Mistral Large 4 vs Step 3.7 Flash

Mistral AI · France  |  StepFun · China · Updated June 2026

Quick verdict

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. Pick Step 3.7 Flash for a 198b-parameter sparse moe vision-language model (11b active) built specifically for agentic coding and search workflows or scmp reported it 'outshines larger rivals' from deepseek and moonshot on some benchmarks despite its smaller active size. Choose Step 3.7 Flash if you need self-hosting or data privacy; Mistral Large 4 if you want a managed API.

Mistral Large 4 (Mistral AI, France) and Step 3.7 Flash (StepFun, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. 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. Step 3.7 Flash is stepFun's 198B MoE vision-language model built for coding and search agents - a distinct Chinese lab with genuinely competitive open weights. 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

Side-by-side specs

SpecMistral Large 4Step 3.7 Flash
ProviderMistral AI (France) StepFun (China)
ReleasedOctober 6, 2026 May 29, 2026
Context window1M tokens (~1,500 pages) 256K (~393 pages)
Price (in/out)$0.68/$2.09 per 1M tokens $0.2/$1.15 per 1M tokens
Open weight?No — API only Yes — self-hostable
Modalitiestext, image, code text, image, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot 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 3.8× more than Step 3.7 Flash'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'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.

A 198B-parameter sparse MoE vision-language model (11B active) built specifically for agentic coding and search workflows

Step 3.7 Flash

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

SCMP reported it 'outshines larger rivals' from DeepSeek and Moonshot on some benchmarks despite its smaller active size

Step 3.7 Flash

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

Open weights (Apache 2.0) at a low per-token price

Step 3.7 Flash

At $0.2/$1.15 per 1M tokens it undercuts Mistral Large 4 ($0.68/$2.09 per 1M tokens), and that gap compounds at volume.

Lowest cost at scale

Step 3.7 Flash

At $0.2/$1.15 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 3.8× larger than Step 3.7 Flash's 256K, fitting roughly 1,500 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

→ Step 3.7 Flash

At $0.2/$1.15 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

→ Step 3.7 Flash

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 a 198b-parameter sparse moe vision-language model (11b active) built specifically for agentic coding and search workflows

→ Step 3.7 Flash

That is its strongest area.

An enterprise with regional data-residency rules

→ Step 3.7 Flash or Mistral Large 4

Origin (France vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.

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.

Step 3.7 Flash: where it fits

StepFun's 198B MoE vision-language model built for coding and search agents - a distinct Chinese lab with genuinely competitive open weights. Released May 29, 2026 by StepFun, it is built for a 198B-parameter sparse MoE vision-language model (11B active) built specifically for agentic coding and search workflows, sCMP reported it 'outshines larger rivals' from DeepSeek and Moonshot on some benchmarks despite its smaller active size, open weights (Apache 2.0) at a low per-token price, and includes a 1.8B vision encoder for image understanding alongside text.

Its trade-offs: stepFun is a newer, less established lab than DeepSeek, Alibaba, or Moonshot, benchmark 'outshines rivals' claims are from press coverage of specific tests, not a full independent leaderboard sweep, and smaller ecosystem and less third-party documentation than the more established Chinese labs. At $0.2 in / $1.15 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. Step 3.7 Flash 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 Mistral Large 4 and Step 3.7 Flash 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 pricing

Frequently asked questions

Is Mistral Large 4 or Step 3.7 Flash 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 Step 3.7 Flash leans toward a 198b-parameter sparse moe vision-language model (11b active) built specifically for agentic coding and search workflows, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, Mistral Large 4 or Step 3.7 Flash?

Step 3.7 Flash 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 3.8× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both Mistral Large 4 and Step 3.7 Flash together?

Yes — a multi-model platform like LumiChats gives you Mistral Large 4, Step 3.7 Flash 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, Mistral Large 4 or Step 3.7 Flash?

Mistral Large 4 — released October 6, 2026, about 4 months after Step 3.7 Flash.

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.