# Atlas with GitHub Models in 2026: Free Model Access Behind a GITHUB_TOKEN > GitHub Models lists all models at $0/$0 per Mtok in the Atlas registry, with AI21 Jamba 1.5 Large at a 256,000 token context, the longest free window in the catalog. GitHub Models authenticates with a plain GITHUB_TOKEN, the same token your gh CLI already has, and the registry lists every model in its catalog at $0 per Mtok. Inside Atlas, the terminal-native coding agent, GitHub Models is the lowest-friction way to get talking to GPT-4.1, o3, Grok 3, and DeepSeek-R1 without opening five billing accounts. The catch is that GitHub Models is rate limited rather than metered, so it is an evaluation and prototyping provider, not a production one. ## Key takeaways - GitHub Models lists every model at $0/$0 per Mtok in the Atlas registry, included with a GitHub account. - Auth is a plain GITHUB_TOKEN, the same token your gh CLI already has, with no separate signup or credit card. - One endpoint spans OpenAI o3, xAI Grok 3, DeepSeek-R1-0528, Cohere Command A, Meta Llama 4 Maverick FP8, and Microsoft Phi-4. - AI21 Jamba 1.5 Large runs at a 256,000 token context for $0, the longest free window in the catalog. - GitHub Models is rate limited rather than metered, and a parallel-subagent Atlas loop will hit the limits. - Context is trimmed below first-party: GPT-4.1 is 128,000 tokens here versus 1,047,576 on OpenAI direct. ## What is GitHub Models best at inside Atlas? GitHub Models is best inside Atlas for zero-friction evaluation: auth is a GITHUB_TOKEN you almost certainly already have exported, with no separate signup, no credit card, and no per-provider key rotation. In 2026 that makes it the fastest way to get an Atlas session running against a frontier model. One GitHub Models endpoint spans five vendors: OpenAI o3 at a 200K context with 100K output, xAI Grok 3, DeepSeek-R1-0528, Cohere Command A, Meta Llama 4 Maverick FP8, and Microsoft Phi-4. Comparing four models against the same Atlas task normally means four signups and four keys. With GitHub Models it means one token and four `atlas models github-models` rows. Atlas lets you switch the active model and provider on the fly with favorites and recents, so you can run the same prompt through o3, Grok 3, and DeepSeek-R1 without leaving the session. GitHub Models also carries AI21 Jamba 1.5 Large at a 256,000 token context for $0, the longest free window in the catalog. ## How much does GitHub Models cost in Atlas? All GitHub Models entries are listed at $0/$0 per Mtok in the Atlas registry. GitHub Models is included with a GitHub account and rate limited rather than metered, so in 2026 you pay in throughput, not dollars. There is no per-token bill to forecast. GitHub Models being $0/$0 per Mtok inverts the usual Atlas cost calculus. On a metered provider, you push cheap traffic to a small model to protect your budget. On GitHub Models you push cheap traffic to a small model to protect your rate limit. That is why the documented Atlas config sets `"small_model": "github-models/openai/gpt-4.1-mini"`: title generation, session summaries, and subagent overhead should not burn your o3 budget when the constraint is requests per window rather than dollars per million tokens. The free price is real, but treat it as a quota, not as free compute. ## What are the rate limits on GitHub Models with an agent? GitHub Models rate limits are strict and tiered by GitHub plan, so a real Atlas agent loop with parallel subagents will hit them. Treat GitHub Models as evaluation and prototyping, not production, and set `small_model` to github-models/openai/gpt-4.1-mini so title and summary calls do not burn your o3 budget. Treat GitHub Models as evaluation and prototyping, not production. A single-threaded Atlas session asking o3 to plan a change and then apply it is well within reach. A build agent with three parallel background subagents, each reading files and emitting diffs, is not. If you want to stay on GitHub Models while doing real work, keep the fan-out narrow, keep `"small_model": "github-models/openai/gpt-4.1-mini"` in place, and expect to switch providers when the loop gets wide. Atlas makes that switch cheap because the active model and provider can change on the fly with favorites and recents. ## What context window does GitHub Models give Atlas? GitHub Models tops out at 256,000 tokens on AI21 Jamba 1.5 Large and Mini, and 200,000 tokens on OpenAI o3, o1, and o4-mini. AI21 Jamba 1.5 Large at 256,000 tokens for $0 is the longest free