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). Choose DeepSeek V3.2 if you need self-hosting or data privacy; Command A if you want a managed API.
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, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
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.
Specifications
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?
No — API only
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 — A core design strength of Command A.
Strong long-context retrieval accuracy: Command A — A core design strength of Command A.
Multilingual: Command A — A core design strength of Command A.
Long-context efficiency via DeepSeek Sparse Attention (DSA): DeepSeek V3.2 — A core design strength of DeepSeek V3.2.
Agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes): DeepSeek V3.2 — A core design strength of DeepSeek V3.2.
Elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386): DeepSeek V3.2 — A core design strength of DeepSeek V3.2.
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, 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.
A team with data-privacy or self-hosting needs: DeepSeek V3.2 — Open weights let you run it on your own hardware; Command A is API-only.
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: 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
The defining split here is open vs. closed. DeepSeek V3.2 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Command A gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Frequently 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 open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Command A is API-metered at $2.5/$10 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
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.
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). Choose DeepSeek V3.2 if you need self-hosting or data privacy; Command A if you want a managed API.
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, context window and open vs. closed weights — 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?
No — API only
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
A core design strength of Command A.
Strong long-context retrieval accuracy
Command A
A core design strength of Command A.
Multilingual
Command A
A core design strength of Command A.
Long-context efficiency via DeepSeek Sparse Attention (DSA)
DeepSeek V3.2
A core design strength of DeepSeek V3.2.
Agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes)
DeepSeek V3.2
A core design strength of DeepSeek V3.2.
Elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386)
DeepSeek V3.2
A core design strength of DeepSeek V3.2.
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, 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.
A team with data-privacy or self-hosting needs
→ DeepSeek V3.2
Open weights let you run it on your own hardware; Command A is API-only.
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: 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
The defining split here is open vs. closed. DeepSeek V3.2 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Command A gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
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.
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 open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Command A is API-metered at $2.5/$10 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
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.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.