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Add Baseten provider support
- Add Baseten provider type definitions with 10 models - Implement BasetenHandler extending BaseOpenAiCompatibleProvider - Add Baseten UI component for API key configuration - Add validation logic for Baseten API key - Include Baseten in provider settings schema and constants - Add Baseten translations (basetenApiKey, getBasetenApiKey) - Add basetenApiKey to SECRET_STATE_KEYS for proper storage - Add Baseten provider documentation - Default model: moonshotai/Kimi-K2-Thinking
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docs/providers/baseten.md
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docs/providers/baseten.md
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---
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title: "Baseten"
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description: "Learn how to configure and use Baseten's Model APIs with Roo Code. Access frontier open-source models with enterprise-grade performance, reliability, and competitive pricing."
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---
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Baseten provides on-demand frontier model APIs designed for production applications, not just experimentation. Built on the Baseten Inference Stack, these APIs deliver optimized inference for leading open-source models from OpenAI, DeepSeek, Moonshot AI, and Alibaba Cloud.
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**Website:** [https://www.baseten.co/products/model-apis/](https://www.baseten.co/products/model-apis/)
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## Getting an API Key
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1. **Sign Up/Sign In:** Go to [Baseten](https://www.baseten.co/) and create an account or sign in.
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2. **Navigate to API Keys:** Access your dashboard and go to the API Keys section at [https://app.baseten.co/settings/api_keys](https://app.baseten.co/settings/api_keys).
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3. **Create a Key:** Generate a new API key. Give it a descriptive name (e.g., "Roo Code").
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4. **Copy the Key:** Copy the API key immediately and store it securely.
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## Configuration in Roo Code
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1. **Open Roo Code Settings:** Click the settings icon (⚙️) in the Roo Code panel or open VS Code settings.
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2. **Select Provider:** Choose "Baseten" from the "API Provider" dropdown.
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3. **Enter API Key:** Paste your Baseten API key into the "Baseten API Key" field.
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4. **Select Model:** Choose your desired model from the "Model" dropdown.
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**IMPORTANT: For Kimi K2 Thinking:** To use the `moonshotai/Kimi-K2-Thinking` model, you must enable **Native Tool Call (Experimental)** in Roo Code settings. This setting allows Roo Code to call tools through their native tool processor and is required for this reasoning model to function properly.
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## Supported Models
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Roo Code supports all current models under Baseten Model APIs, including:
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For the most updated pricing, please visit: https://www.baseten.co/products/model-apis/
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- `moonshotai/Kimi-K2-Thinking` (Moonshot AI) - Enhanced reasoning capabilities with step-by-step thought processes (262K context) - $0.60/$2.50 per 1M tokens
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- `zai-org/GLM-4.6` (Z AI) - Frontier open model with advanced agentic, reasoning and coding capabilities by Z AI (200k context) $0.60/$2.20 per 1M tokens
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- `moonshotai/Kimi-K2-Instruct-0905` (Moonshot AI) - September update with enhanced capabilities (262K context) - $0.60/$2.50 per 1M tokens
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- `openai/gpt-oss-120b` (OpenAI) - 120B MoE with strong reasoning capabilities (128K context) - $0.10/$0.50 per 1M tokens
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- `Qwen/Qwen3-Coder-480B-A35B-Instruct` - Advanced coding and reasoning (262K context) - $0.38/$1.53 per 1M tokens
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- `Qwen/Qwen3-235B-A22B-Instruct-2507` - Math and reasoning expert (262K context) - $0.22/$0.80 per 1M tokens
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- `deepseek-ai/DeepSeek-R1` - DeepSeek's first-generation reasoning model (163K context) - $2.55/$5.95 per 1M tokens
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- `deepseek-ai/DeepSeek-R1-0528` - Latest revision of DeepSeek's reasoning model (163K context) - $2.55/$5.95 per 1M tokens
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- `deepseek-ai/DeepSeek-V3.1` - Hybrid reasoning with advanced tool calling (163K context) - $0.50/$1.50 per 1M tokens
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- `deepseek-ai/DeepSeek-V3-0324` - Fast general-purpose with enhanced reasoning (163K context) - $0.77/$0.77 per 1M tokens
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## Production-First Architecture
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Baseten's Model APIs are built for production environments with several key advantages:
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### Enterprise-Grade Reliability
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- **Four nines of uptime** (99.99%) through active-active redundancy
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- **Cloud-agnostic, multi-cluster autoscaling** for consistent availability
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- **SOC 2 Type II certified** and **HIPAA compliant** for security requirements
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### Optimized Performance
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- **Pre-optimized models** shipped with the Baseten Inference Stack
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- **Latest-generation GPUs** with multi-cloud infrastructure
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- **Ultra-fast inference** optimized from the bottom up for production workloads
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### Cost Efficiency
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- **5-10x less expensive** than closed alternatives
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- **Optimized multi-cloud infrastructure** for efficient resource utilization
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- **Transparent pricing** with no hidden costs or rate limit surprises
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### Developer Experience
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- **OpenAI compatible API** - migrate by swapping a single URL
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- **Drop-in replacement** for closed models with comprehensive observability and analytics
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- **Seamless scaling** from Model APIs to dedicated deployments
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## Special Features
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### Function Calling & Tool Use
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All Baseten models support structured outputs, function calling, and tool use as part of the Baseten Inference Stack, making them ideal for agentic applications and coding workflows.
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## Tips and Notes
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- **Static Model List:** Roo Code uses a curated list of Baseten models. The default model is `moonshotai/Kimi-K2-Thinking`.
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- **Multi-Cloud Capacity Management (MCM):** Baseten's multi-cloud infrastructure ensures high availability and low latency globally.
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- **Support:** Baseten provides dedicated support for production deployments and can work with you on dedicated resources as you scale.
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## Pricing Information
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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.
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@ -124,4 +124,4 @@ export const basetenModels = {
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export type BasetenModelId = keyof typeof basetenModels
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export const basetenDefaultModelId = "zai-org/GLM-4.6" satisfies BasetenModelId
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export const basetenDefaultModelId = "moonshotai/Kimi-K2-Thinking" satisfies BasetenModelId
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