DeepSeek V3 (open weights) is the original 671B Mixture-of-Experts release that made frontier-adjacent coding cheap. DeepSeek's own API no longer serves it, but DeepInfra still hosts the open weights at $0.32 per Mtok input, $0.89 per Mtok output with a 128K tokens (DeepInfra model page) context. Inside Atlas it is a stable, pinnable baseline: a checkpoint that cannot be swapped or deprecated under you.
Why is DeepSeek V3 (open weights) so cheap for a 671B model?
DeepSeek V3 (open weights) has 671B total parameters with only 37B activated per token, an MoE ratio that is why hosted V3 lands near $0.32/Mtok instead of frontier dense-model prices. DeepInfra serves it at $0.32 per Mtok input and $0.89 per Mtok output in 2026.
The pricing on DeepSeek V3 (open weights) is a direct consequence of its architecture. A Mixture-of-Experts model with 671B total parameters activates only 37B per token, so the compute per token is closer to a mid-size dense model than to a 671B one. That ratio is why hosted V3 lands near $0.32/Mtok instead of frontier dense-model prices. For Atlas, where a session streams a lot of retrieved code through the prompt, the input rate is the one you feel. Atlas indexes code by AST declarations using tree-sitter, not blind line windows, so what enters that prompt is dense with real declarations rather than padding, and at $0.32 per Mtok a wide retrieval pass is affordable.
Why pin DeepSeek V3 (open weights) instead of a newer model?
DeepSeek V3 (open weights) is MIT licensed on HuggingFace, so the exact checkpoint you evaluate today cannot be silently swapped or deprecated under you. That reproducibility is the reason V3 survives in 2026 as a stable baseline even though DeepSeek's first-party API no longer exposes it.
Reproducibility is the argument for DeepSeek V3 (open weights). The weights are MIT licensed on HuggingFace, so the exact checkpoint you evaluate today cannot be silently swapped or deprecated under you. Anyone who has watched a hosted model's behavior drift under a fixed model id understands why that matters when you are running an evaluation suite or a regression baseline. DeepSeek's own API no longer serves V3, but DeepInfra still hosts the open weights, which is why it survives as a stable, pinnable baseline. There is a third property worth naming: DeepSeek V3 (open weights) is served from DeepInfra in the US, which sidesteps the China data-residency question that applies to api.deepseek.com.
Is a 128K context window enough for Atlas on DeepSeek V3?
DeepSeek V3 (open weights) has a 128K tokens (DeepInfra model page) context, a fraction of the 1,000,000 tokens on DeepSeek V4, so large-repo work needs tighter file selection. Atlas's retrieval quality carries more weight at 128K than it does at a million.
The 128K window on DeepSeek V3 (open weights) is workable but not generous. Large-repo work needs tighter file selection, and that is where Atlas's retrieval design earns its keep. Atlas searches code with hybrid semantic and keyword retrieval fused by reciprocal rank fusion, and Atlas indexes code by AST declarations using tree-sitter, not blind line windows. Both mean the 128K you have is filled with declarations that actually bear on the task rather than arbitrary line spans around a keyword hit. On a 1,000,000 token model you can afford to be sloppy about what you retrieve. On DeepSeek V3 you cannot, so let Atlas's index do the selection and resist dumping whole directories into the prompt.
When should you pick V3.1 or V3.2 over DeepSeek V3?
DeepSeek V3 (open weights) is superseded on agentic and tool-calling benchmarks by V3.1 and V3.2 at similar cost, so pick it only when you specifically want this frozen checkpoint. For everyday Atlas work in 2026, the newer V3 line checkpoints are the default.
The case against DeepSeek V3 (open weights) is straightforward. V3 is superseded on agentic and tool-calling benchmarks by V3.1 and V3.2 at similar cost, and agentic tool-calling is most of what an Atlas session consists of. V3 predates the agentic tool-calling training in the 3.1/3.2 line, which shows up as a higher rate of awkward or malformed tool calls in long runs. The reason to still choose V3 is deliberate: you want this exact frozen checkpoint, MIT licensed and reproducible, for a benchmark, a compliance review, or a self-hosting plan. Atlas lets you switch the active model and provider on the fly with favorites and recents, so keeping V3 as a pinned comparison point costs you nothing while a newer model does the daily work.
