Updated Baseten models to use dynamic fetching, added docs (#6148)

* Updated Baseten models to use dynamic fetching, added docs

* removed unused function, supportsImages

* added parsePrice into model-utils.ts

* updated toolcalling comment

* updated tools to fetch from API

* fixed conflicts and updated supportTools

* applied npm run format:fix

* added bedrock stuff back in that was breaking test

---------

Co-authored-by: AlexKer <AlexKer@users.noreply.github.com>

.changeset/nice-mirrors-report.md

@@ -0,0 +1,5 @@
+---
+"claude-dev": minor
+---
+
+Updated Baseten models to use dynamic fetching, added docs

docs/provider-config/baseten.mdx

@@ -0,0 +1,114 @@
+---
+title: "Baseten"
+description: "Learn how to configure and use Baseten's Model APIs with Cline. Access frontier open-source models with enterprise-grade performance, reliability, and competitive pricing."
+---
+
+Baseten provides on-demand frontier model APIs designed for production applications, not just experimentation. Built on the Baseten Inference Stack, these APIs deliver enterprise-grade performance and reliability with optimized inference for leading open-source models from OpenAI, DeepSeek, Meta, Moonshot AI, and Alibaba Cloud.
+
+**Website:** [https://www.baseten.co/products/model-apis/](https://www.baseten.co/products/model-apis/)
+
+### Getting an API Key
+
+1.  **Sign Up/Sign In:** Go to [Baseten](https://www.baseten.co/) and create an account or sign in.
+2.  **Navigate to API Keys:** Access your dashboard and go to the API Keys section.
+3.  **Create a Key:** Generate a new API key. Give it a descriptive name (e.g., "Cline").
+4.  **Copy the Key:** Copy the API key immediately and store it securely.
+
+### Supported Models
+
+Cline supports all current models under Baseten Model APIs, including:
+
+**Reasoning Models:**
+-   `deepseek-ai/DeepSeek-R1` - DeepSeek's first-generation reasoning model (163K context) - $2.55/$5.95 per 1M tokens
+-   `deepseek-ai/DeepSeek-R1-0528` - Latest revision of DeepSeek's reasoning model (163K context) - $2.55/$5.95 per 1M tokens
+-   `deepseek-ai/DeepSeek-V3.1` - Hybrid reasoning with advanced tool calling (163K context) - $0.50/$1.50 per 1M tokens
+-   `deepseek-ai/DeepSeek-V3-0324` - Fast general-purpose with enhanced reasoning (163K context) - $0.77/$0.77 per 1M tokens
+
+**Flagship Models:**
+-   `openai/gpt-oss-120b` (OpenAI) - 120B MoE with strong reasoning capabilities (128K context) - $0.10/$0.50 per 1M tokens
+-   `moonshotai/Kimi-K2-Instruct` (Moonshot AI) - 1 trillion parameter model for agentic tasks (131K context) - $0.60/$2.50 per 1M tokens
+-   `moonshotai/Kimi-K2-Instruct-0905` (Moonshot AI) - September update with enhanced capabilities (262K context) - $0.60/$2.50 per 1M tokens
+
+**Meta Llama 4 Series:**
+-   `meta-llama/Llama-4-Maverick-17B-128E-Instruct` - High-efficiency processing (1M context!) - $0.19/$0.72 per 1M tokens
+-   `meta-llama/Llama-4-Scout-17B-16E-Instruct` - Precise context understanding (1M context!) - $0.13/$0.50 per 1M tokens
+
+**Coding Specialists:**
+-   `Qwen/Qwen3-Coder-480B-A35B-Instruct` (Alibaba Cloud) - Advanced coding and reasoning (262K context) - $0.38/$1.53 per 1M tokens
+-   `Qwen/Qwen3-235B-A22B-Instruct-2507` (Alibaba Cloud) - Math and reasoning expert (262K context) - $0.22/$0.80 per 1M tokens
+
+### Configuration in Cline
+
+1.  **Open Cline Settings:** Click the settings icon (⚙️) in the Cline panel.
+2.  **Select Provider:** Choose "Baseten" from the "API Provider" dropdown.
+3.  **Enter API Key:** Paste your Baseten API key into the "Baseten API Key" field.
+4.  **Select Model:** Choose your desired model from the "Model" dropdown.
+
+### Production-First Architecture
+
+Baseten's Model APIs are built for production environments with several key advantages:
