DeepSeek R1 (0528) is the model that proved reinforcement learning alone could produce chain of thought, and 0528 is the refreshed checkpoint that gateways still serve. DeepInfra hosts it at $0.50 per Mtok input and $2.15 per Mtok output with a 160K tokens (DeepInfra) window. Inside Atlas it is the classic choice when you want an open reasoning model with a fully visible trace, and its cost is verbosity.
Why does DeepSeek R1 (0528) show its reasoning trace?
DeepSeek R1 (0528) emits an explicit, readable reasoning trace rather than a hidden summary, which makes it genuinely debuggable when an Atlas agent takes a wrong turn mid-plan. R1 is the model that proved reinforcement learning alone could produce chain of thought.
Most hosted reasoning models return a summarized or redacted account of their thinking. DeepSeek R1 (0528) emits an explicit, readable reasoning trace rather than a hidden summary, and for an agent operator that is a diagnostic tool rather than a curiosity. When an Atlas run goes sideways, the question is always where the model's model of the codebase diverged from reality. With R1 you can read the answer. Atlas drafts a plan in a read-only plan agent and asks before switching to a build agent, so the trace arrives before anything is written, at the exact moment you can still act on it. Combine that with the fact that Atlas computes a unified diff for every file edit and surfaces it for approval before writing, and you have two independent inspection points before code changes.
How much does DeepSeek R1 (0528) cost on DeepInfra?
DeepSeek R1 (0528) costs $0.50 per Mtok input and $2.15 per Mtok output on DeepInfra hosting in 2026, with a 160K tokens window. That $2.15/Mtok output is over 5x DeepSeek Chat's $0.28, and R1 is verbose by construction, so agent loops get expensive fast.
DeepInfra hosts DeepSeek R1 (0528) at $0.50 in and $2.15 out per Mtok. The output number is the one that governs your bill, because a reasoning model's chain of thought is output. $2.15/Mtok output is over 5x DeepSeek Chat's, and R1 is verbose by construction, so agent loops get expensive fast. That is not a reason to avoid R1, it is a reason to scope it. Set `"small_model"` to something cheap so R1's output rate is not billed for chat titles. Atlas fans out work to subagents that can run in the foreground or in parallel background sessions, and every one of those subagents billing at R1's output rate is how a session gets expensive without anyone noticing.
Is DeepSeek R1 (0528) good at tool calling in an agent loop?
DeepSeek R1 (0528) has weaker tool-calling discipline than the models trained for agentic use after it, specifically V3.1, V3.2, and K2 Thinking, so expect more malformed tool calls in long runs. R1 predates the agentic training that those later checkpoints received.
Tool calling is the weak spot of DeepSeek R1 (0528). Its tool-calling discipline is weaker than the models trained for agentic use after it, including V3.1, V3.2, and K2 Thinking, so expect more malformed tool calls in long runs. R1 was trained to reason, not to operate an agent harness for fifty consecutive turns. The structural answer is to use R1 where its strength lies and hand off before its weakness shows. Use it as Atlas's plan agent and hand the resulting plan to a cheaper build model, which is where its price-to-value ratio is best. The plan agent is read-only, which means malformed tool calls there cost you a retry, not a corrupted working tree. Atlas lets you switch the active model and provider on the fly with favorites and recents, so the handoff is a keystroke.
Should you run DeepSeek R1 (0528) instead of api.deepseek.com?
DeepSeek R1 (0528) on DeepInfra is hosted in the US, so you get R1 without routing prompts through api.deepseek.com. For teams with a data-residency constraint in 2026, that hosting choice is often the deciding factor rather than the $0.50 per Mtok input price.
