Baseten offers Atlas users in 2026 a dedicated-deployment host for open-weights models, distinguished by its generous output limits that match the full context window, such as 262,000 tokens for Kimi K2.7 Code. This makes it ideal for long agentic coding sessions where extensive output is crucial. Pricing starts as low as $0.10/$0.50 per Mtok for GPT OSS 120B, providing a cost-effective option for many tasks within Atlas.
What is Baseten best for with Atlas?
Baseten excels within Atlas for tasks requiring extensive model output, as its dedicated deployments ensure the max output equals the full context window. For instance, Kimi K2.7 Code can theoretically emit 262,000 tokens, making it perfect for large-scale code generation or refactoring in 2026.
Atlas, the terminal-native AI coding agent, benefits significantly from Baseten's unique offering of dedicated-deployment models. Unlike many providers that cap output, Baseten ensures that models like Kimi K2.6, Kimi K2.5, Kimi K2.7 Code, and DeepSeek V4 Pro all provide a 262,000 token context window with an equally generous 262,000 token maximum output. This capability is critical for Atlas's agentic workflows, especially when drafting a plan in a read-only plan agent or executing complex edits in a build agent. When Atlas needs to generate a substantial amount of code, refactor large files, or process extensive code search results from Axis, the hybrid semantic and keyword code search, Baseten's uncapped output prevents premature truncation. This allows Atlas to complete more comprehensive tasks without needing to break down requests into smaller, less efficient chunks, enhancing the overall developer experience in 2026.
What are the cost and context tradeoffs of Baseten models?
Baseten offers competitive pricing for its open-weights models, with GPT OSS 120B available at $0.10/$0.50 per Mtok, which is roughly a third of the Groq price. However, developers must consider the tradeoff of dedicated capacity, which can introduce cold starts and per-deployment configuration.
When integrating Baseten with Atlas, developers in 2026 will find a clear set of cost and context tradeoffs. On the cost front, Baseten provides highly competitive rates for specific models. GPT OSS 120B, for example, is priced at an attractive $0.10 per Mtok for input and $0.50 per Mtok for output, making it a strong contender for Atlas's "small_model" slot where cost efficiency is paramount. Nemotron Ultra also offers a compelling price point at $0.60/$2.40 per Mtok, undercutting Together AI's listing for the same 550B A55B model on output by 33 percent. The primary advantage in context handling is the 262,000 token context window with matching 262,000 token max output for models like Kimi K2.7 Code, which is superior to providers like GLM-5.1 that cap output at just 16,384 tokens for similar models. The tradeoff, however, stems from Baseten's dedicated-deployment nature. While this enables the generous output limits, it means developers must account for potential cold starts and manage per-deployment configurations, which adds a layer of operational consideration compared to shared, instantly available model pools.
When should I choose a different model provider over Baseten?
Developers should consider alternative model providers if their Atlas workflows in 2026 require proprietary models like Claude, GPT-5, or Gemini, as Baseten's registry currently features only 13 open-weights models. This limitation means Baseten cannot serve as a sole provider for all AI agent needs.
While Baseten offers significant advantages for Atlas, particularly with its generous output limits and competitive pricing on specific open-weights models, there are clear scenarios where a different model provider would be more suitable. Baseten's registry is limited to 13 open-weights models, meaning it does not host proprietary, state-of-the-art models such as Claude, GPT-5, or Gemini. If an Atlas user's core development tasks or specific project requirements in 2026 necessitate the unique capabilities or performance characteristics of these closed-source models, Baseten cannot fulfill that need. In such cases, Atlas's ability to switch the active model and provider on the fly becomes invaluable, allowing developers to direct transition to a provider that offers the required proprietary models. Additionally, if the overhead of managing dedicated capacity, including potential cold starts and per-deployment configuration, outweighs the benefits of uncapped output for a particular workflow, a provider with instantly available, shared model pools might be preferred, even if it means accepting output truncation.
Setup
- 01Export your BASETEN_API_KEY from the Baseten dashboard into your environment. This key authenticates Atlas's requests to Baseten's inference service.
- 02Atlas connects to Baseten's API endpoint at https://inference.baseten.co/v1 through the @ai-sdk/openai-compatible interface, ensuring direct integration.
- 03Run `atlas models baseten` in your terminal to list all 13 resolved models available from Baseten within your Atlas environment.
- 04For long agentic edit sessions where output truncation is a significant risk, pin `"model": "baseten/moonshotai/Kimi-K2.7-Code"` in your `atlas.json` configuration file.
- 05To leverage cost-effective options for smaller tasks, set `"small_model": "baseten/openai/gpt-oss-120b"` in your `atlas.json`, utilizing its $0.10/$0.50 per Mtok pricing.
Frequently asked questions
- What is the maximum output for Baseten models in Atlas?
- For many Baseten models like Kimi K2.7 Code, the maximum output equals the full context window of 262,000 tokens, a key advantage for extensive code generation in Atlas.
- How much does GPT OSS 120B cost on Baseten?
- GPT OSS 120B on Baseten is priced at $0.10 per Mtok for input and $0.50 per Mtok for output, making it a highly cost-effective option for Atlas users.
- Can I use proprietary models like GPT-5 or Claude with Baseten in Atlas?
- No, Baseten's registry in 2026 consists solely of 13 open-weights models and does not include proprietary models like GPT-5, Claude, or Gemini.
- What are the main benefits of Baseten's dedicated deployments for Atlas?
- The main benefit is that max output equals the full context window, preventing output truncation for large tasks. For example, Kimi K2.7 Code can emit 262,000 tokens.
- How do I configure Atlas to use Baseten models?
- You need to export your BASETEN_API_KEY, then you can list models with `atlas models baseten` and pin specific models like `"baseten/moonshotai/Kimi-K2.7-Code"` in your `atlas.json`.
- Does Baseten offer a larger context window than other providers?
- Baseten offers a 262,000 token context window for several models, which is competitive. Its unique strength is matching this with a 262,000 token max output, unlike some providers that cap output significantly lower.
- What is the pricing for Kimi K2.7 Code on Baseten?
- Kimi K2.7 Code on Baseten is priced at $0.95 per Mtok for input and $4.00 per Mtok for output, offering a powerful model for extensive coding tasks.
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