+
+#### Enterprise-Grade Reliability
+- **Four nines of uptime** (99.99%) through active-active redundancy
+- **Cloud-agnostic, multi-cluster autoscaling** for consistent availability
+- **SOC 2 Type II certified** and **HIPAA compliant** for security requirements
+
+#### Optimized Performance
+- **Pre-optimized models** shipped with the Baseten Inference Stack
+- **Latest-generation GPUs** with multi-cloud infrastructure
+- **Ultra-fast inference** optimized from the bottom up for production workloads
+
+#### Cost Efficiency
+- **5-10x less expensive** than closed alternatives
+- **Optimized multi-cloud infrastructure** for efficient resource utilization
+- **Transparent pricing** with no hidden costs or rate limit surprises
+
+#### Developer Experience
+- **OpenAI compatible API** - migrate by swapping a single URL
+- **Drop-in replacement** for closed models with comprehensive observability
+- **Seamless scaling** from Model APIs to dedicated deployments
+
+### Special Features
+
+#### Function Calling & Tool Use
+All Baseten models support structured outputs, function calling, and tool use as part of the Baseten Inference Stack, making them ideal for agentic applications.
+
+#### Reasoning Capabilities
+DeepSeek models offer enhanced reasoning with step-by-step thought processes, while maintaining production-ready performance.
+
+#### Long Context Support
+- **Up to 1 million tokens** for Llama 4 models (Maverick and Scout)
+- **262K tokens** for Qwen3 models
+- **163K tokens** for DeepSeek models
+- **Perfect for code repositories** and complex multi-turn conversations
+
+#### Quantization Optimizations
+Models are deployed with advanced quantization techniques (fp4, fp8, fp16) for optimal performance while maintaining quality.
+
+### Migration from Other Providers
+
+Baseten's OpenAI compatibility makes migration straightforward:
+
+**From OpenAI:**
+- Swap `api.openai.com` with `inference.baseten.co/v1`
+- Keep existing request/response formats
+- Benefit from significant cost savings
+
+**From Other Providers:**
+- Use standard OpenAI SDK format
+- Maintain existing prompting strategies
+- Access to newer open-source models
+
+### Tips and Notes
+
+-   **Model Selection:** Choose models based on your specific use case - reasoning models for complex tasks, coding models for development work, and flagship models for general applications.
+-   **Cost Optimization:** Baseten offers some of the most competitive pricing in the market, especially for open-source models.
+-   **Context Windows:** Take advantage of large context windows (up to 1M tokens) for including substantial codebases and documentation.
+-   **Enterprise Ready:** Baseten is designed for production use with enterprise-grade security, compliance, and reliability.
+-   **Dynamic Model Updates:** Cline automatically fetches the latest model list from Baseten, ensuring access to new models as they're released.
+-   **Multi-Cloud Capacity Management (MCM):** Baseten's multi-cloud infrastructure ensures high availability and low latency globally.
+-   **Support:** Baseten provides dedicated support for production deployments and can work with you on dedicated resources as you scale.
+
+### Pricing Information
+
+Current pricing is highly competitive and transparent. For the most up-to-date pricing, visit the [Baseten Model APIs page](https://www.baseten.co/products/model-apis/). Prices typically range from $0.10-$6.00 per million tokens, making Baseten significantly more cost-effective than many closed-model alternatives while providing access to state-of-the-art open-source models.

src/core/api/providers/baseten.ts

@@ -146,20 +146,15 @@ export class BasetenHandler implements ApiHandler {
 		}
 	}
 
