GPT-5.1 vs Mistral Large 4
OpenAI · US | Mistral AI · France · Updated June 2026
Quick verdict
Pick GPT-5.1 for adaptive reasoning that scales compute to task difficulty or general-purpose reasoning, coding and tool use. 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. On a tight budget at scale, Mistral Large 4 is the value pick.
GPT-5.1 (OpenAI, US) and Mistral Large 4 (Mistral AI, France) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. GPT-5.1 is openAI's late-2025 GPT-5.1 at $1.25/$10 — a capable general model since surpassed by the GPT-5.2–5.6 line. 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 and context window — each quantified below from the models' real specs.
Key differences at a glance
- ▸Price: Mistral Large 4 is about 1.8× cheaper on input ($0.68/$2.09 per 1M tokens vs $1.25/$10 per 1M tokens) — modest, but it adds up at steady volume.
- ▸Context window: Mistral Large 4 holds 2.5× more — 1M tokens (~1,500 pages) vs 400K (~600 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 11 months (released October 6, 2026), usually meaning fresher training data and capabilities.
- ▸Ecosystem: this is a US-vs-France matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
| Spec | GPT-5.1 | Mistral Large 4 |
|---|---|---|
| Provider | OpenAI (US) | Mistral AI (France) |
| Released | November 13, 2025 | October 6, 2026 |
| Context window | 400K (~600 pages) | 1M tokens (~1,500 pages) |
| Price (in/out) | $1.25/$10 per 1M tokens | $0.68/$2.09 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
Adaptive reasoning that scales compute to task difficulty
GPT-5.1
GPT-5.1 lists adaptive reasoning that scales compute to task difficulty among its strengths; Mistral Large 4 does not.
General-purpose reasoning, coding and tool use
GPT-5.1
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
A large ~400K context for long documents and sessions
GPT-5.1
GPT-5.1 lists a large ~400K context for long documents and sessions 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 2.5× more than GPT-5.1's 400K 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
GPT-5.1 is comparatively weak here — exact context window unconfirmed (400K vs 410K across trackers)
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.
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 2.5× larger than GPT-5.1's 400K, 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 GPT-5.1, 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 adaptive reasoning that scales compute to task difficulty
→ GPT-5.1
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.
An enterprise with regional data-residency rules
→ GPT-5.1 or Mistral Large 4
Origin (US vs France) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
GPT-5.1: where it fits
OpenAI's late-2025 GPT-5.1 at $1.25/$10 — a capable general model since surpassed by the GPT-5.2–5.6 line. Released November 13, 2025 by OpenAI, it is built for adaptive reasoning that scales compute to task difficulty, general-purpose reasoning, coding and tool use, a large ~400K context for long documents and sessions, and text and image input.
Its trade-offs are real: superseded by GPT-5.2 through GPT-5.6 — kept for compatibility rather than being current, no reliably published SWE-Bench Verified figure, exact context window unconfirmed (400K vs 410K across trackers), and premium output price at $10/1M for a now-older model. At $1.25 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
This is less "which is smarter" and more "which ecosystem fits." GPT-5.1 (US) and Mistral Large 4 (France) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Mistral Large 4 is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.
Want both GPT-5.1 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 pricingFrequently asked questions
Is GPT-5.1 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, GPT-5.1 leans toward adaptive reasoning that scales compute to task difficulty 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, GPT-5.1 or Mistral Large 4?
Mistral Large 4 is cheaper — $1.25/$10 per 1M tokens vs $0.68/$2.09 per 1M tokens, roughly 1.8× apart on input.
Which has the bigger context window?
Mistral Large 4 — 1M tokens vs 400K, about 2.5× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GPT-5.1 and Mistral Large 4 together?
Yes — a multi-model platform like LumiChats gives you GPT-5.1, 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, GPT-5.1 or Mistral Large 4?
Mistral Large 4 — released October 6, 2026, about 11 months after GPT-5.1.
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.