Command A vs Mistral Large 4

Cohere · Global  |  Mistral AI · France · Updated June 2026

Quick verdict

Pick Command A for enterprise rag and retrieval or strong long-context retrieval accuracy. Pick Mistral Large 4 for mistral's largest model to date — 1.05t total parameters, 49b active per token (granular moe), plus a 1.6b-parameter vision encoder or trained from scratch on 3,800 nvidia grace blackwell gpus in mistral's own european data centers, across 160+ languages. Choose Command A if you need self-hosting or data privacy; Mistral Large 4 if you want a managed API.

Command A (Cohere) and Mistral Large 4 (Mistral AI) 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. Mistral Large 4 is mistral's October 6, 2026 public preview of 'Le Chonk' — a 1.05T-parameter, natively multimodal MoE model with open weights planned by month's end. 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

Side-by-side specs

SpecCommand AMistral Large 4
ProviderCohere (Global) Mistral AI (France)
ReleasedMarch 2025 October 6, 2026
Context window256K (~384 pages) 1M tokens (~1,500 pages)
Price (in/out)$2.5/$10 per 1M tokens $0.68/$2.09 per 1M tokens
Open weight?Yes — self-hostable No — API only
Modalitiestext, code text, image, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot 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 its weights are open while Mistral Large 4 is API-only.

Strong long-context retrieval accuracy

Command A

Command A lists strong long-context retrieval accuracy among its strengths; Mistral Large 4 does not.

Multilingual

Command A

Command A lists multilingual among its strengths; Mistral Large 4 does not.

Mistral's largest model to date — 1.05T total parameters, 49B active per token (granular MoE), plus a 1.6B-parameter vision encoder

Mistral Large 4

Its 1M tokens window holds about 3.9× more than Command A's 256K in a single prompt.

Trained from scratch on 3,800 Nvidia Grace Blackwell GPUs in Mistral's own European data centers, across 160+ languages

Mistral Large 4

Mistral's October 6, 2026 public preview of 'Le Chonk' — a 1.05T-parameter, natively multimodal MoE model with open weights planned by month's end — and it runs cheaper at $0.68/$2.09 per 1M tokens.

Hybrid instruct/reasoning design — answers quickly by default or switches into step-by-step reasoning

Mistral Large 4

Mistral's October 6, 2026 public preview of 'Le Chonk' — a 1.05T-parameter, natively multimodal MoE model with open weights planned by month's end — and it carries the larger 1M tokens context.

Lowest cost at scale

Mistral Large 4

At $0.68/$2.09 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.

Largest single-prompt input

Mistral Large 4

Its 1M tokens window is about 3.9× larger than Command A's 256K, fitting roughly 1,500 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

→ Mistral Large 4

At $0.68/$2.09 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

→ Mistral Large 4

Larger 1M tokens window fits more in one prompt.

A team with data-privacy or self-hosting needs

→ Command A

Open weights let you run it on your own hardware; Mistral Large 4 is API-only.

Anyone whose priority is enterprise rag and retrieval

→ Command A

It is specifically built for that.

Anyone whose priority is mistral's largest model to date — 1.05t total parameters, 49b active per token (granular moe), plus a 1.6b-parameter vision encoder

→ Mistral Large 4

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.

Mistral Large 4: where it fits

Mistral's October 6, 2026 public preview of 'Le Chonk' — a 1.05T-parameter, natively multimodal MoE model with open weights planned by month's end. Released October 6, 2026 by Mistral AI, it is built for mistral's largest model to date — 1.05T total parameters, 49B active per token (granular MoE), plus a 1.6B-parameter vision encoder, trained from scratch on 3,800 Nvidia Grace Blackwell GPUs in Mistral's own European data centers, across 160+ languages, hybrid instruct/reasoning design — answers quickly by default or switches into step-by-step reasoning, and open weights planned by end of October 2026, which would make it among the largest open-weight models available.

Its trade-offs: still a public preview, not a finished release — Mistral says training is ongoing with 'no signs of saturation', weights not yet shipped as of mid-October 2026 — API-only for now despite the open-weight framing, launch pricing ($0.68/$2.09 per million) is a preview discount — Mistral's own model page lists a $1.36/$4.18 list price once the promotion ends, and scores 61.7% on DeepSWE v1.1, trailing DeepSeek V4.1 Flash (74.2%) and Kimi K3 (68.0%) on that coding benchmark. At $0.68 in / $2.09 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. Command A gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Mistral Large 4 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 Mistral Large 4 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 pricing

Frequently asked questions

Is Command A or Mistral Large 4 better for coding?

Public SWE-Bench figures are not available for either model, 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 Mistral Large 4 leans toward mistral's largest model to date — 1.05t total parameters, 49b active per token (granular moe), plus a 1.6b-parameter vision encoder, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, Command A or Mistral Large 4?

Command A is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Mistral Large 4 is API-metered at $0.68/$2.09 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?

Mistral Large 4 — 1M tokens vs 256K, about 3.9× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both Command A and Mistral Large 4 together?

Yes — a multi-model platform like LumiChats gives you Command A, Mistral Large 4 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 Mistral Large 4?

Mistral Large 4 — released October 6, 2026, about 19 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.