Pick Llama 4 Scout for largest advertised context (10m) or open weights, single-gpu friendly. Pick Mistral Small 3.2 24B for extremely cheap open-weight model at about $0.075/$0.20 hosted or self-hostable under apache-2.0 with no per-token cost. On a tight budget at scale, Llama 4 Scout is the value pick.
Llama 4 Scout (Meta, US) and Mistral Small 3.2 24B (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 Small 3.2 24B is mistral's Apache-2.0 open 24B model at about $0.075/$0.20 hosted — very cheap and self-hostable, sized for volume over frontier quality. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
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 Small 3.2 24B is the newer model by about 3 months (released June 20, 2025), 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 Small 3.2 24B
Provider
Meta (US)
Mistral AI (France)
Released
April 2025
June 20, 2025
Context window
10M (~15,000 pages)
256K (~384 pages)
Price (in/out)
Open weight (self-host / free)
$0.075/$0.2 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
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 39× more than Mistral Small 3.2 24B's 256K in a single prompt.
Open weights, single-GPU friendly: 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.
Self-hosted, data-private deployment: Llama 4 Scout — Mistral Small 3.2 24B is comparatively weak here — hosted prices vary by provider; the figure shown is a common host rate
Extremely cheap open-weight model at about $0.075/$0.20 hosted: Mistral Small 3.2 24B — Mistral's Apache-2.0 open 24B model at about $0.075/$0.20 hosted — very cheap and self-hostable, sized for volume over frontier quality — and it is the newer of the two.
Self-hostable under Apache-2.0 with no per-token cost: Mistral Small 3.2 24B — Mistral Small 3.2 24B lists self-hostable under Apache-2.0 with no per-token cost among its strengths; Llama 4 Scout does not.
Instruction following and function calling at 24B scale: Mistral Small 3.2 24B — Mistral Small 3.2 24B lists instruction following and function calling at 24B scale among its strengths; Llama 4 Scout does not.
Lowest cost at scale: Llama 4 Scout — Its weights are open, so at volume you pay for your own hardware instead of Mistral Small 3.2 24B's $0.075/$0.2 per 1M tokens.
Largest single-prompt input: Llama 4 Scout — Its 10M window is about 39× larger than Mistral Small 3.2 24B's 256K, 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 Small 3.2 24B, 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.
Anyone whose priority is largest advertised context (10m): Llama 4 Scout — It is specifically built for that.
Anyone whose priority is extremely cheap open-weight model at about $0.075/$0.20 hosted: Mistral Small 3.2 24B — That is its strongest area.
An enterprise with regional data-residency rules: Llama 4 Scout or Mistral Small 3.2 24B — 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 Small 3.2 24B: where it fits
Mistral's Apache-2.0 open 24B model at about $0.075/$0.20 hosted — very cheap and self-hostable, sized for volume over frontier quality. Released June 20, 2025 by Mistral AI, it is built for extremely cheap open-weight model at about $0.075/$0.20 hosted, self-hostable under Apache-2.0 with no per-token cost, instruction following and function calling at 24B scale, and runs on modest hardware for local or private deployment.
Its trade-offs: a 24B small model — not a frontier reasoner, context reported as 256K but some references cite 128K native, no published SWE-Bench Verified score, and hosted prices vary by provider; the figure shown is a common host rate. At $0.075 in / $0.2 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." Llama 4 Scout (US) and Mistral Small 3.2 24B (France) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Llama 4 Scout 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.
Frequently asked questions
Is Llama 4 Scout or Mistral Small 3.2 24B 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 Small 3.2 24B leans toward extremely cheap open-weight model at about $0.075/$0.20 hosted, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Llama 4 Scout or Mistral Small 3.2 24B?
Llama 4 Scout is cheaper — Open weight (self-host / free) vs $0.075/$0.2 per 1M tokens.
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 Small 3.2 24B together?
Yes — a multi-model platform like LumiChats gives you Llama 4 Scout, Mistral Small 3.2 24B 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 Small 3.2 24B?
Mistral Small 3.2 24B — released June 20, 2025, about 3 months after Llama 4 Scout.