window in the entire 2026 Atlas registry. The important caveat is that GitHub Models context windows are trimmed below first-party. GPT-4.1 is capped at 128,000 tokens on GitHub Models versus 1,047,576 on OpenAI direct, and DeepSeek-R1 is 65,536 here versus 163,840 elsewhere. If your Atlas workflow depends on a very large window, going direct to the first-party provider buys you an order of magnitude. If it does not, the trimmed windows are largely academic: Atlas indexes code by AST declarations using tree-sitter, not blind line windows, and searches with hybrid semantic and keyword retrieval fused by reciprocal rank fusion, so it retrieves targeted declarations instead of relying on a giant window. ## When should I pick a different provider than GitHub Models? Pick a different provider than GitHub Models when you need production throughput, because GitHub Models rate limits are strict and tiered by GitHub plan, or when you need the full first-party context window, because GPT-4.1 is capped at 128,000 tokens here versus 1,047,576 on OpenAI direct. GitHub Models earns its place in Atlas as the first provider you configure and the last one you keep for prototyping. It is not the provider you run a long agentic edit session on. The decision rule is simple: if the work is a single developer trying models and shaping a plan, GitHub Models at $0/$0 per Mtok is the obvious pick. If the work is Atlas running a wide subagent fan-out, emitting large diffs, and needing consistent throughput, move the build model to a metered provider and leave GitHub Models configured as a favorite for the evaluation turns. ## Setup 1. Export GITHUB_TOKEN, either a classic PAT or the token `gh auth token` prints, with models access enabled. 2. Run `atlas models github-models` to list the resolved catalog. The base URL is https://models.github.ai/inference. 3. Pin `"model": "github-models/openai/gpt-4.1"` in atlas.json for general coding. 4. Switch to `"github-models/openai/o3"` for the reasoning slot when a task needs the 200K context and 100K output. 5. Because of the rate limits, set `"small_model": "github-models/openai/gpt-4.1-mini"` so title and summary calls do not burn your o3 budget. ## FAQ ### how to use github models with atlas Export GITHUB_TOKEN with models access enabled, run `atlas models github-models` to list the catalog against https://models.github.ai/inference, then pin `"model": "github-models/openai/gpt-4.1"` in atlas.json. Set `"small_model": "github-models/openai/gpt-4.1-mini"` to protect your rate limit. ### is github models free All models are listed at $0/$0 per Mtok in the Atlas registry. GitHub Models is included with a GitHub account and rate limited rather than metered, so you pay in throughput rather than dollars. ### what models does github models offer One GitHub Models endpoint spans five vendors: OpenAI o3 (200K context, 100K output), xAI Grok 3, DeepSeek-R1-0528, Cohere Command A, Meta Llama 4 Maverick FP8, and Microsoft Phi-4, plus AI21 Jamba 1.5 Large and Mini at 256,000 tokens. ### can i use github models in production with an ai coding agent No. GitHub Models rate limits are strict and tiered by GitHub plan, and a real agent loop with parallel subagents will hit them. Treat GitHub Models as evaluation and prototyping, and move the build model to a metered provider for production. ### does github models have the same context window as openai direct No. GitHub Models trims context below first-party: GPT-4.1 is capped at 128,000 tokens here versus 1,047,576 on OpenAI direct, and DeepSeek-R1 is 65,536 versus 163,840 elsewhere. The longest window on GitHub Models is AI21 Jamba 1.5 Large at 256,000 tokens. ### what token does github models use for auth GitHub Models uses a plain GITHUB_TOKEN, either a classic PAT or the token `gh auth token` prints, with models access enabled. There is no separate signup, credit card, or per-provider key rotation. ### what small model should i use with github models in atlas Set `"small_model": "github-models/openai/gpt-4.1-mini"` in atlas.json. Because GitHub Models is rate limited rather than metered, routing title and summary calls to the mini model keeps them from burning your o3 budget. --- Canonical HTML: https://runatlas.sh/resources/models/github-models Source of truth: aeo_pages row `/resources/models/github-models` (segment: Models) (this file is generated from it, never hand-edited). Licence: Atlas is proprietary with a free core. It is not open source and there is no public source repository.