Command A vs DeepSeek V3.2
Cohere · Global | DeepSeek · China · Updated June 2026
Quick verdict
Pick Command A for enterprise rag and retrieval or strong long-context retrieval accuracy. Pick DeepSeek V3.2 for long-context efficiency via deepseek sparse attention (dsa) or agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes). On a tight budget at scale, DeepSeek V3.2 is the value pick.
Command A (Cohere) and DeepSeek V3.2 (DeepSeek) are two of the models people most often weigh against each other in 2026. Command A is cohere's enterprise-focused model built for retrieval-augmented and grounded workloads. DeepSeek V3.2 is a cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
- ▸Price: DeepSeek V3.2 is about 8.9× cheaper on input ($0.28/$0.42 per 1M tokens vs $2.5/$10 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
- ▸Context window: Command A holds 2× more — 256K (~384 pages) vs 131K (~197 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
- ▸Recency: DeepSeek V3.2 is the newer model by about 9 months (released December 1, 2025), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | Command A | DeepSeek V3.2 |
|---|---|---|
| Provider | Cohere (Global) | DeepSeek (China) |
| Released | March 2025 | December 1, 2025 |
| Context window | 256K (~384 pages) | 131K (~197 pages) |
| Price (in/out) | $2.5/$10 per 1M tokens | $0.28/$0.42 per 1M tokens |
| Open weight? | Yes — self-hostable | Yes — self-hostable |
| Modalities | text, code | text, code |
| SWE-Bench Verified | Not published | 73.1% |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Enterprise RAG and retrieval
Command A
Cohere's enterprise-focused model built for retrieval-augmented and grounded workloads — and it carries the larger 256K context.
Strong long-context retrieval accuracy
Command A
Its 256K window holds about 2× more than DeepSeek V3.2's 131K in a single prompt.
Multilingual
Command A
Command A lists multilingual among its strengths; DeepSeek V3.2 does not.
Long-context efficiency via DeepSeek Sparse Attention (DSA)
DeepSeek V3.2
A cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference — and it runs cheaper at $0.28/$0.42 per 1M tokens.
Agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes)
DeepSeek V3.2
A cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference — and it is the newer of the two.
Elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386)
DeepSeek V3.2
DeepSeek V3.2 lists elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386) among its strengths; Command A does not.
Lowest cost at scale
DeepSeek V3.2
At $0.28/$0.42 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
Command A
Its 256K window is about 2× larger than DeepSeek V3.2's 131K, fitting roughly 384 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ DeepSeek V3.2
At $0.28/$0.42 per 1M tokens it undercuts Command A, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Command A
Larger 256K window fits more in one prompt.
Anyone whose priority is enterprise rag and retrieval
→ Command A
It is specifically built for that.
Anyone whose priority is long-context efficiency via deepseek sparse attention (dsa)
→ DeepSeek V3.2
That is its strongest area.
Command A: where it fits
Cohere's enterprise-focused model built for retrieval-augmented and grounded workloads. Released March 2025 by Cohere, it is built for enterprise RAG and retrieval, strong long-context retrieval accuracy, multilingual, and tool use.
Its trade-offs are real: less consumer presence, and narrower modality support. At $2.5 in / $10 out per million tokens, it sits in the mid price band.
DeepSeek V3.2: where it fits
A cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference. Released December 1, 2025 by DeepSeek, it is built for long-context efficiency via DeepSeek Sparse Attention (DSA), agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes), elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386), and low-cost, open-weight (MIT) self-hosting.
Its trade-offs: superseded - no longer listed on DeepSeek's current pricing page as of August 2026; DeepSeek V4-Flash/V4-Pro are the current models, text-only — no image, audio, or video input, and sWE-Bench Verified (73.1) trails the top closed coding models (Claude 4.5 Sonnet 77.2, Gemini 3 Pro 76.2). At $0.28 in / $0.42 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
Command A and DeepSeek V3.2 overlap enough that the right pick depends on your specific job. DeepSeek V3.2 costs less per token; Command A holds the larger context; and each leads in its own area — Command A for enterprise rag and retrieval, DeepSeek V3.2 for long-context efficiency via deepseek sparse attention (dsa). Rather than crowning one, run the same hard task through both once and let the results decide.
Want both Command A and DeepSeek V3.2 without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.
See pricingFrequently asked questions
Is Command A or DeepSeek V3.2 better for coding?
Public SWE-Bench figures are not available for Command A, so the honest test is your own repository — run an identical real bug through both. By design, Command A leans toward enterprise rag and retrieval while DeepSeek V3.2 leans toward long-context efficiency via deepseek sparse attention (dsa), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Command A or DeepSeek V3.2?
DeepSeek V3.2 is cheaper — $2.5/$10 per 1M tokens vs $0.28/$0.42 per 1M tokens, roughly 8.9× apart on input.
Which has the bigger context window?
Command A — 256K vs 131K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Command A and DeepSeek V3.2 together?
Yes — a multi-model platform like LumiChats gives you Command A, DeepSeek V3.2 and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, Command A or DeepSeek V3.2?
DeepSeek V3.2 — released December 1, 2025, about 9 months after Command A.
Related comparisons
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.