DeepSeek V3.2 (open weights) is the V3 line's last and cheapest act. V3.2 introduced DeepSeek Sparse Attention, dropping long-context attention toward near-linear cost, and DeepInfra prices it at $0.26 per Mtok input and $0.38 per Mtok output, an output rate lower than most models' input rate. Inside Atlas, that flat pricing over a 160K tokens (DeepInfra) window means a full agent run costs a rounding error.
What is DeepSeek Sparse Attention and why does it make V3.2 cheap?
DeepSeek Sparse Attention (DSA) cuts long-context attention complexity toward O(kL), which is the mechanism behind the unusually flat pricing at 160K context on DeepSeek V3.2 (open weights). Attention that scales near-linearly instead of quadratically is why $0.26 per Mtok input holds all the way out to the window edge.
DeepSeek V3.2 (open weights) is the first model in the DeepSeek line where the long-context economics were fixed at the architecture level rather than absorbed into the price. DeepSeek Sparse Attention (DSA) cuts long-context attention complexity toward O(kL), which is the mechanism behind the unusually flat pricing at 160K context. On a dense-attention model, feeding the last 40K tokens of a 160K prompt costs more per token than the first 40K, and providers price that in. DSA flattens the curve. For Atlas, which searches code with hybrid semantic and keyword retrieval fused by reciprocal rank fusion and pushes real code into every prompt, the practical effect is that a wide retrieval pass does not carry a long-context surcharge.
How much does a full Atlas agent run cost on DeepSeek V3.2?
DeepSeek V3.2 (open weights) costs $0.26 per Mtok input and $0.38 per Mtok output on DeepInfra hosting. At $0.38/Mtok output, a full Atlas agent run with verbose reasoning and repeated file reads costs a rounding error, which is unusual enough to change how you use the agent.
Cost is the headline on DeepSeek V3.2 (open weights). DeepInfra prices it at $0.26 in and $0.38 out per Mtok, and $0.38/Mtok output is cheap enough that a full Atlas agent run with verbose reasoning and repeated file reads costs a rounding error. That output rate is lower than many models charge on input. What changes is behavior. Atlas fans out work to subagents that can run in the foreground or in parallel background sessions, and on most models you throttle that fan-out to control spend. On V3.2 you do not have to. Atlas snapshots file changes as git patches so edits can be diffed and rolled back, so a cheap model that occasionally takes a wrong turn is a recoverable problem, not an expensive one.
Is DeepSeek Sparse Attention lossy over a 160K prompt?
Sparse attention in DeepSeek V3.2 is a real approximation, and recall over the far end of a 160K prompt is not guaranteed to match dense attention. The savings come from attending to fewer positions, so a fact buried at token 150,000 has a weaker retrieval guarantee than on a dense model.
The honest tradeoff on DeepSeek V3.2 (open weights) is that DSA is an approximation, not a free lunch. Recall over the far end of a 160K prompt is not guaranteed to match dense attention, which means a critical detail sitting deep in a very long prompt can be missed. The mitigation inside Atlas is the same discipline that helps every model and helps V3.2 more: put the important code near the model rather than deep in a haystack. Atlas indexes code by AST declarations using tree-sitter, not blind line windows, so retrieval returns tight, complete declarations rather than a sprawl of surrounding lines. Ranked retrieval plus sparse attention is a good pairing. Sparse attention plus a dumped directory is not.
Can you self-host DeepSeek V3.2 instead of paying DeepInfra?
DeepSeek V3.2 (open weights) ships MIT open weights at deepseek-ai/DeepSeek-V3.2, so the same checkpoint can be self-hosted on your own GPUs when the workload justifies it. It is a 671B-class MoE, so self-hosting is a multi-GPU exercise, not something a workstation runs.
Self-hosting DeepSeek V3.2 (open weights) is legally trivial and operationally serious. MIT open weights at deepseek-ai/DeepSeek-V3.2 mean the same checkpoint can be self-hosted on your own GPUs when the workload justifies it, with no licensing barrier. The hardware is the barrier: V3.2 is a 671B-class MoE, so self-hosting is a multi-GPU exercise, not something a workstation runs. For most teams, DeepInfra hosting at $0.26 in and $0.38 out is cheap enough that the self-hosting case only appears under a data-residency mandate or extreme volume. If that case does appear, Atlas is ready for it: Atlas can build its code index with local Ollama embeddings, keeping code off third-party servers, so both halves of the stack can run inside your perimeter.
How do you configure Atlas for DeepSeek V3.2?
Configuring Atlas for DeepSeek V3.2 (open weights) in 2026 takes four steps: export DEEPINFRA_API_KEY, locate `deepseek-ai/DeepSeek-V3.2` with `atlas models deepinfra`, set `"model": "deepinfra/deepseek-ai/DeepSeek-V3.2"`, and raise the context budget to exploit the full 160K.