Where DeepSeek R1 (0528) runs matters as much as what it costs. DeepInfra hosts it in the US, so you get R1 without routing prompts through api.deepseek.com, which is the practical answer for teams that want open reasoning weights but cannot send source code out of their jurisdiction. The model itself is 671B total and 37B activated MoE under an MIT license, the release that made open reasoning weights a real category, which means the checkpoint is also self-hostable if the gateway is not enough. Atlas can build its code index with local Ollama embeddings, keeping code off third-party servers, so the indexing half of the pipeline can stay entirely local regardless of which inference endpoint you point at.
How do you set up Atlas with DeepSeek R1 (0528)?
To run Atlas on DeepSeek R1 (0528) in 2026, export DEEPINFRA_API_KEY, find the id `deepseek-ai/DeepSeek-R1-0528` with `atlas models deepinfra`, and put `"model": "deepinfra/deepseek-ai/DeepSeek-R1-0528"` in atlas.json alongside a cheap small model.
Export DEEPINFRA_API_KEY or run `atlas login` and pick DeepInfra. Find the id with `atlas models deepinfra`; it is `deepseek-ai/DeepSeek-R1-0528`. Put `"model": "deepinfra/deepseek-ai/DeepSeek-R1-0528"` in atlas.json, and set `"small_model"` to something cheap so R1's output rate is not billed for chat titles. That second half is not optional advice at $2.15 per Mtok output. Then structure the session correctly: use it as Atlas's plan agent and hand the resulting plan to a cheaper build model, which is where its price-to-value ratio is best. Atlas snapshots file changes as git patches so edits can be diffed and rolled back, which is a useful backstop given R1's weaker tool-calling discipline in long runs.
Setup
- 01Export DEEPINFRA_API_KEY or run `atlas login` and pick DeepInfra.
- 02Find the id with `atlas models deepinfra`; it is `deepseek-ai/DeepSeek-R1-0528`.
- 03Put `"model": "deepinfra/deepseek-ai/DeepSeek-R1-0528"` in atlas.json, and set `"small_model"` to something cheap so R1's output rate is not billed for chat titles.
- 04Use it as Atlas's plan agent and hand the resulting plan to a cheaper build model, which is where its price-to-value ratio is best.
- 05Watch for malformed tool calls in long runs, since R1's tool-calling discipline is weaker than V3.1, V3.2, and K2 Thinking.
Frequently asked questions
- How much does DeepSeek R1 0528 cost per million tokens?
- DeepInfra hosts DeepSeek R1 (0528) at $0.50 per Mtok input and $2.15 per Mtok output. That output rate is over 5x DeepSeek Chat's $0.28 per Mtok.
- Does DeepSeek R1 show its chain of thought?
- Yes. DeepSeek R1 (0528) emits an explicit, readable reasoning trace rather than a hidden summary, which makes it genuinely debuggable when an Atlas agent takes a wrong turn mid-plan.
- How do I run Atlas on DeepSeek R1 0528?
- Export DEEPINFRA_API_KEY or run `atlas login` and pick DeepInfra, find `deepseek-ai/DeepSeek-R1-0528` with `atlas models deepinfra`, then set `"model": "deepinfra/deepseek-ai/DeepSeek-R1-0528"` in atlas.json.
- Why is DeepSeek R1 making malformed tool calls?
- DeepSeek R1 (0528) has weaker tool-calling discipline than the models trained for agentic use after it, namely V3.1, V3.2, and K2 Thinking. Expect more malformed tool calls in long runs.
- Is DeepSeek R1 hosted outside China?
- On DeepInfra, yes. DeepSeek R1 (0528) is hosted in the US on DeepInfra, so you get R1 without routing prompts through api.deepseek.com.
- What is the best way to use DeepSeek R1 in a coding agent?
- Use DeepSeek R1 (0528) as Atlas's plan agent and hand the resulting plan to a cheaper build model. That split is where its price-to-value ratio is best, given $2.15 per Mtok output.
- What is DeepSeek R1 0528's context window?
- DeepSeek R1 (0528) has a 160K tokens window on DeepInfra. The window is comfortable; the constraint is cost, since R1 is verbose by construction.
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