-	/**
-	 * Checks if the current model supports vision/images
-	 */
-	supportsImages(): boolean {
-		const model = this.getModel()
-		return model.info.supportsImages === true
-	}
-
 	/**
 	 * Checks if the current model supports tools
 	 */
 	supportsTools(): boolean {
-		const _model = this.getModel()
-		// Baseten models support tools via OpenAI-compatible API
-		return true
+		const model = this.getModel()
+		const modelInfo = model.info as any
+
+		// Use dynamic API data when available, fallback to true since all current Baseten models support tools
+		// (as of 2025-09-16 - could change if Baseten add non-tool models in future, currently no plans to do so)
+		return modelInfo.supportedFeatures ? modelInfo.supportedFeatures.includes("tools") : true
 	}
 }

src/core/controller/models/refreshBasetenModels.ts

@@ -2,6 +2,7 @@ import { GlobalFileNames } from "@core/storage/disk"
 import { EmptyRequest } from "@shared/proto/cline/common"
 import { OpenRouterCompatibleModelInfo, OpenRouterModelInfo } from "@shared/proto/cline/models"
 import { fileExistsAtPath } from "@utils/fs"
+import { parsePrice } from "@utils/model-utils"
 import axios from "axios"
 import fs from "fs/promises"
 import path from "path"
@@ -24,11 +25,11 @@ export async function refreshBasetenModels(
 	// Get the Baseten API key from the controller's state
 	const basetenApiKey = controller.stateManager.getSecretKey("basetenApiKey")
 
-	const models: Record<string, Partial<OpenRouterModelInfo>> = {}
+	const models: Record<string, Partial<OpenRouterModelInfo> & { supportedFeatures?: string[] }> = {}
 	try {
 		if (!basetenApiKey) {
 			console.log("No Baseten API key found, using static models as fallback")
-			// Don't throw an error, just use static models
+			// Don't throw an error, just use static models, althought this might be slightly out of date
 			for (const [modelId, modelInfo] of Object.entries(basetenModels)) {
 				models[modelId] = {
 					maxTokens: modelInfo.maxTokens,
@@ -69,25 +70,20 @@ export async function refreshBasetenModels(
 						continue
 					}
 
-					// Only include models that are listed in the static basetenModels
-					if (!(rawModel.id in basetenModels)) {
-						console.log(`Skipping model ${rawModel.id} - not in static basetenModels list`)
-						continue
-					}
-
 					// Check if we have static pricing information for this model
 					const staticModelInfo = basetenModels[rawModel.id as keyof typeof basetenModels]
 
-					const modelInfo: Partial<OpenRouterModelInfo> = {
-						maxTokens: staticModelInfo?.maxTokens || 8192,
-						contextWindow: staticModelInfo?.contextWindow || 8192,
-						supportsImages: staticModelInfo?.supportsImages || false,
+					const modelInfo: Partial<OpenRouterModelInfo> & { supportedFeatures?: string[] } = {
+						maxTokens: rawModel.max_completion_tokens || staticModelInfo?.maxTokens,
+						contextWindow: rawModel.context_length || staticModelInfo?.contextWindow,
+						supportsImages: false, // Baseten model APIs does not support image input
 						supportsPromptCache: staticModelInfo?.supportsPromptCache || false,
-						inputPrice: staticModelInfo?.inputPrice || 0,
-						outputPrice: staticModelInfo?.outputPrice || 0,
+						inputPrice: parsePrice(rawModel.pricing?.prompt) || staticModelInfo?.inputPrice || 0,
+						outputPrice: parsePrice(rawModel.pricing?.completion) || staticModelInfo?.outputPrice || 0,
 						cacheWritesPrice: staticModelInfo?.cacheWritesPrice || 0,
 						cacheReadsPrice: staticModelInfo?.cacheReadsPrice || 0,
 						description: generateModelDescription(rawModel, staticModelInfo),
+						supportedFeatures: rawModel.supported_features || [],
 					}
 
