Pick Llama 4 Scout for largest advertised context (10m) or open weights, single-gpu friendly. Pick Mistral NeMo for multilingual understanding across 11+ languages or runs on a single gpu with fp8 quantization-aware training. On a tight budget at scale, Llama 4 Scout is the value pick.
Llama 4 Scout (Meta, US) and Mistral NeMo (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 NeMo is a 12B Apache-2.0 open-weight model co-developed by Mistral and NVIDIA, pairing a 128K context and strong multilingual performance with efficiency that fits on a single GPU. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Context window: Llama 4 Scout holds 76× more — 10M (~15,000 pages) vs 128K (~197 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: Llama 4 Scout is the newer model by about 9 months (released April 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 NeMo
Provider
Meta (US)
Mistral (France)
Released
April 2025
July 18, 2024
Context window
10M (~15,000 pages)
128K (~197 pages)
Price (in/out)
Open weight (self-host / free)
$0.02/$0.03 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, code
text
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.
Multilingual understanding across 11+ languages: Mistral NeMo — A core design strength of Mistral NeMo.
Runs on a single GPU with FP8 quantization-aware training: Mistral NeMo — A core design strength of Mistral NeMo.
128K-token context for long documents: Mistral NeMo — A core design strength of Mistral NeMo.
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 76× 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 NeMo, 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 multilingual understanding across 11+ languages: Mistral NeMo — That is its strongest area.
An enterprise with regional data-residency rules: Llama 4 Scout or Mistral NeMo — 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 NeMo: where it fits
A 12B Apache-2.0 open-weight model co-developed by Mistral and NVIDIA, pairing a 128K context and strong multilingual performance with efficiency that fits on a single GPU. Released July 18, 2024 by Mistral, it is built for multilingual understanding across 11+ languages, runs on a single GPU with FP8 quantization-aware training, 128K-token context for long documents, and function calling and structured tool use.
Its trade-offs: 12B scale trails larger frontier models on complex reasoning and coding, and text-only; no vision or audio input. At $0.02 in / $0.03 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 NeMo (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 NeMo 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 NeMo leans toward multilingual understanding across 11+ languages, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Llama 4 Scout or Mistral NeMo?
Llama 4 Scout is cheaper — Open weight (self-host / free) vs $0.02/$0.03 per 1M tokens.
Which has the bigger context window?
Llama 4 Scout — 10M vs 128K, about 76× 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 NeMo together?
Yes — a multi-model platform like LumiChats gives you Llama 4 Scout, Mistral NeMo 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 NeMo?
Llama 4 Scout — released April 2025, about 9 months after Mistral NeMo.
Llama 4 Scout vs Mistral NeMo
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 NeMo for multilingual understanding across 11+ languages or runs on a single gpu with fp8 quantization-aware training. On a tight budget at scale, Llama 4 Scout is the value pick.
Llama 4 Scout (Meta, US) and Mistral NeMo (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 NeMo is a 12B Apache-2.0 open-weight model co-developed by Mistral and NVIDIA, pairing a 128K context and strong multilingual performance with efficiency that fits on a single GPU. 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 76× more — 10M (~15,000 pages) vs 128K (~197 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: Llama 4 Scout is the newer model by about 9 months (released April 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 NeMo
Provider
Meta (US)
Mistral (France)
Released
April 2025
July 18, 2024
Context window
10M (~15,000 pages)
128K (~197 pages)
Price (in/out)
Open weight (self-host / free)
$0.02/$0.03 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, code
text
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.
Multilingual understanding across 11+ languages
Mistral NeMo
A core design strength of Mistral NeMo.
Runs on a single GPU with FP8 quantization-aware training
Mistral NeMo
A core design strength of Mistral NeMo.
128K-token context for long documents
Mistral NeMo
A core design strength of Mistral NeMo.
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 76× 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 NeMo, 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 multilingual understanding across 11+ languages
→ Mistral NeMo
That is its strongest area.
An enterprise with regional data-residency rules
→ Llama 4 Scout or Mistral NeMo
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 NeMo: where it fits
A 12B Apache-2.0 open-weight model co-developed by Mistral and NVIDIA, pairing a 128K context and strong multilingual performance with efficiency that fits on a single GPU. Released July 18, 2024 by Mistral, it is built for multilingual understanding across 11+ languages, runs on a single GPU with FP8 quantization-aware training, 128K-token context for long documents, and function calling and structured tool use.
Its trade-offs: 12B scale trails larger frontier models on complex reasoning and coding, and text-only; no vision or audio input. At $0.02 in / $0.03 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 NeMo (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 NeMo 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 NeMo 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 NeMo leans toward multilingual understanding across 11+ languages, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Llama 4 Scout or Mistral NeMo?
Llama 4 Scout is cheaper — Open weight (self-host / free) vs $0.02/$0.03 per 1M tokens.
Which has the bigger context window?
Llama 4 Scout — 10M vs 128K, about 76× 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 NeMo together?
Yes — a multi-model platform like LumiChats gives you Llama 4 Scout, Mistral NeMo 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 NeMo?
Llama 4 Scout — released April 2025, about 9 months after Mistral NeMo.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.