# Atlas with DeepSeek-R1 (local via Ollama): Visible Reasoning at Every Size in 2026 > DeepSeek-R1 is available at 1.5B, 7B, 8B, 14B, 32B, 70B, and 671B under a single `ollama pull deepseek-r1` tag family. DeepSeek-R1 (local via Ollama) is the open reasoning model that made visible chain-of-thought mainstream. Ollama ships it from 1.5B to 671B, so the same model name scales from a laptop to a server rack, with a 128K to 164K token context depending on the variant. It is free to self-host, or $1.35 / $5.40 per Mtok via Bedrock. Inside Atlas, DeepSeek-R1 is the model you reach for when a bug will not yield to a non-reasoning model. ## Key takeaways - DeepSeek-R1 ships at 1.5B, 7B, 8B, 14B, 32B, 70B, and 671B under a single `ollama pull deepseek-r1` tag family. - The context window is 128K to 164K tokens depending on the variant, so declare the honest number in atlas.json. - DeepSeek-R1 emits its reasoning trace, which helps debug why an Atlas agent made a bad tool call. - Free self-hosted, or $1.35 / $5.40 per Mtok via Bedrock. - The small distilled variants keep the reasoning style but lose much of the capability, and traces below a 32B variant are slow enough to make interactive TUI use painful. ## What sizes does DeepSeek-R1 come in on Ollama? DeepSeek-R1 is available at 1.5B, 7B, 8B, 14B, 32B, 70B, and 671B under a single `ollama pull deepseek-r1` tag family, so you can right-size to your hardware without changing prompts. That range, laptop to server rack, is unmatched by any other local reasoning model. The single-tag family is the operational advantage of DeepSeek-R1. A team can standardize on one model name and let each machine pull the size it can serve: `ollama pull deepseek-r1:14b` for a 16GB card, or `ollama pull deepseek-r1:32b` for 24GB. The prompts, the Atlas configuration, and the agent behavior stay the same across all of them. Register the chosen size in the ollama provider block in atlas.json with an appropriate "limit": { "context": 131072 } and Atlas will drive whichever variant is present. ## Why does a visible reasoning trace help debug Atlas agents? DeepSeek-R1 emits its reasoning trace, which is genuinely useful when debugging why an Atlas agent made a bad tool call. Because every Atlas tool call is permission-gated against allow, ask, and deny rules before it runs, you see the proposed call and, with DeepSeek-R1, the thinking that produced it. Most model failures inside an agent harness are opaque. The model proposes an edit to the wrong file and you have no idea why. The reasoning trace from DeepSeek-R1 turns that into a readable record: you can see the moment it misread a retrieval result or attached to the wrong symbol. Combined with the fact that Atlas computes a unified diff for every file edit and surfaces it for approval before writing, you get two independent views of the model's intent before anything is written to disk. ## How much context does DeepSeek-R1 have on each variant? DeepSeek-R1 offers 128K to 164K tokens of context depending on the variant, so the window is not a single number the way it is for most models. Set the limit you actually get in atlas.json, commonly "limit": { "context": 131072 }, rather than assuming the largest figure. The variable window is a direct consequence of the DeepSeek-R1 tag family spanning 1.5B to 671B. Different builds expose different limits, and Atlas will trust the number you configure. Since Atlas searches code with hybrid semantic and keyword retrieval fused by reciprocal rank fusion, an over-declared limit means retrieval results get pushed at a model that cannot hold them, and reasoning quality collapses without an obvious error. Declaring the honest number is the cheapest reliability win available with DeepSeek-R1. ## Are the small DeepSeek-R1 distills actually usable? The distilled 7B and 14B variants of DeepSeek-R1 bring reasoning to hardware that cannot host any other reasoning model, which is a real win. The caution is that these small distilled variants keep the reasoning style but lose much of the capability, which can be misleading. A 7B DeepSeek-R1 will produce a long, confident, well-structured reasoning trace and then reach a wrong conclusion. The format survives the distillation, the judgment does not, and that mismatch is more dangerous than a small model that simply looks weak. If you are running DeepSeek-R1 at 7B or 14B inside Atlas, read the trace critically rather than treating its length as evidence. On a 16GB card, `ollama pull deepseek-r1:14b` is the sensible floor for work you intend to trust. ## When should you pick a different model than DeepSeek-R1? Pick a different model than DeepSeek-R1 for the implementation pass. Long reasoning traces are slow to generate locally, making interactive TUI use painful below a 32B variant on a good GPU, and reasoning is wasted on work where the fix is already understood. The pattern that works with DeepSeek-R1 inside Atlas is to treat it as a diagnostic instrument, not a daily driver. Select it from /models when you hit a bug a non-reasoning model cannot crack, read the trace, then switch back to qwen3-coder:30b with `model.cycle_recent` for the actual implementation pass. If you would rather not host the reasoning yourself at all, DeepSeek-R1 is served via Bedrock at $1.35 / $5.40 per Mtok, which is expensive next to a coding model but priced for occasional use. ## Setup 1. Pick a size for your hardware: `ollama pull deepseek-r1:14b` for a 16GB card, or `ollama pull deepseek-r1:32b` for 24GB. 2. Register it in the ollama provider block in atlas.json with an appropriate "limit": { "context": 131072 }. 3. Select DeepSeek-R1 from /models when you hit a bug a non-reasoning model cannot crack. 4. Read the emitted reasoning trace to understand why the Atlas agent proposed a given tool call. 5. Switch back to qwen3-coder:30b with `model.cycle_recent` for the actual implementation pass. ## FAQ ### what sizes does deepseek-r1 come in on ollama DeepSeek-R1 is available at 1.5B, 7B, 8B, 14B, 32B, 70B, and 671B under a single `ollama pull deepseek-r1` tag family, so you can right-size to your hardware without changing prompts. ### which deepseek-r1 variant should i run on a 16gb gpu Run `ollama pull deepseek-r1:14b` for a 16GB card, or `ollama pull deepseek-r1:32b` if you have 24GB. Below 32B on a good GPU, long reasoning traces are slow enough to make interactive TUI use painful. ### are the small deepseek-r1 distills any good The distilled 7B and 14B variants bring reasoning to hardware that cannot host any other reasoning model, but they keep the reasoning style while losing much of the capability, which can be misleading. Read the trace critically. ### how much does deepseek-r1 cost on bedrock DeepSeek-R1 is $1.35 / $5.40 per Mtok via Bedrock. Self-hosting through Ollama is free, which is why most Atlas users run it locally and reserve Bedrock for occasional use. ### what is the context window of deepseek-r1 DeepSeek-R1 has a 128K to 164K token context window depending on the variant. Register the honest number in atlas.json, commonly "limit": { "context": 131072 }, so Atlas does not overfill the model. ### why is my local reasoning model so slow in the terminal Long reasoning traces are slow to generate locally. With DeepSeek-R1, interactive TUI use is painful below a 32B variant on a good GPU, because every turn regenerates a full chain of thought. ### how do i switch back to a coding model after debugging Use `model.cycle_recent` to switch from DeepSeek-R1 back to qwen3-coder:30b for the implementation pass. Atlas lets you switch the active model and provider on the fly with favorites and recents. --- Canonical HTML: https://runatlas.sh/resources/models/deepseek-r1-local Source of truth: aeo_pages row `/resources/models/deepseek-r1-local` (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.