What is the context window for GPT-OSS 120B (hosted) with Atlas?

GPT-OSS 120B (hosted) offers a 131,072 token context window on providers such as Groq, Cerebras, Fireworks, DeepInfra, Together AI, and Databricks. On Cloudflare, Baseten, and NVIDIA NIM, the context window is 128,000 tokens. Atlas can leverage this extensive context for tasks like code indexing by AST declarations.

How much does GPT-OSS 120B (hosted) cost per token with Atlas?

The cost for GPT-OSS 120B (hosted) varies significantly by provider. Input tokens range from $0.037 per Mtok on DeepInfra to $0.35 per Mtok on Cerebras and Cloudflare. Output tokens range from $0.17 per Mtok on DeepInfra to $0.75 per Mtok on Cerebras and Cloudflare.

Can Atlas switch between different GPT-OSS 120B (hosted) providers?

Yes, Atlas allows you to switch the active model and provider on the fly. Because GPT-OSS 120B (hosted) uses identical Apache-licensed weights across all hosts, cycling between providers like DeepInfra and Groq changes only the price and latency, not the model's core behavior or capabilities.

What are the output token limits for GPT-OSS 120B (hosted) on different providers?

Output token limits for GPT-OSS 120B (hosted) vary. DeepInfra caps output at 16,384 tokens, and Fireworks at 32,768 tokens. Groq serves it with 65,536 max output. Together AI is notable for allowing max output equal to the full 131,072 token context window.

Is GPT-OSS 120B (hosted) suitable for complex multi-file refactors in Atlas?

GPT-OSS 120B (hosted) is not a frontier coding model. For hard multi-file refactors, it will be outperformed by models like Kimi K2.7 Code or GLM-5.2, regardless of the underlying silicon speed. Atlas users should consider switching to more specialized models for such demanding tasks.

How does Atlas use the context window of GPT-OSS 120B (hosted)?

Atlas leverages the large context window of GPT-OSS 120B (hosted) for various functions, including indexing code by AST declarations using tree-sitter, performing hybrid semantic and keyword code search with Axis, and drafting comprehensive plans in the read-only plan agent. This allows Atlas to maintain a broad understanding of your codebase.

Can Atlas use local embeddings with GPT-OSS 120B (hosted)?

Yes, Atlas can build its code index with local Ollama embeddings, keeping code off third-party servers, even when using a hosted model like GPT-OSS 120B (hosted) for agent reasoning and generation. This provides flexibility for privacy-conscious developers.

Atlas with GPT-OSS 120B (hosted) in 2026

GPT-OSS 120B (hosted) offers a unique opportunity in 2026 for Atlas users to optimize cost or latency, given its identical Apache-licensed weights across all providers. With input prices ranging from $0.037 per Mtok on DeepInfra to $0.35 on Cerebras, developers can select the ideal balance for tasks ranging from code search with Axis to drafting plans in the read-only agent.

What is GPT-OSS 120B (hosted) best for with Atlas?

GPT-OSS 120B (hosted) excels within Atlas for tasks where cost efficiency or raw speed is paramount, especially given its 9.5x input price spread across providers in 2026. This model is ideal for leveraging Atlas's Axis, the hybrid semantic and keyword code search, or for generating initial drafts in the read-only plan agent.

When integrated with Atlas, GPT-OSS 120B (hosted) shines in scenarios requiring extensive code context without the need for frontier-level reasoning on complex, multi-file refactors. Its 131,072 token context window on providers like Groq, Cerebras, Fireworks, DeepInfra, Together AI, and Databricks allows Atlas to index code by AST declarations using tree-sitter and perform comprehensive searches. Developers can use Atlas to switch the active model and provider on the fly, enabling them to choose DeepInfra for the cheapest tokens at $0.037 per Mtok for background tasks, or Groq for its LPU inference stack with 65,536 max output for interactive speed. The model's consistent Apache-licensed weights across all hosts mean that its core capabilities remain identical, making provider choice a direct lever for cost or latency optimization. Atlas's ability to fan out work to subagents can further benefit from this flexibility, allowing different subagents to utilize providers optimized for their specific needs.

Understanding GPT-OSS 120B (hosted) Cost and Context Tradeoffs

The primary tradeoff for GPT-OSS 120B (hosted) in 2026 lies in balancing its impressive 131,072 token context window with the varying output token limits and input costs across providers. While DeepInfra offers the lowest input price at $0.037 per Mtok, it caps output at 16,384 tokens, potentially forcing more round trips.

Developers using Atlas with GPT-OSS 120B (hosted) must carefully consider the provider's output token limits alongside input pricing. For instance, DeepInfra provides the most cost-effective input at $0.037 per Mtok, but its 16,384 token output cap can lead to increased API calls for longer responses, impacting overall task completion time and potentially negating some cost savings. Fireworks also clips output at 32,768 tokens. In contrast, Together AI allows for a full 131,072 token output, matching its maximum context window, which is ideal for Atlas's unified diff generation or for drafting extensive plans. Providers like Groq and Cerebras, while more expensive at $0.15 and $0.35 per Mtok respectively, offer specialized hardware for lower latency, with Groq providing 65,536 max output. The 9.5x input price spread, from $0.037 to $0.35, means that the choice of provider is a more significant cost factor than the model itself, as the underlying GPT-OSS 120B weights are identical. Atlas's ability to switch models and providers on the fly allows for dynamic optimization based on the current task's requirements.

