Llama 4 Scout vs Mistral Large 4
Meta · US | Mistral AI · France · Updated June 2026
Quick verdict
Pick Llama 4 Scout for largest advertised context (10m) or open weights, single-gpu friendly. 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 Llama 4 Scout if you need self-hosting or data privacy; Mistral Large 4 if you want a managed API.
Llama 4 Scout (Meta, 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. Llama 4 Scout is the 10M-token open-weight giant — enormous on paper, but usable recall is far smaller. 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
- ▸Cost model: Llama 4 Scout ships open weights you can self-host (hardware cost only, no per-token fee), while Mistral Large 4 is API-metered at $0.68/$2.09 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
- ▸Context window: Llama 4 Scout holds 10× more — 10M (~15,000 pages) vs 1M tokens (~1,500 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 18 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 | Llama 4 Scout | Mistral Large 4 |
|---|---|---|
| Provider | Meta (US) | Mistral AI (France) |
| Released | April 2025 | October 6, 2026 |
| Context window | 10M (~15,000 pages) | 1M tokens (~1,500 pages) |
| Price (in/out) | Open weight (self-host / free) | $0.68/$2.09 per 1M tokens |
| Open weight? | Yes — self-hostable | No — API only |
| Modalities | text, image, code | text, image, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | 15% | Not published |
Who wins what
Largest advertised context (10M)
Llama 4 Scout
Its 10M window holds about 10× more than Mistral Large 4's 1M tokens in a single prompt.
Open weights, single-GPU friendly
Llama 4 Scout
Open weights make this possible at all — Mistral Large 4 is API-only, so it cannot leave the vendor's servers.
Self-hosted, data-private deployment
Llama 4 Scout
The 10M-token open-weight giant — enormous on paper, but usable recall is far smaller — and it carries the larger 10M context.
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
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.
Trained from scratch on 3,800 Nvidia Grace Blackwell GPUs in Mistral's own European data centers, across 160+ languages
Mistral Large 4
Mistral Large 4 lists trained from scratch on 3,800 Nvidia Grace Blackwell GPUs in Mistral's own European data centers, across 160+ languages among its strengths; Llama 4 Scout does not.
Hybrid instruct/reasoning design — answers quickly by default or switches into step-by-step reasoning
Mistral Large 4
Llama 4 Scout is comparatively weak here — ~15% on long-context multi-needle reasoning
Lowest cost at scale
Llama 4 Scout
Its weights are open, so at volume you pay for your own hardware instead of Mistral Large 4's $0.68/$2.09 per 1M tokens.
Largest single-prompt input
Llama 4 Scout
Its 10M window is about 10× larger than Mistral Large 4's 1M tokens, fitting roughly 15,000 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Llama 4 Scout
At Open weight (self-host / free) it undercuts Mistral Large 4, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Llama 4 Scout
Larger 10M window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ Llama 4 Scout
Open weights let you run it on your own hardware; Mistral Large 4 is API-only.
Anyone whose priority is largest advertised context (10m)
→ Llama 4 Scout
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
→ Llama 4 Scout 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.
Llama 4 Scout: where it fits
The 10M-token open-weight giant — enormous on paper, but usable recall is far smaller. Released April 2025 by Meta, it is built for largest advertised context (10M), open weights, single-GPU friendly, self-hosted, data-private deployment, and retrieval over very long inputs.
Its trade-offs are real: effective recall degrades far below 10M, and ~15% on long-context multi-needle reasoning. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
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. Llama 4 Scout 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 Llama 4 Scout 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 Llama 4 Scout 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, Llama 4 Scout leans toward largest advertised context (10m) 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, Llama 4 Scout or Mistral Large 4?
Llama 4 Scout 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?
Llama 4 Scout — 10M vs 1M tokens, about 10× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Llama 4 Scout and Mistral Large 4 together?
Yes — a multi-model platform like LumiChats gives you Llama 4 Scout, 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, Llama 4 Scout or Mistral Large 4?
Mistral Large 4 — released October 6, 2026, about 18 months after Llama 4 Scout.
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.