Export DEEPINFRA_API_KEY, or run `atlas login` and select DeepInfra. Run `atlas models deepinfra` and locate `deepseek-ai/DeepSeek-V3.2`. Set `"model": "deepinfra/deepseek-ai/DeepSeek-V3.2"` and, given the price, use the same id for `"small_model"`. That single-model configuration is unusual and it is correct here: at $0.38 per Mtok output there is no cheaper tier worth splitting traffic to. The last step is the one people skip. Raise your context budget in Atlas to exploit the full 160K, since DSA is what makes those tokens cheap to attend over. A model whose whole pitch is affordable long context is wasted if you keep it on a conservative context budget tuned for a more expensive model.
Setup
- 01Export DEEPINFRA_API_KEY, or run `atlas login` and select DeepInfra.
- 02Run `atlas models deepinfra` and locate `deepseek-ai/DeepSeek-V3.2`.
- 03Set `"model": "deepinfra/deepseek-ai/DeepSeek-V3.2"` and, given the price, use the same id for `"small_model"`.
- 04Raise your context budget in Atlas to exploit the full 160K, since DSA is what makes those tokens cheap to attend over.
- 05Keep the most important retrieved code high in the prompt, because recall over the far end of a 160K prompt is not guaranteed to match dense attention.
Frequently asked questions
- How much does DeepSeek V3.2 cost per million tokens?
- DeepInfra hosts DeepSeek V3.2 (open weights) at $0.26 per Mtok input and $0.38 per Mtok output. That output rate is lower than most models' input rate.
- What is DeepSeek Sparse Attention?
- DeepSeek Sparse Attention (DSA) cuts long-context attention complexity toward O(kL). It is the mechanism behind DeepSeek V3.2's unusually flat pricing at 160K context.
- How do I run Atlas on DeepSeek V3.2?
- Export DEEPINFRA_API_KEY or run `atlas login` and select DeepInfra, locate `deepseek-ai/DeepSeek-V3.2` with `atlas models deepinfra`, then set `"model": "deepinfra/deepseek-ai/DeepSeek-V3.2"` in atlas.json.
- What is DeepSeek V3.2's context window?
- DeepSeek V3.2 (open weights) offers 160K tokens on DeepInfra. Raise your context budget in Atlas to actually use it, since DSA is what makes those tokens cheap to attend over.
- Is sparse attention worse than normal attention?
- It is an approximation. Recall over the far end of a 160K prompt on DeepSeek V3.2 is not guaranteed to match dense attention, so keep the code that matters high in the prompt rather than buried.
- Can I self-host DeepSeek V3.2?
- Yes, under the MIT open weights at deepseek-ai/DeepSeek-V3.2. It is a 671B-class MoE, so self-hosting is a multi-GPU exercise, not something a workstation runs.
- Should DeepSeek V3.2 be both my model and small_model in Atlas?
- Yes. Given the price, use the same id for `"small_model"` as for `"model"`. At $0.38 per Mtok output there is no cheaper tier worth splitting traffic to.
Try Atlas in your terminal
The terminal-native AI coding agent. Free core, single binary.
Install AtlasRelated guides
Write Unit Tests for Untested Code with Atlas in 2026
How to write unit tests for untested code with Atlas in 2026: the lsp tool enumerates exported symbols, grep copies repo conventions, and bash actually runs the suite.
Atlas vs GitHub Copilot CLI: A Developer's Guide for 2026
Comparing Atlas and GitHub Copilot CLI for developers in 2026. Explore pricing, privacy, agentic workflows, and extensibility to choose the best terminal AI coding agent.
Atlas for Angular in 2026
Adopt Atlas, the terminal-native AI coding agent, for your Angular projects in 2026. Enhance development with intelligent code search, secure local embeddings, and granular control over AI actions.
Atlas for Blazor: Terminal-Native AI Coding for .razor Components in 2026
Atlas is a terminal-native AI coding agent for Blazor developers in 2026. Work across .razor components, render modes, and the C# and JS interop boundary safely.
Atlas vs Gemini CLI: A Developer's Guide to Terminal AI Agents in 2026
Atlas vs Gemini CLI in 2026: Compare terminal AI coding agents. Atlas offers permission-gated tool calls and diff-based approvals. Gemini CLI provides a million-plus token context and a free tier.
Atlas for .NET: Your AI Coding Agent in 2026
Atlas empowers .NET developers in 2026 with a terminal-native AI coding agent. Securely integrate Atlas with ASP.NET Core for web APIs and services, leveraging local embeddings and robust safety features for efficient
Atlas vs Base44: Terminal AI Coding Agents in 2026
Compare Atlas, the terminal-native AI coding agent, with Base44, the Wix-owned no-code app builder, for developers in 2026. Evaluate features, pricing, and workflow.
Atlas for SolidJS: A Terminal-Native AI Coding Agent Built on Solid, for Solid, in 2026
Atlas is a terminal-native AI coding agent for SolidJS in 2026. It reads your createSignal and createMemo graph, fixes destructured props that break reactivity, and runs vitest.