Mistral Large 4 vs Mistral Small 3.2 24B
Mistral AI · France | Mistral AI · France · Updated June 2026
Quick verdict
Both are Mistral AI models. Mistral Large 4 is the newer, generally stronger default; reach for Mistral Small 3.2 24B when its lower price or a specific cost or latency profile matters more than the latest capabilities.
Mistral Large 4 and Mistral Small 3.2 24B are both Mistral AI models, so the real question is not which lab to trust but which tier fits your workload and budget. 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. Mistral Small 3.2 24B is mistral's Apache-2.0 open 24B model at about $0.075/$0.20 hosted — very cheap and self-hostable, sized for volume over frontier quality. Since both come from the same lab, the comparison below focuses on the tier-and-cost trade-offs that actually separate them.
Key differences at a glance
- ▸Price: Mistral Small 3.2 24B is about 9.1× cheaper on input ($0.075/$0.2 per 1M tokens vs $0.68/$2.09 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
- ▸Context window: Mistral Large 4 holds 7.6× more — 1M tokens (~1,500 pages) vs 128K (~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: Mistral Large 4 is the newer model by about 16 months (released October 6, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | Mistral Large 4 | Mistral Small 3.2 24B |
|---|---|---|
| Provider | Mistral AI (France) | Mistral AI (France) |
| Released | October 6, 2026 | June 20, 2025 |
| Context window | 1M tokens (~1,500 pages) | 128K (~197 pages) |
| Price (in/out) | $0.68/$2.09 per 1M tokens | $0.075/$0.2 per 1M tokens |
| Open weight? | No — API only | Yes — self-hostable |
| Modalities | text, image, code | text, image, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
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 7.6× more than Mistral Small 3.2 24B's 128K 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 carries the larger 1M tokens context.
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 is the newer of the two.
Extremely cheap open-weight model at about $0.075/$0.20 hosted
Mistral Small 3.2 24B
At $0.075/$0.2 per 1M tokens it undercuts Mistral Large 4 ($0.68/$2.09 per 1M tokens), and that gap compounds at volume.
Self-hostable under Apache-2.0 with no per-token cost
Mistral Small 3.2 24B
Open weights make this possible at all — Mistral Large 4 is API-only, so it cannot leave the vendor's servers.
Instruction following and function calling at 24B scale
Mistral Small 3.2 24B
Mistral's Apache-2.0 open 24B model at about $0.075/$0.20 hosted — very cheap and self-hostable, sized for volume over frontier quality — and it runs cheaper at $0.075/$0.2 per 1M tokens.
Lowest cost at scale
Mistral Small 3.2 24B
At $0.075/$0.2 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 7.6× larger than Mistral Small 3.2 24B's 128K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Mistral Small 3.2 24B
At $0.075/$0.2 per 1M tokens it undercuts Mistral Large 4, 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
→ Mistral Small 3.2 24B
Open weights let you run it on your own hardware; Mistral Large 4 is API-only.
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
It is specifically built for that.
Anyone whose priority is extremely cheap open-weight model at about $0.075/$0.20 hosted
→ Mistral Small 3.2 24B
That is its strongest area.
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 are real: 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.
Mistral Small 3.2 24B: where it fits
Mistral's Apache-2.0 open 24B model at about $0.075/$0.20 hosted — very cheap and self-hostable, sized for volume over frontier quality. Released June 20, 2025 by Mistral AI, it is built for extremely cheap open-weight model at about $0.075/$0.20 hosted, self-hostable under Apache-2.0 with no per-token cost, instruction following and function calling at 24B scale, and runs on modest hardware for local or private deployment.
Its trade-offs: a 24B small model — not a frontier reasoner, context reported as 256K but some references cite 128K native, no published SWE-Bench Verified score, and hosted prices vary by provider; the figure shown is a common host rate. At $0.075 in / $0.2 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
Because Mistral Large 4 and Mistral Small 3.2 24B come from the same lab (Mistral AI), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. Mistral Large 4 is the more capable, more recent option; the other earns its place only when its price or latency profile fits a specific job better. Most teams should default to Mistral Large 4 and drop down only with a concrete reason.
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See pricingFrequently asked questions
Is Mistral Large 4 or Mistral Small 3.2 24B 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, 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 while Mistral Small 3.2 24B leans toward extremely cheap open-weight model at about $0.075/$0.20 hosted, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Mistral Large 4 or Mistral Small 3.2 24B?
Mistral Small 3.2 24B 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 128K, about 7.6× larger. Useful only if the model actually reasons over the full window, which not all do.
Should I upgrade from Mistral Small 3.2 24B to Mistral Large 4?
Since both are Mistral AI models, the newer one (Mistral Large 4) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, Mistral Large 4 or Mistral Small 3.2 24B?
Mistral Large 4 — released October 6, 2026, about 16 months after Mistral Small 3.2 24B.
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.