Mistral Large 4 vs Mistral Medium 3
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 Medium 3 when its lower price or a specific cost or latency profile matters more than the latest capabilities.
Mistral Large 4 and Mistral Medium 3 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 Medium 3 is mistral's mid-tier model at $0.40/$2.00 — efficient general capability with a 128K window, below the 1M-context frontier tier. 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 Medium 3 is about 1.7× cheaper on input ($0.4/$2 per 1M tokens vs $0.68/$2.09 per 1M tokens) — modest, but it adds up at steady volume.
- ▸Context window: Mistral Large 4 holds 7.8× more — 1M tokens (~1,500 pages) vs 128K (~192 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 17 months (released October 6, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | Mistral Large 4 | Mistral Medium 3 |
|---|---|---|
| Provider | Mistral AI (France) | Mistral AI (France) |
| Released | October 6, 2026 | May 7, 2025 |
| Context window | 1M tokens (~1,500 pages) | 128K (~192 pages) |
| Price (in/out) | $0.68/$2.09 per 1M tokens | $0.4/$2 per 1M tokens |
| Open weight? | No — API only | No — API only |
| 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 7.8× more than Mistral Medium 3'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 Medium 3 is comparatively weak here — proprietary, unlike Mistral's open-weight releases
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.
Strong cost-to-capability at $0.40/$2.00
Mistral Medium 3
At $0.4/$2 per 1M tokens it undercuts Mistral Large 4 ($0.68/$2.09 per 1M tokens), and that gap compounds at volume.
General reasoning, coding and multimodal tasks
Mistral Medium 3
Mistral Large 4 is comparatively weak here — scores 61.7% on DeepSWE v1.1, trailing DeepSeek V4.1 Flash (74.2%) and Kimi K3 (68.0%) on that coding benchmark
Efficient mid-tier deployment for production workloads
Mistral Medium 3
Mistral's mid-tier model at $0.40/$2.00 — efficient general capability with a 128K window, below the 1M-context frontier tier — and it runs cheaper at $0.4/$2 per 1M tokens.
Lowest cost at scale
Mistral Medium 3
At $0.4/$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 7.8× larger than Mistral Medium 3's 128K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Mistral Medium 3
At $0.4/$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.
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 strong cost-to-capability at $0.40/$2.00
→ Mistral Medium 3
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 Medium 3: where it fits
Mistral's mid-tier model at $0.40/$2.00 — efficient general capability with a 128K window, below the 1M-context frontier tier. Released May 7, 2025 by Mistral AI, it is built for strong cost-to-capability at $0.40/$2.00, general reasoning, coding and multimodal tasks, efficient mid-tier deployment for production workloads, and text and image input.
Its trade-offs: a 128K context — smaller than the 1M-window flagships here, no published SWE-Bench Verified score, a mid-tier model, not a frontier reasoner, and proprietary, unlike Mistral's open-weight releases. At $0.4 in / $2 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
Because Mistral Large 4 and Mistral Medium 3 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 Medium 3 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 Medium 3 leans toward strong cost-to-capability at $0.40/$2.00, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Mistral Large 4 or Mistral Medium 3?
Mistral Medium 3 is cheaper — $0.68/$2.09 per 1M tokens vs $0.4/$2 per 1M tokens, roughly 1.7× apart on input.
Which has the bigger context window?
Mistral Large 4 — 1M tokens vs 128K, about 7.8× larger. Useful only if the model actually reasons over the full window, which not all do.
Should I upgrade from Mistral Medium 3 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 Medium 3?
Mistral Large 4 — released October 6, 2026, about 17 months after Mistral Medium 3.
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.