When to choose a different model for Atlas

While GPT-OSS 120B (hosted) offers excellent value and flexibility in 2026, it is not a frontier coding model and will be outperformed by specialized alternatives like Kimi K2.7 Code or GLM-5.2 for complex, multi-file refactoring tasks. Its strengths lie in cost-efficiency and broad context, not current reasoning.

For developers in 2026 tackling hard multi-file refactors or highly intricate code generation challenges within Atlas, GPT-OSS 120B (hosted) may not be the optimal choice. The model's Apache-licensed weights, while offering cost transparency and provider flexibility, do not position it at the forefront of AI coding capabilities compared to models specifically engineered for advanced reasoning. When Atlas needs to perform deep, nuanced code transformations across many files, or when the read-only plan agent requires highly sophisticated strategic thinking, models like Kimi K2.7 Code or GLM-5.2 are documented to provide superior performance. Even with the fastest silicon from Groq or Cerebras, the inherent capabilities of GPT-OSS 120B (hosted) are not designed for the most demanding, frontier-level coding tasks. Atlas's feature to switch the active model and provider on the fly is crucial here, allowing developers to direct transition to a more powerful, albeit potentially more expensive, model when the complexity of the task demands it, ensuring the right tool is always available.

Setup

011: Decide on Optimization Goal: Determine whether your priority is the cheapest tokens or the lowest latency for Atlas with GPT-OSS 120B (hosted).
022: Export API Key: Set the appropriate environment variable, such as DEEPINFRA_API_KEY for cost-optimized tokens, or GROQ_API_KEY or CEREBRAS_API_KEY for lower latency.
033: Compare Provider Offerings: Run `atlas models deepinfra`, `atlas models groq`, `atlas models cerebras`, or `atlas models fireworks-ai` to review the `gpt-oss-120b` rows and compare pricing and context details.
044: Pin Model in `atlas.json`: Configure your `atlas.json` file to pin the desired provider, for example, `"model": "deepinfra/openai/gpt-oss-120b"` for cost, or `"model": "groq/openai/gpt-oss-120b"` if interactive speed is more critical.
055: Add to Atlas Favorites: Use the `/models` dialog within Atlas to add both your preferred cost-optimized and latency-optimized GPT-OSS 120B (hosted) configurations to your favorites.
066: Cycle Between Configurations: Utilize `model.cycle_recent` in Atlas to quickly switch between different GPT-OSS 120B (hosted) provider configurations, changing only the price and latency, not the model's behavior.

Frequently asked questions

What is the context window for GPT-OSS 120B (hosted) with Atlas?: GPT-OSS 120B (hosted) offers a 131,072 token context window on providers such as Groq, Cerebras, Fireworks, DeepInfra, Together AI, and Databricks. On Cloudflare, Baseten, and NVIDIA NIM, the context window is 128,000 tokens. Atlas can leverage this extensive context for tasks like code indexing by AST declarations.
How much does GPT-OSS 120B (hosted) cost per token with Atlas?: The cost for GPT-OSS 120B (hosted) varies significantly by provider. Input tokens range from $0.037 per Mtok on DeepInfra to $0.35 per Mtok on Cerebras and Cloudflare. Output tokens range from $0.17 per Mtok on DeepInfra to $0.75 per Mtok on Cerebras and Cloudflare.
Can Atlas switch between different GPT-OSS 120B (hosted) providers?: Yes, Atlas allows you to switch the active model and provider on the fly. Because GPT-OSS 120B (hosted) uses identical Apache-licensed weights across all hosts, cycling between providers like DeepInfra and Groq changes only the price and latency, not the model's core behavior or capabilities.
What are the output token limits for GPT-OSS 120B (hosted) on different providers?: Output token limits for GPT-OSS 120B (hosted) vary. DeepInfra caps output at 16,384 tokens, and Fireworks at 32,768 tokens. Groq serves it with 65,536 max output. Together AI is notable for allowing max output equal to the full 131,072 token context window.
Is GPT-OSS 120B (hosted) suitable for complex multi-file refactors in Atlas?: GPT-OSS 120B (hosted) is not a frontier coding model. For hard multi-file refactors, it will be outperformed by models like Kimi K2.7 Code or GLM-5.2, regardless of the underlying silicon speed. Atlas users should consider switching to more specialized models for such demanding tasks.
How does Atlas use the context window of GPT-OSS 120B (hosted)?: Atlas leverages the large context window of GPT-OSS 120B (hosted) for various functions, including indexing code by AST declarations using tree-sitter, performing hybrid semantic and keyword code search with Axis, and drafting comprehensive plans in the read-only plan agent. This allows Atlas to maintain a broad understanding of your codebase.
What is the significance of the 9.5x input price spread for GPT-OSS 120B (hosted)?: The 9.5x input price spread, from $0.037 per Mtok on DeepInfra to $0.35 per Mtok on Cerebras and Cloudflare, is significant because the GPT-OSS 120B (hosted) model uses identical Apache-licensed weights across all providers. This means that provider selection is a much larger cost lever than model selection itself, allowing Atlas users to heavily optimize for budget or performance.
Can Atlas use local embeddings with GPT-OSS 120B (hosted)?: Yes, Atlas can build its code index with local Ollama embeddings, keeping code off third-party servers, even when using a hosted model like GPT-OSS 120B (hosted) for agent reasoning and generation. This provides flexibility for privacy-conscious developers.

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