 					models[rawModel.id] = modelInfo
@@ -125,11 +121,9 @@ export async function refreshBasetenModels(
 		const cachedModels = await readBasetenModels(controller)
 		if (cachedModels && Object.keys(cachedModels).length > 0) {
 			console.log("Using cached Baseten models")
-			// Filter cached models to only include those in static basetenModels
+			// Use all cached models (no filtering)
 			for (const [modelId, modelInfo] of Object.entries(cachedModels)) {
-				if (modelId in basetenModels) {
-					models[modelId] = modelInfo
-				}
+				models[modelId] = modelInfo
 			}
 		} else {
 			// Fall back to static models from shared/api.ts
@@ -165,6 +159,7 @@ export async function refreshBasetenModels(
 			cacheReadsPrice: model.cacheReadsPrice ?? 0,
 			description: model.description ?? "",
 			tiers: model.tiers ?? [],
+			// Note: supportedFeatures is preserved as custom property but not part of OpenRouterModelInfo proto
 		}
 	}
 
@@ -219,14 +214,47 @@ function isValidChatModel(rawModel: any): boolean {
  * Generates a descriptive name for the model
  */
 function generateModelDescription(rawModel: any, staticModelInfo?: any): string {
-	// Use static description if available
+	// Use static description if available and preferred
 	if (staticModelInfo?.description) {
 		return staticModelInfo.description
 	}
 
-	// Generate description based on model characteristics
-	const modelId = rawModel.id
-	const ownedBy = rawModel.owned_by || "Unknown"
+	// Use API description if available
+	if (rawModel.description) {
+		const contextWindow = rawModel.context_length
+		const quantization = rawModel.quantization
+		const features = rawModel.supported_features || []
 
-	return `${ownedBy} model: ${modelId}`
+		let description = rawModel.description
+
+		// Add technical details if available
+		const technicalDetails = []
+		if (contextWindow) {
+			technicalDetails.push(`${contextWindow.toLocaleString()} token context`)
+		}
+		if (quantization) {
+			technicalDetails.push(`${quantization} precision`)
+		}
+		if (features.length > 0) {
+			const featureList = features.join(", ")
+			technicalDetails.push(`supports ${featureList}`)
+		}
+
+		if (technicalDetails.length > 0) {
+			description += ` (${technicalDetails.join(", ")})`
+		}
+
+		return description
+	}
+
+	// Fallback: use name or model ID
+	const modelName = rawModel.name || rawModel.id
+	const contextWindow = rawModel.context_length
+	const ownedBy = rawModel.owned_by || "Baseten"
+
+	if (contextWindow) {
+		return `${ownedBy} ${modelName} with ${contextWindow.toLocaleString()} token context window`
+	}
+
+	return `${ownedBy} model: ${modelName}`
 }