Llama 4 Scout vs Mistral Small 3.2 24B
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 Small 3.2 24B for extremely cheap open-weight model at about $0.075/$0.20 hosted or self-hostable under apache-2.0 with no per-token cost. On a tight budget at scale, Llama 4 Scout is the value pick.
Llama 4 Scout (Meta, US) and Mistral Small 3.2 24B (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 Small 3.2 24B is mistral's Apache-2.0 open 24B model at about $0.075/$0.20 hosted — very cheap and self-hostable, sized for volume over frontier quality. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸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 Small 3.2 24B is the newer model by about 3 months (released June 20, 2025), 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 Small 3.2 24B
Provider
Meta (US)
Mistral AI (France)
Released
April 2025
June 20, 2025
Context window
10M (~15,000 pages)
256K (~384 pages)
Price (in/out)
Open weight (self-host / free)
$0.075/$0.2 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
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 39× more than Mistral Small 3.2 24B's 256K in a single prompt.
Open weights, single-GPU friendly
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.
Self-hosted, data-private deployment
Llama 4 Scout
Mistral Small 3.2 24B is comparatively weak here — hosted prices vary by provider; the figure shown is a common host rate
Extremely cheap open-weight model at about $0.075/$0.20 hosted
Mistral Small 3.2 24B
Mistral's Apache-2.0 open 24B model at about $0.075/$0.20 hosted — very cheap and self-hostable, sized for volume over frontier quality — and it is the newer of the two.
Self-hostable under Apache-2.0 with no per-token cost
Mistral Small 3.2 24B
Mistral Small 3.2 24B lists self-hostable under Apache-2.0 with no per-token cost among its strengths; Llama 4 Scout does not.
Instruction following and function calling at 24B scale
Mistral Small 3.2 24B
Mistral Small 3.2 24B lists instruction following and function calling at 24B scale among its strengths; Llama 4 Scout does not.
Lowest cost at scale
Llama 4 Scout
Its weights are open, so at volume you pay for your own hardware instead of Mistral Small 3.2 24B's $0.075/$0.2 per 1M tokens.
Largest single-prompt input
Llama 4 Scout
Its 10M window is about 39× larger than Mistral Small 3.2 24B's 256K, 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 Small 3.2 24B, 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.
Anyone whose priority is largest advertised context (10m)
→ Llama 4 Scout
It is specifically built for that.
Anyone whose priority is extremely cheap open-weight model at about $0.075/$0.20 hosted
→ Mistral Small 3.2 24B
That is its strongest area.
An enterprise with regional data-residency rules
→ Llama 4 Scout or Mistral Small 3.2 24B
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 Small 3.2 24B: where it fits
Mistral's Apache-2.0 open 24B model at about $0.075/$0.20 hosted — very cheap and self-hostable, sized for volume over frontier quality. Released June 20, 2025 by Mistral AI, it is built for extremely cheap open-weight model at about $0.075/$0.20 hosted, self-hostable under Apache-2.0 with no per-token cost, instruction following and function calling at 24B scale, and runs on modest hardware for local or private deployment.
Its trade-offs: a 24B small model — not a frontier reasoner, context reported as 256K but some references cite 128K native, no published SWE-Bench Verified score, and hosted prices vary by provider; the figure shown is a common host rate. At $0.075 in / $0.2 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." Llama 4 Scout (US) and Mistral Small 3.2 24B (France) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Llama 4 Scout 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 Llama 4 Scout and Mistral Small 3.2 24B 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 Small 3.2 24B 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 Small 3.2 24B leans toward extremely cheap open-weight model at about $0.075/$0.20 hosted, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Llama 4 Scout or Mistral Small 3.2 24B?
Llama 4 Scout is cheaper — Open weight (self-host / free) vs $0.075/$0.2 per 1M tokens.
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 Small 3.2 24B together?
Yes — a multi-model platform like LumiChats gives you Llama 4 Scout, Mistral Small 3.2 24B 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 Small 3.2 24B?
Mistral Small 3.2 24B — released June 20, 2025, about 3 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.