How do you run Atlas on DeepSeek V3 through DeepInfra?
To run Atlas on DeepSeek V3 (open weights) in 2026, create a DeepInfra key and export DEEPINFRA_API_KEY, run `atlas models deepinfra` and look for `deepseek-ai/DeepSeek-V3`, then set `"model": "deepinfra/deepseek-ai/DeepSeek-V3"` in atlas.json.
DeepSeek V3 (open weights) reaches Atlas through DeepInfra, not through DeepSeek's own API. Create a DeepInfra key and export DEEPINFRA_API_KEY, or run `atlas login` and select DeepInfra. Atlas loads it through `@ai-sdk/deepinfra`. Run `atlas models deepinfra` and look for `deepseek-ai/DeepSeek-V3`, then set `"model": "deepinfra/deepseek-ai/DeepSeek-V3"` in atlas.json. One habit matters more with this model than with newer ones: keep Atlas's unified-diff review on, since V3 predates the agentic tool-calling training in the 3.1/3.2 line and benefits from a human gate on writes. Atlas computes a unified diff for every file edit and surfaces it for approval before writing, and Atlas snapshots file changes as git patches so edits can be diffed and rolled back.
Setup
- 01Create a DeepInfra key and export DEEPINFRA_API_KEY, or run `atlas login` and select DeepInfra. Atlas loads it through `@ai-sdk/deepinfra`.
- 02Run `atlas models deepinfra` and look for `deepseek-ai/DeepSeek-V3`.
- 03Set `"model": "deepinfra/deepseek-ai/DeepSeek-V3"` in atlas.json.
- 04Keep Atlas's unified-diff review on, since V3 predates the agentic tool-calling training in the 3.1/3.2 line and benefits from a human gate on writes.
- 05Tighten file selection for large repos, because the context window is 128K tokens rather than the 1,000,000 on DeepSeek V4.
Frequently asked questions
- How much does DeepSeek V3 cost on DeepInfra?
- DeepInfra hosts DeepSeek V3 (open weights) at $0.32 per Mtok input and $0.89 per Mtok output. DeepSeek's first-party API no longer exposes V3.
- Can I still use DeepSeek V3 in 2026?
- Yes, through DeepInfra. DeepSeek's own API no longer serves it, but DeepInfra still hosts the open weights, which is why V3 survives as a stable, pinnable baseline.
- How do I set DeepSeek V3 as the model in Atlas?
- Export DEEPINFRA_API_KEY or run `atlas login` and select DeepInfra, find `deepseek-ai/DeepSeek-V3` with `atlas models deepinfra`, then set `"model": "deepinfra/deepseek-ai/DeepSeek-V3"` in atlas.json.
- What is DeepSeek V3's context window?
- DeepSeek V3 (open weights) has a 128K tokens context per the DeepInfra model page. That is a fraction of the 1,000,000 tokens on DeepSeek V4, so large-repo work needs tighter file selection.
- Is DeepSeek V3 hosted in the US?
- On DeepInfra, yes. DeepSeek V3 (open weights) is served from DeepInfra in the US, which sidesteps the China data-residency question that applies to api.deepseek.com.
- DeepSeek V3 vs V3.1 vs V3.2 for coding agents?
- V3 is superseded on agentic and tool-calling benchmarks by V3.1 and V3.2 at similar cost. Pick V3 only when you specifically want this frozen, MIT-licensed checkpoint.
- Why is a 671B model only $0.32 per million input tokens?
- DeepSeek V3 activates only 37B of its 671B parameters per token. That Mixture-of-Experts ratio is why hosted V3 lands near $0.32/Mtok instead of frontier dense-model prices.
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