MiniMax M3 vs Mistral Large 4
MiniMax · China | Mistral AI · France · Updated June 2026
Quick verdict
Pick MiniMax M3 for open-weight 428b moe (~23b active per token) with a 1m-token context or native multimodal input — text, image and video. 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 MiniMax M3 if you need self-hosting or data privacy; Mistral Large 4 if you want a managed API.
MiniMax M3 (MiniMax, China) 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. MiniMax M3 is miniMax's open-weight 428B multimodal model with a 1M context — strong reported coding (59.0 SWE-Bench Pro) at low entry pricing. 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
- ▸Price: MiniMax M3 is about 3× cheaper on input ($0.23/$0.96 per 1M tokens vs $0.68/$2.09 per 1M tokens) — meaningful once you are processing millions of tokens a month.
- ▸Context window: 1M vs 1M tokens — within a few percent of each other, so treat this as a tie and test on your own long inputs, since usable recall varies by model.
- ▸Recency: Mistral Large 4 is the newer model by about 4 months (released October 6, 2026), usually meaning fresher training data and capabilities.
- ▸Ecosystem: this is a China-vs-France matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
| Spec | MiniMax M3 | Mistral Large 4 |
|---|---|---|
| Provider | MiniMax (China) | Mistral AI (France) |
| Released | May 31, 2026 | October 6, 2026 |
| Context window | 1M (~1,573 pages) | 1M tokens (~1,500 pages) |
| Price (in/out) | $0.23/$0.96 per 1M tokens | $0.68/$2.09 per 1M tokens |
| Open weight? | Yes — self-hostable | No — API only |
| Modalities | text, image, video, code | text, image, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Open-weight 428B MoE (~23B active per token) with a 1M-token context
MiniMax M3
Open weights make this possible at all — Mistral Large 4 is API-only, so it cannot leave the vendor's servers.
Native multimodal input — text, image and video
MiniMax M3
MiniMax's open-weight 428B multimodal model with a 1M context — strong reported coding (59.0 SWE-Bench Pro) at low entry pricing — and it runs cheaper at $0.23/$0.96 per 1M tokens.
Reports 59.0 on SWE-Bench Pro (a strong open-weight score), surpassing GPT-5.5
MiniMax M3
Mistral Large 4 is comparatively weak here — weights not yet shipped as of mid-October 2026 — API-only for now despite the open-weight framing
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
MiniMax M3 is comparatively weak here — price doubles to $0.60/$2.40 above 512K tokens — not flat across 1M
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 is the newer of the two.
Hybrid instruct/reasoning design — answers quickly by default or switches into step-by-step reasoning
Mistral Large 4
Mistral Large 4 lists hybrid instruct/reasoning design — answers quickly by default or switches into step-by-step reasoning among its strengths; MiniMax M3 does not.
Lowest cost at scale
MiniMax M3
At $0.23/$0.96 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Which should you pick?
A cost-sensitive startup shipping high volume
→ MiniMax M3
At $0.23/$0.96 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
→ MiniMax M3
Larger 1M window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ MiniMax M3
Open weights let you run it on your own hardware; Mistral Large 4 is API-only.
Anyone whose priority is open-weight 428b moe (~23b active per token) with a 1m-token context
→ MiniMax M3
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
→ Mistral Large 4 or MiniMax M3
Origin (China vs France) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
MiniMax M3: where it fits
MiniMax's open-weight 428B multimodal model with a 1M context — strong reported coding (59.0 SWE-Bench Pro) at low entry pricing. Released May 31, 2026 by MiniMax, it is built for open-weight 428B MoE (~23B active per token) with a 1M-token context, native multimodal input — text, image and video, reports 59.0 on SWE-Bench Pro (a strong open-weight score), surpassing GPT-5.5, and low entry pricing at $0.30/$1.20 per million up to 512K tokens.
Its trade-offs are real: price doubles to $0.60/$2.40 above 512K tokens — not flat across 1M, miniMax's own reported 80.5 SWE-Bench Verified figure is vendor-stated, not independently verified, sWE-Bench Pro is a different, harder benchmark than SWE-Bench Verified, and newer than M2.7 but with less independent testing so far. At $0.23 in / $0.96 out per million tokens, it sits in the budget 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. MiniMax M3 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 MiniMax M3 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 MiniMax M3 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, MiniMax M3 leans toward open-weight 428b moe (~23b active per token) with a 1m-token context 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, MiniMax M3 or Mistral Large 4?
MiniMax M3 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?
Effectively neither — 1M vs 1M tokens is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both MiniMax M3 and Mistral Large 4 together?
Yes — a multi-model platform like LumiChats gives you MiniMax M3, 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, MiniMax M3 or Mistral Large 4?
Mistral Large 4 — released October 6, 2026, about 4 months after MiniMax M3.
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.