src/shared/api.ts

@@ -3271,8 +3271,35 @@ export const huaweiCloudMaasModels = {
 
 // Baseten
 // https://baseten.co/products/model-apis/
+// Extended ModelInfo to include supportedFeatures, like tools
+export interface BasetenModelInfo extends ModelInfo {
+	supportedFeatures?: string[]
+}
+
 export const basetenModels = {
-	"deepseek-ai/DeepSeek-R1-0528": {
+	"Qwen/Qwen3-235B-A22B-Instruct-2507": {
+		maxTokens: 262144,
+		contextWindow: 262144,
+		supportsImages: false,
+		supportsPromptCache: false,
+		inputPrice: 0.22,
+		outputPrice: 0.8,
+		cacheWritesPrice: 0,
+		cacheReadsPrice: 0,
+		description: "Mixture-of-experts LLM with math and reasoning capabilities",
+	},
+	"meta-llama/Llama-4-Maverick-17B-128E-Instruct": {
+		maxTokens: 131072,
+		contextWindow: 1000000,
+		supportsImages: false,
+		supportsPromptCache: false,
+		inputPrice: 0.19,
+		outputPrice: 0.72,
+		cacheWritesPrice: 0,
+		cacheReadsPrice: 0,
+		description: "High-efficiency language processing",
+	},
+	"deepseek-ai/DeepSeek-R1": {
 		maxTokens: 131072,
 		contextWindow: 163840,
 		supportsImages: false,
@@ -3281,8 +3308,7 @@ export const basetenModels = {
 		outputPrice: 5.95,
 		cacheWritesPrice: 0,
 		cacheReadsPrice: 0,
-		description:
-			"DeepSeek R1 0528 - A state-of-the-art 671B-parameter MoE LLM with o1-style reasoning licensed for commercial use.",
+		description: "DeepSeek's first-generation reasoning model",
 	},
 	"deepseek-ai/DeepSeek-V3-0324": {
 		maxTokens: 131072,
@@ -3293,64 +3319,84 @@ export const basetenModels = {
 		outputPrice: 0.77,
 		cacheWritesPrice: 0,
 		cacheReadsPrice: 0,
-		description: "DeepSeek V3 0324 - A state-of-the-art 671B-parameter MoE LLM licensed for commercial use.",
-	},
-	"meta-llama/Llama-4-Maverick-17B-128E-Instruct": {
-		maxTokens: 8192,
-		contextWindow: 131072,
-		supportsImages: false,
-		supportsPromptCache: false,
-		inputPrice: 0.19,
-		outputPrice: 0.72,
-		cacheWritesPrice: 0,
-		cacheReadsPrice: 0,
-		description: "Meta's Llama 4 Maverick - A SOTA mixture-of-experts multi-modal LLM with 400 billion total parameters.",
+		description: "Fast general-purpose LLM with enhanced reasoning capabilities",
 	},
 	"meta-llama/Llama-4-Scout-17B-16E-Instruct": {
-		maxTokens: 8192,
-		contextWindow: 131072,
+		maxTokens: 131072,
+		contextWindow: 1000000,
 		supportsImages: false,
 		supportsPromptCache: false,
 		inputPrice: 0.13,
 		outputPrice: 0.5,
 		cacheWritesPrice: 0,
 		cacheReadsPrice: 0,
-		description: "Meta's Llama 4 Scout - A SOTA mixture-of-experts multi-modal LLM with 109 billion total parameters.",
+		description: "Precise context understanding with efficient reasoning capabilities",
 	},
-	"moonshotai/Kimi-K2-Instruct": {
+	"deepseek-ai/DeepSeek-V3.1": {
 		maxTokens: 131072,
-		contextWindow: 131072,
+		contextWindow: 163840,
+		supportsImages: false,
+		supportsPromptCache: false,
+		inputPrice: 0.5,
+		outputPrice: 1.5,
+		cacheWritesPrice: 0,
+		cacheReadsPrice: 0,
+		description: "Extremely capable general-purpose LLM with hybrid reasoning capabilities and advanced tool calling",
+	},
+	"Qwen/Qwen3-Coder-480B-A35B-Instruct": {
+		maxTokens: 262144,
+		contextWindow: 262144,
+		supportsImages: false,
+		supportsPromptCache: false,
+		inputPrice: 0.38,
+		outputPrice: 1.53,
+		cacheWritesPrice: 0,
+		cacheReadsPrice: 0,
+		description: "Mixture-of-experts LLM with advanced coding and reasoning capabilities",
+	},
+	"openai/gpt-oss-120b": {
+		maxTokens: 128072,
+		contextWindow: 128072,
+		supportsImages: false,
+		supportsPromptCache: false,
+		inputPrice: 0.1,
+		outputPrice: 0.5,
+		cacheWritesPrice: 0,
+		cacheReadsPrice: 0,
+		description: "Extremely capable general-purpose LLM with strong, controllable reasoning capabilities",
+	},
+	"moonshotai/Kimi-K2-Instruct-0905": {
+		maxTokens: 168000,
+		contextWindow: 262000,
 		supportsImages: false,
 		supportsPromptCache: false,
 		inputPrice: 0.6,
 		outputPrice: 2.5,
 		cacheWritesPrice: 0,
 		cacheReadsPrice: 0,
-		description: "Moonshot AI's Kimi K2 - The world's first 1 trillion parameter open source model.",
+		description: "State of the art language model for agentic and coding tasks. Septemeber Update.",
 	},
-	"Qwen/Qwen3-235B-A22B-Instruct-2507": {
-		maxTokens: 163800,
-		contextWindow: 163800,
+	"moonshotai/Kimi-K2-Instruct": {
+		maxTokens: 131000,
+		contextWindow: 131000,
 		supportsImages: false,
 		supportsPromptCache: false,
-		inputPrice: 0.22,
-		outputPrice: 0.8,
+		inputPrice: 0.6,
+		outputPrice: 2.5,
 		cacheWritesPrice: 0,
 		cacheReadsPrice: 0,
-		description:
-			"Qwen3-235B-A22B-Instruct-2507 is a multilingual, instruction-tuned mixture-of-experts language model based on the Qwen3-235B architecture, with 22B active parameters per forward pass.",
+		description: "State of the art language model for agentic and coding tasks",
 	},
-	"Qwen/Qwen3-Coder-480B-A35B-Instruct": {
-		maxTokens: 163800,
-		contextWindow: 163800,
+	"deepseek-ai/DeepSeek-R1-0528": {
+		maxTokens: 131072,
+		contextWindow: 163840,
 		supportsImages: false,
 		supportsPromptCache: false,
-		inputPrice: 1.7,
-		outputPrice: 1.7,
+		inputPrice: 2.55,
+		outputPrice: 5.95,
 		cacheWritesPrice: 0,
 		cacheReadsPrice: 0,
-		description:
-			"Qwen3-Coder-480B-A35B-Instruct is a 480B parameter, instruction-tuned, agentic coding model that excels at function calling, tool use, and long-context reasoning over repositories.",
+		description: "The latest revision of DeepSeek's first-generation reasoning model",
 	},
 } as const satisfies Record<string, ModelInfo>
 export type BasetenModelId = keyof typeof basetenModels

