Mistral Large 4 vs Mistral Medium 3.5
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 3.5 when a specific cost or latency profile matters more than the latest capabilities.
Mistral Large 4 and Mistral Medium 3.5 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 3.5 is mistral's efficient 128B open-weight model — a single dense model unifying reasoning and coding that punches above its size on independent tests (AA Index 30). 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 Large 4 is about 2.2× cheaper on input ($0.68/$2.09 per 1M tokens vs $1.5/$7.5 per 1M tokens) — meaningful once you are processing millions of tokens a month.
- ▸Context window: Mistral Large 4 holds 3.9× more — 1M tokens (~1,500 pages) vs 256K (~384 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 5 months (released October 6, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | Mistral Large 4 | Mistral Medium 3.5 |
|---|---|---|
| Provider | Mistral AI (France) | Mistral AI (France) |
| Released | October 6, 2026 | April 28, 2026 |
| Context window | 1M tokens (~1,500 pages) | 256K (~384 pages) |
| Price (in/out) | $0.68/$2.09 per 1M tokens | $1.5/$7.5 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 3.9× more than Mistral Medium 3.5'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 Medium 3.5 is comparatively weak here — license framing ('open' vs enterprise) varies across sources — confirm terms for commercial use
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 runs cheaper at $0.68/$2.09 per 1M tokens.
Strong intelligence-for-size — Artificial Analysis Intelligence Index 30, near the top of its price tier
Mistral Medium 3.5
Mistral Large 4 is comparatively weak here — 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
128B dense open-weight model — self-hostable
Mistral Medium 3.5
Open weights make this possible at all — Mistral Large 4 is API-only, so it cannot leave the vendor's servers.
Unifies reasoning and coding into one model with an adjustable reasoning effort
Mistral Medium 3.5
Mistral's efficient 128B open-weight model — a single dense model unifying reasoning and coding that punches above its size on independent tests (AA Index 30) — and its weights are open while Mistral Large 4 is API-only.
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 Mistral Medium 3.5'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 Mistral Medium 3.5, 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 Medium 3.5
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 strong intelligence-for-size — artificial analysis intelligence index 30, near the top of its price tier
→ Mistral Medium 3.5
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 3.5: where it fits
Mistral's efficient 128B open-weight model — a single dense model unifying reasoning and coding that punches above its size on independent tests (AA Index 30). Released April 28, 2026 by Mistral AI, it is built for strong intelligence-for-size — Artificial Analysis Intelligence Index 30, near the top of its price tier, 128B dense open-weight model — self-hostable, unifies reasoning and coding into one model with an adjustable reasoning effort, and 256K context with text and image input.
Its trade-offs: below the absolute frontier — a value/efficiency pick, not a flagship-beater, output pricing ($7.50/M) is higher than the cheapest Chinese rivals, license framing ('open' vs enterprise) varies across sources — confirm terms for commercial use, and no native video or audio. At $1.5 in / $7.5 out per million tokens, it sits in the mid price band.
The bottom line for this matchup
Because Mistral Large 4 and Mistral Medium 3.5 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 3.5 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 3.5 leans toward strong intelligence-for-size — artificial analysis intelligence index 30, near the top of its price tier, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Mistral Large 4 or Mistral Medium 3.5?
Mistral Medium 3.5 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.
Should I upgrade from Mistral Medium 3.5 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 3.5?
Mistral Large 4 — released October 6, 2026, about 5 months after Mistral Medium 3.5.
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.