diff --git a/src/api/providers/lmstudio.ts b/src/api/providers/lmstudio.ts
index 6053ed056d4..0901cb27680 100644
--- a/src/api/providers/lmstudio.ts
+++ b/src/api/providers/lmstudio.ts
@@ -25,57 +25,108 @@ export class LmStudioHandler extends BaseProvider implements SingleCompletionHan
 	}
 
 	override async *createMessage(systemPrompt: string, messages: Anthropic.Messages.MessageParam[]): ApiStream {
-		const openAiMessages: OpenAI.Chat.ChatCompletionMessageParam[] = [
-			{ role: "system", content: systemPrompt },
-			...convertToOpenAiMessages(messages),
-		]
+	const openAiMessages: OpenAI.Chat.ChatCompletionMessageParam[] = [
+		{ role: "system", content: systemPrompt },
+		...convertToOpenAiMessages(messages),
+	]
 
-		try {
-			// Create params object with optional draft model
-			const params: any = {
-				model: this.getModel().id,
-				messages: openAiMessages,
-				temperature: this.options.modelTemperature ?? LMSTUDIO_DEFAULT_TEMPERATURE,
-				stream: true,
-			}
-
-			// Add draft model if speculative decoding is enabled and a draft model is specified
-			if (this.options.lmStudioSpeculativeDecodingEnabled && this.options.lmStudioDraftModelId) {
-				params.draft_model = this.options.lmStudioDraftModelId
-			}
+	// -------------------------
+	// Track token usage
+	// -------------------------
+	const toContentBlocks = (
+		blocks: Anthropic.Messages.MessageParam[] | string,
+	): Anthropic.Messages.ContentBlockParam[] => {
+		if (typeof blocks === "string") {
+			return [{ type: "text", text: blocks }]
+		}
 
-			const results = await this.client.chat.completions.create(params)
-			
-			const matcher = new XmlMatcher(
-				"think",
-				(chunk) =>
-					({
-						type: chunk.matched ? "reasoning" : "text",
-						text: chunk.data,
-					}) as const,
-			)
-			
-			// Stream handling
-			// @ts-ignore
-			for await (const chunk of results) {
-				const delta = chunk.choices[0]?.delta
-
-				if (delta?.content) {
-					for (const chunk of matcher.update(delta.content)) {
-						yield chunk
+		const result: Anthropic.Messages.ContentBlockParam[] = []
+		for (const msg of blocks) {
+			if (typeof msg.content === "string") {
+				result.push({ type: "text", text: msg.content })
+			} else if (Array.isArray(msg.content)) {
+				for (const part of msg.content) {
+					if (part.type === "text") {
+						result.push({ type: "text", text: part.text })
 					}
 				}
 			}
-			for (const chunk of matcher.final()) {
-				yield chunk
+		}
+		return result
+	}
+
+	let inputTokens = 0
+	try {
+		inputTokens = await this.countTokens([
+			{ type: "text", text: systemPrompt },
+			...toContentBlocks(messages),
+		])
+	} catch (err) {
+		console.error("[LmStudio] Failed to count input tokens:", err)
+		inputTokens = 0
+	}
+
+	let assistantText = ""
+
+	try {
+		const params: OpenAI.Chat.ChatCompletionCreateParamsStreaming & { draft_model?: string } = {
+			model: this.getModel().id,
+			messages: openAiMessages,
+			temperature: this.options.modelTemperature ?? LMSTUDIO_DEFAULT_TEMPERATURE,
+			stream: true,
+		}
+
+		if (this.options.lmStudioSpeculativeDecodingEnabled && this.options.lmStudioDraftModelId) {
+			params.draft_model = this.options.lmStudioDraftModelId
+		}
+
+		const results = await this.client.chat.completions.create(params)
+
+		const matcher = new XmlMatcher(
+			"think",
+			(chunk) =>
+				({
+					type: chunk.matched ? "reasoning" : "text",
+					text: chunk.data,
+				}) as const,
+		)
+
+		for await (const chunk of results) {
+			const delta = chunk.choices[0]?.delta
+
+			if (delta?.content) {
+				assistantText += delta.content
+				for (const processedChunk of matcher.update(delta.content)) {
+					yield processedChunk
+				}
 			}
-		} catch (error) {
-			// LM Studio doesn't return an error code/body for now
-			throw new Error(
-				"Please check the LM Studio developer logs to debug what went wrong. You may need to load the model with a larger context length to work with Roo Code's prompts.",
-			)
 		}
+
+		for (const processedChunk of matcher.final()) {
+			yield processedChunk
+		}
+
+		
+		let outputTokens = 0
+		try {
+			outputTokens = await this.countTokens([{ type: "text", text: assistantText }])
+		} catch (err) {
+			console.error("[LmStudio] Failed to count output tokens:", err)
+			outputTokens = 0
+		}
+
+		yield {
+			type: "usage",
+			inputTokens,
+			outputTokens,
+		} as const
+	} catch (error) {
+		throw new Error(
+			"Please check the LM Studio developer logs to debug what went wrong. You may need to load the model with a larger context length to work with Roo Code's prompts.",
+		)
 	}
+}
+
 
 	override getModel(): { id: string; info: ModelInfo } {
 		return {
diff --git a/src/api/providers/ollama.ts b/src/api/providers/ollama.ts
index 26374d5d583..1b721a59093 100644
--- a/src/api/providers/ollama.ts
+++ b/src/api/providers/ollama.ts
@@ -11,6 +11,9 @@ import { DEEP_SEEK_DEFAULT_TEMPERATURE } from "./constants"
 import { XmlMatcher } from "../../utils/xml-matcher"
 import { BaseProvider } from "./base-provider"
 
+// Alias for the usage object returned in streaming chunks
+type CompletionUsage = OpenAI.Chat.Completions.ChatCompletionChunk["usage"]
+
 export class OllamaHandler extends BaseProvider implements SingleCompletionHandler {
 	protected options: ApiHandlerOptions
 	private client: OpenAI
@@ -37,6 +40,7 @@ export class OllamaHandler extends BaseProvider implements SingleCompletionHandl
 			messages: openAiMessages,
 			temperature: this.options.modelTemperature ?? 0,
 			stream: true,
+			stream_options: { include_usage: true },
 		})
 		const matcher = new XmlMatcher(
 			"think",
@@ -46,18 +50,30 @@ export class OllamaHandler extends BaseProvider implements SingleCompletionHandl
 					text: chunk.data,
 				}) as const,
 		)
+		let lastUsage: CompletionUsage | undefined
 		for await (const chunk of stream) {
 			const delta = chunk.choices[0]?.delta
 
 			if (delta?.content) {
-				for (const chunk of matcher.update(delta.content)) {
-					yield chunk
+				for (const matcherChunk of matcher.update(delta.content)) {
+					yield matcherChunk
 				}
 			}
+			if (chunk.usage) {
+				lastUsage = chunk.usage
+			}
 		}
 		for (const chunk of matcher.final()) {
 			yield chunk
 		}
+
+		if (lastUsage) {
+			yield {
+				type: "usage",
+				inputTokens: lastUsage?.prompt_tokens || 0,
+				outputTokens: lastUsage?.completion_tokens || 0,
+			}
+		}
 	}
 
 	override getModel(): { id: string; info: ModelInfo } {