src/utils/model-utils.ts

@@ -58,6 +58,23 @@ export function isLocalModel(providerInfo: ApiProviderInfo): boolean {
 	return localProviders.includes(normalize(providerInfo.providerId))
 }
 
+/**
+ * Parses a price string and converts it from per-token to per-million-tokens
+ * @param priceString The price string to parse (e.g. from API responses)
+ * @returns The price multiplied by 1,000,000 for per-million-token pricing, or 0 if invalid
+ */
+export function parsePrice(priceString: string | undefined): number {
+	if (!priceString || priceString === "" || priceString === "0") {
+		return 0
+	}
+	const parsed = parseFloat(priceString)
+	if (Number.isNaN(parsed)) {
+		return 0
+	}
+	// Convert from per-token to per-million-tokens (multiply by 1,000,000)
+	return parsed * 1_000_000
+}
+
 function normalize(text: string): string {
 	return text.trim().toLowerCase()
 }

webview-ui/src/components/settings/BasetenModelPicker.tsx

@@ -30,12 +30,6 @@ const BasetenModelPicker: React.FC<BasetenModelPickerProps> = ({ isPopup, curren
 	const dropdownListRef = useRef<HTMLDivElement>(null)
 
 	const handleModelChange = (newModelId: string) => {
-		// Only allow selection of models that are in the static basetenModels
-		if (!(newModelId in basetenModels)) {
-			console.warn(`Model ${newModelId} is not in the static basetenModels list`)
-			return
-		}
-
 		// Use dynamic models if available, otherwise fall back to static models
 		const modelInfo = dynamicBasetenModels?.[newModelId] || basetenModels[newModelId as keyof typeof basetenModels]
 
