Groq (gateway) runs open models on custom LPU hardware, and its differentiator inside Atlas is throughput, not model selection. Groq serves GPT-OSS, Llama, and Qwen3 at token rates no GPU provider matches, with 131K tokens (131,072) across most models. Pricing is GPT-OSS 120B $0.15 / $0.60; GPT-OSS 20B $0.075 / $0.30; Llama 3.1 8B $0.05 / $0.08 per Mtok. The catch is that Groq serves open models only, so it cannot be your only Atlas provider.
What is Groq best at inside Atlas?
Groq (gateway) is best inside Atlas at making the agent loop feel instant. LPU inference delivers token throughput far beyond GPU serving, and an Atlas turn is a loop: search, read, plan, diff, repeat. Groq serves GPT-OSS, Llama, and Qwen3 at 131,072 tokens of context across most models.
Speed compounds in an agent, and Groq (gateway) is where that shows. Atlas fans out work to subagents that can run in the foreground or in parallel background sessions, and every one of those subagents is a full generation. Atlas searches code with hybrid semantic and keyword retrieval fused by reciprocal rank fusion, then the model reads, then Atlas computes a unified diff for every file edit and surfaces it for approval before writing. On a slow provider each hop adds seconds of dead time. On Groq's LPU hardware, token throughput far beyond GPU serving is what makes the loop feel instant rather than merely fast.
How much does Groq cost per million tokens?
Groq (gateway) prices GPT-OSS 120B at $0.15 / $0.60, GPT-OSS 20B at $0.075 / $0.30, and Llama 3.1 8B at $0.05 / $0.08 per Mtok. GPT-OSS 120B at $0.15 / $0.60 is cheaper than most providers charge for models a fraction of its size.
The pricing on Groq (gateway) is unusual because speed normally carries a premium. GPT-OSS 120B at $0.15 / $0.60 per Mtok is the headline: a 120B-class model priced below what many providers charge for far smaller ones. GPT-OSS 20B at $0.075 / $0.30 halves that again for lighter work. And Llama 3.1 8B at $0.05 / $0.08 per Mtok is effectively free for Atlas's `small_model` slot, the one that handles titles and summaries across a long session. Set "small_model": "groq/llama-3.1-8b-instant" in atlas.json and that half of the bill essentially disappears while staying fast.
How do you use Groq as Atlas's cheap fast slot?
Set "small_model": "groq/llama-3.1-8b-instant" in atlas.json. Llama 3.1 8B on Groq (gateway) costs $0.05 / $0.08 per Mtok, effectively free for Atlas's small_model slot, and LPU throughput keeps titles and summaries from stalling the TUI while the build agent works.
Atlas splits work between a build agent and a cheaper auxiliary model, and Groq (gateway) is close to ideal for the auxiliary half. Llama 3.1 8B at $0.05 / $0.08 per Mtok is both the cheapest and one of the fastest things Atlas can call, so session titles, summaries, and short classification calls return before you notice them. Then use `/models` to switch the build agent to groq/openai/gpt-oss-120b when you want a fast reasoning pass. That combination, $0.05 / $0.08 for the small slot and $0.15 / $0.60 for the build slot, is one of the cheapest fully hosted Atlas configurations available in 2026.
What are the limits of running Atlas on Groq?
Groq (gateway) serves open models only: no Claude, no GPT-5, no Gemini, so Groq cannot be your only Atlas provider. Groq also has a 131,072 token context ceiling across the catalog, well short of the 1M windows now common in 2026.
Two hard limits on Groq (gateway), and both are worth knowing before you commit. The catalog is open models, GPT-OSS, Llama, and Qwen3, which means the frontier proprietary models are simply not reachable through Groq. If a refactor needs the strongest available reasoning, Groq cannot supply it. The second limit is the 131,072 token ceiling across the catalog. On a large Atlas session where hybrid retrieval pulls in many AST declarations and several unified diffs accumulate, 131,072 tokens fills faster than you expect, and providers with 1M windows do not have that problem. Keep a second provider registered.
When should you pick a different provider instead of Groq?
Pick a different provider than Groq (gateway) when a task needs Claude, GPT-5, or Gemini, or when an Atlas session needs more than 131,072 tokens of context. Groq's LPU speed and $0.15 / $0.60 GPT-OSS 120B pricing do not compensate for a model that is not in the catalog.
Groq (gateway) works best as one provider among two or three inside Atlas. Because Atlas lets you switch the active model and provider on the fly with favorites and recents, the sensible configuration is Groq for the fast lane, groq/llama-3.1-8b-instant as small_model and groq/openai/gpt-oss-120b for quick reasoning passes, plus a frontier provider starred in `/models` for the turns that need it. Run `atlas models groq` to see the current catalog before assuming a model is there. When the window matters more than the speed, a 1M-context provider is the right call and Groq is not.
Setup
- 01Export `GROQ_API_KEY=gsk_...`. Atlas loads it through `@ai-sdk/groq`.
- 02List the catalog: `atlas models groq`.
- 03Set the cheap slot for speed: "small_model": "groq/llama-3.1-8b-instant" in atlas.json.
- 04Use `/models` to switch the build agent to groq/openai/gpt-oss-120b when you want a fast reasoning pass.
Frequently asked questions
- how to set up groq with atlas
- Export `GROQ_API_KEY=gsk_...` and Atlas loads it through `@ai-sdk/groq`. Run `atlas models groq` to list the catalog, then select a model from `/models` in the Atlas TUI.
- how much does groq cost per million tokens
- On Groq, GPT-OSS 120B is $0.15 / $0.60, GPT-OSS 20B is $0.075 / $0.30, and Llama 3.1 8B is $0.05 / $0.08 per Mtok. The Llama 3.1 8B rate is effectively free for Atlas's small_model slot.
- what is groq's context window
- Groq serves 131K tokens (131,072) across most models. That ceiling applies across the catalog, which is well short of the 1M context windows now common with other providers in 2026.
- can i use claude or gpt-5 through groq
- No. Groq serves open models only: no Claude, no GPT-5, no Gemini. Groq's catalog covers GPT-OSS, Llama, and Qwen3, so it cannot be your only Atlas provider if you need a frontier proprietary model.
- why is groq faster than other model providers
- Groq runs open models on custom LPU hardware rather than GPUs. LPU inference delivers token throughput far beyond GPU serving, which is what makes an Atlas agent loop feel instant rather than merely fast.
- what is the cheapest way to run atlas on groq
- Set "small_model": "groq/llama-3.1-8b-instant" at $0.05 / $0.08 per Mtok for titles and summaries, and use groq/openai/gpt-oss-120b at $0.15 / $0.60 per Mtok for the build agent.
- is groq good for an ai coding agent
- Groq is a strong fast lane for Atlas because LPU throughput compresses the search, read, plan, diff loop. Its limits are the open-models-only catalog and the 131,072 token context ceiling, so pair it with a second provider.
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