
+					 * Mistral Devstral 2 models support streaming "thinking" tokens that show
+					 * the model's reasoning process. These are streamed as separate chunks with
+					 * type "thinking" and are displayed to users as reasoning steps.
+					 *
+					 * The SDK v1.9.18+ supports ThinkChunk with type "thinking".
+					 *
+					 * Content chunk types:
+					 * - "thinking": Model's reasoning process (yielded as "reasoning" type)
+					 * - "text": Final response text (yielded as "text" type)
+					 *
+					 * This implementation ensures thinking tokens are properly streamed and
+					 * displayed in the UI, addressing performance concerns about showing
+					 * model progress during generation.
+					 */
 					for (const chunk of delta.content as ContentChunkWithThinking[]) {
 						if (chunk.type === "thinking" && chunk.thinking) {
 							// Handle thinking content as reasoning chunks