@@ -60,27 +54,13 @@ const BasetenModelPicker: React.FC<BasetenModelPickerProps> = ({ isPopup, curren
 	useMount(() => {
 		ModelsServiceClient.refreshBasetenModels(EmptyRequest.create({}))
 			.then((response) => {
-				// Filter to only include models that are listed in the static basetenModels
-				const filteredModels: Record<string, any> = {}
-
-				// Always include the default model
-				filteredModels[basetenDefaultModelId] = basetenModels[basetenDefaultModelId]
-
-				// Only include models from the API response that exist in static basetenModels
-				for (const [modelId, modelInfo] of Object.entries(response.models)) {
-					if (modelId in basetenModels) {
-						filteredModels[modelId] = modelInfo
-					}
-				}
-
-				setBasetenModels(filteredModels)
+				setBasetenModels({
+					[basetenDefaultModelId]: basetenModels[basetenDefaultModelId],
+					...response.models,
+				})
 			})
 			.catch((err) => {
 				console.error("Failed to refresh Baseten models:", err)
-				// On error, fall back to only static models
-				setBasetenModels({
-					[basetenDefaultModelId]: basetenModels[basetenDefaultModelId],
-				})
 			})
 	})
 
@@ -113,24 +93,8 @@ const BasetenModelPicker: React.FC<BasetenModelPickerProps> = ({ isPopup, curren
 	}, [])
 
 	const allBasetenModels = useMemo(() => {
-		// Only include models that are listed in the static basetenModels
-		const filteredModels: Record<string, any> = {}
-
-		// Start with static models
-		for (const [modelId, modelInfo] of Object.entries(basetenModels)) {
-			filteredModels[modelId] = modelInfo
-		}
-
-		// Override with dynamic models, but only if they exist in static basetenModels
-		if (dynamicBasetenModels) {
-			for (const [modelId, modelInfo] of Object.entries(dynamicBasetenModels)) {
-				if (modelId in basetenModels) {
-					filteredModels[modelId] = modelInfo
-				}
-			}
-		}
-
-		return filteredModels
+		// Merge static models with dynamic models, with dynamic taking precedence
+		return { ...basetenModels, ...(dynamicBasetenModels || {}) }
 	}, [dynamicBasetenModels])
 
 	const modelIds = useMemo(() => {
@@ -163,6 +127,28 @@ const BasetenModelPicker: React.FC<BasetenModelPickerProps> = ({ isPopup, curren
 		return results
 	}, [searchableItems, debouncedSearchTerm, fuse])
 
+	// Safe HTML parser for highlighted search results
+	const parseHighlightedText = React.useCallback((htmlString: string) => {
+		// Split by highlight spans and reconstruct as React elements
+		const parts = htmlString.split(/(<span class="model-item-highlight">.*?<\/span>)/g)
+
+		return parts
+			.map((part) => {
+				if (part.startsWith('<span class="model-item-highlight">')) {
+					// Extract text content from span
+					const text = part.replace(/<span class="model-item-highlight">(.*?)<\/span>/, "$1")
+					return (
+						<span className="model-item-highlight" key={`highlight-${text}`}>
+							{text}
+						</span>
+					)
+				}
+				// Return plain text without wrapping in span
+				return part || null
+			})
+			.filter((part) => part !== null && part !== "")
+	}, [])
+
 	const handleKeyDown = (event: KeyboardEvent<HTMLInputElement>) => {
 		if (!isDropdownVisible) {
 			return
@@ -266,17 +252,17 @@ const BasetenModelPicker: React.FC<BasetenModelPickerProps> = ({ isPopup, curren
 									className={`px-2.5 py-1.5 cursor-pointer break-all whitespace-normal hover:bg-[var(--vscode-list-activeSelectionBackground)] ${
 										index === selectedIndex ? "bg-[var(--vscode-list-activeSelectionBackground)]" : ""
 									}`}
-									dangerouslySetInnerHTML={{
-										__html: item.html,
-									}}
 									key={item.id}
 									onClick={() => {
 										handleModelChange(item.id)
 										setIsDropdownVisible(false)
 									}}
 									onMouseEnter={() => setSelectedIndex(index)}
-									ref={(el: HTMLDivElement | null) => (itemRefs.current[index] = el)}
-								/>
+									ref={(el: HTMLDivElement | null) => {
+										itemRefs.current[index] = el
+									}}>
+									{parseHighlightedText(item.html)}
+								</div>
 							))}
 						</div>
 					)}