Pick Llama 4 Scout for largest advertised context (10m) or open weights, single-gpu friendly. Pick Mistral Large 3 for european data-residency option or strong multilingual performance. Choose Llama 4 Scout if you need self-hosting or data privacy; Mistral Large 3 if you want a managed API.
Llama 4 Scout (Meta, US) and Mistral Large 3 (Mistral, 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 3 is france's frontier contender — strong multilingual model with European data residency. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Cost model: Llama 4 Scout ships open weights you can self-host (hardware cost only, no per-token fee), while Mistral Large 3 is API-metered at $2/$6 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 39× more — 10M (~15,000 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 3 is the newer model by about 10 months (released 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.
Specifications
Spec
Llama 4 Scout
Mistral Large 3
Provider
Meta (US)
Mistral (France)
Released
April 2025
2026
Context window
10M (~15,000 pages)
256K (~384 pages)
Price (in/out)
Open weight (self-host / free)
$2/$6 per 1M tokens
Open weight?
Yes — self-hostable
No — API only
Modalities
text, image, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
15%
Not published
Who wins what
Largest advertised context (10M): Llama 4 Scout — A core design strength of Llama 4 Scout.
Open weights, single-GPU friendly: Llama 4 Scout — A core design strength of Llama 4 Scout.
Self-hosted, data-private deployment: Llama 4 Scout — A core design strength of Llama 4 Scout.
European data-residency option: Mistral Large 3 — A core design strength of Mistral Large 3.
Strong multilingual performance: Mistral Large 3 — A core design strength of Mistral Large 3.
Efficient inference: Mistral Large 3 — A core design strength of Mistral Large 3.
Lowest cost at scale: Llama 4 Scout — At Open weight (self-host / free), it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: Llama 4 Scout — Its 10M window is about 39× larger, 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 3, 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 3 is API-only.
Anyone whose priority is largest advertised context (10m): Llama 4 Scout — It is specifically built for that.
Anyone whose priority is european data-residency option: Mistral Large 3 — That is its strongest area.
An enterprise with regional data-residency rules: Llama 4 Scout or Mistral Large 3 — 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 3: where it fits
France's frontier contender — strong multilingual model with European data residency. Released 2026 by Mistral, it is built for european data-residency option, strong multilingual performance, efficient inference, and function calling.
Its trade-offs: smaller context than US/China frontier, and less benchmark coverage. At $2 in / $6 out per million tokens, it sits in the mid 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 3 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.
Frequently asked questions
Is Llama 4 Scout or Mistral Large 3 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 3 leans toward european data-residency option, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Llama 4 Scout or Mistral Large 3?
Llama 4 Scout is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Mistral Large 3 is API-metered at $2/$6 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 256K, about 39× 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 3 together?
Yes — a multi-model platform like LumiChats gives you Llama 4 Scout, Mistral Large 3 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 3?
Mistral Large 3 — released 2026, about 10 months after Llama 4 Scout.
Llama 4 Scout vs Mistral Large 3
Meta · US | Mistral · France · Updated June 2026
Quick verdict
Pick Llama 4 Scout for largest advertised context (10m) or open weights, single-gpu friendly. Pick Mistral Large 3 for european data-residency option or strong multilingual performance. Choose Llama 4 Scout if you need self-hosting or data privacy; Mistral Large 3 if you want a managed API.
Llama 4 Scout (Meta, US) and Mistral Large 3 (Mistral, 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 3 is france's frontier contender — strong multilingual model with European data residency. 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 3 is API-metered at $2/$6 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 39× more — 10M (~15,000 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 3 is the newer model by about 10 months (released 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 3
Provider
Meta (US)
Mistral (France)
Released
April 2025
2026
Context window
10M (~15,000 pages)
256K (~384 pages)
Price (in/out)
Open weight (self-host / free)
$2/$6 per 1M tokens
Open weight?
Yes — self-hostable
No — API only
Modalities
text, image, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
15%
Not published
Who wins what
Largest advertised context (10M)
Llama 4 Scout
A core design strength of Llama 4 Scout.
Open weights, single-GPU friendly
Llama 4 Scout
A core design strength of Llama 4 Scout.
Self-hosted, data-private deployment
Llama 4 Scout
A core design strength of Llama 4 Scout.
European data-residency option
Mistral Large 3
A core design strength of Mistral Large 3.
Strong multilingual performance
Mistral Large 3
A core design strength of Mistral Large 3.
Efficient inference
Mistral Large 3
A core design strength of Mistral Large 3.
Lowest cost at scale
Llama 4 Scout
At Open weight (self-host / free), it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
Llama 4 Scout
Its 10M window is about 39× larger, 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 3, 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 3 is API-only.
Anyone whose priority is largest advertised context (10m)
→ Llama 4 Scout
It is specifically built for that.
Anyone whose priority is european data-residency option
→ Mistral Large 3
That is its strongest area.
An enterprise with regional data-residency rules
→ Llama 4 Scout or Mistral Large 3
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 3: where it fits
France's frontier contender — strong multilingual model with European data residency. Released 2026 by Mistral, it is built for european data-residency option, strong multilingual performance, efficient inference, and function calling.
Its trade-offs: smaller context than US/China frontier, and less benchmark coverage. At $2 in / $6 out per million tokens, it sits in the mid 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 3 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 3 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.
Is Llama 4 Scout or Mistral Large 3 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 3 leans toward european data-residency option, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Llama 4 Scout or Mistral Large 3?
Llama 4 Scout is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Mistral Large 3 is API-metered at $2/$6 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 256K, about 39× 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 3 together?
Yes — a multi-model platform like LumiChats gives you Llama 4 Scout, Mistral Large 3 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 3?
Mistral Large 3 — released 2026, about 10 months after Llama 4 Scout.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.