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Home/Mistral Small 3.1 vs Llama 4 Scout
Winner: Mistral Small 3.1Mistral vs Meta

Mistral Small 3.1 vs Llama 4 Scout

Mistral Small 3.1 wins on coding (55 vs 54) and writing quality and price ($0.1 vs $0.5/1M input). Llama 4 Scout wins on context window (512K vs 128K). For most workflows, Mistral Small 3.1 is the stronger default — ultra-cheap multimodal model for massive-volume, low-complexity pipelines.

Last verified Jun 26, 2026/Model data modified Jun 26, 2026
Rankings refresh dailyScored on 6 criteriaNo paid rankings
MistralBudget
Input cost
$0.35/1M
Context
128k tokens
Speed
Very fast

Clear recommendation block

The shortest way to see the safest default, the lower-cost option, and the specialist pick before you read deeper.

Best overall model

Mistral Small 3.1

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Why this recommendation

Mistral Small 3.1 is the safest overall answer here when you want the strongest default instead of the lowest list price.

MistralBudget
Best for
Ultra-high-volume classification, summarisation, and lightweight vision tasks
Price
$0.35/1M
Context
128k tokens
Best budget model

Mistral: Mistral Nemo

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Why this recommendation

Mistral: Mistral Nemo is the lower-cost option to start with when you still need useful output at scale.

MistralBudget
Best for
Teams needing a cheap, fast, multilingual workhorse for classification, summarization, or light coding tasks at scale.
Price
$0.02/1M
Context
131k tokens
Best for speed

Llama 4 Scout

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Why this recommendation

Llama 4 Scout is the better pick when response speed matters more than maximum reasoning depth.

MetaBudget
Best for
Affordable self-hosted long-context workflows and analysis pipelines
Price
$0.10/1M
Context
512k tokens

Why this page recommends it

Mistral Small 3.1 leads on coding with a score of 55 vs 54 for Llama 4 Scout.

Llama 4 Scout has the larger context window: 512K vs 128K for Mistral Small 3.1.

Mistral Small 3.1 is cheaper at $0.1/1M input tokens vs $0.5/1M for Llama 4 Scout.

Decision notes

Choose Mistral Small 3.1 for writing and budget — ultra-high-volume classification.

Choose Llama 4 Scout when affordable self-hosted long-context workflows and analysis pipelines.

Both models serve different primary workflows — consider using each where it has a clear edge.

Interactive decision lab

Test the recommendation against your priority

Switch the scoring lens to see whether the top answer changes when you care more about cost, speed, or long-document work.

#1Llama 4 Scout67 pts
#2Mistral Small 3.161 pts
Quality first

Llama 4 Scout

Meta / Budget / Jun 8, 2026

67

Best open-weight long-context option for self-hosted pipelines.

Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.

Cost
$0.10/1M
$0.30/1M out
Speed
Fast
4/100 score
Context
512k tokens
input window
View model
Data-backed recommendation
Avoid this pick if

You want a hosted solution — Gemini 3.1 Flash gives more context for roughly the same cost.

Recommended comparisons

The fastest way to see where the recommendation shifts when your priority changes.

MistralBudgetWinner: Mistral Small 3.1

Mistral Small 3.1

Ultra-cheap multimodal model for massive-volume, low-complexity pipelines.

Best use case
Ultra-high-volume classification, summarisation, and lightweight vision tasks
Input
$0.35/1M
Pricing
Budget
Speed
Very fast
Context
128k tokens
BudgetMultimodalUltra cheap
MetaBudgetOption 2

Llama 4 Scout

Best open-weight long-context option for self-hosted pipelines.

Best use case
Affordable self-hosted long-context workflows and analysis pipelines
Input
$0.10/1M
Pricing
Budget
Speed
Fast
Context
512k tokens
Long contextCheapOpen weights

Pros

One of the cheapest models in the directory at $0.10/1M input

Multimodal — handles images alongside text at this price point

Fast and efficient for simple, well-defined tasks

Cons

Weak on complex reasoning, hard coding, and nuanced writing

Not suitable for tasks requiring deep context retention or multi-step logic

Limited to simpler use cases compared to Codestral or DeepSeek V3

Explore related decisions

Mistral
Mistral Small 3.1Ultra-cheap multimodal model for massive-volume, low-complexity pipelines.Read guide
Meta
Llama 4 ScoutBest open-weight long-context option for self-hosted pipelines.Read guide
Guide
Best AI for WritingClaude leads AI writing quality in 2026. Compare Claude Opus 4.7, Sonnet 4.6, GPT-4o, and budget picks for long-form content, brand voice, and professional…Read guide
Guide
Best AI for ResearchClaude Opus 4.7 and Gemini 3.1 Pro lead AI research in 2026. Compare 1M-token context models, Perplexity for real-time search, and the best picks for document…Read guide
Tool
Compare models side by sidePick any two models and see pricing, benchmarks, and context windows in one table.Read guide
Pricing
AI API pricing comparisonInput and output cost per million tokens for every model, updated when providers change prices.Read guide

Quick links

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FAQ

Is Mistral Small 3.1 better than Llama 4 Scout?

Mistral Small 3.1 wins on more categories — writing, budget, multimodal. Llama 4 Scout is the better pick when affordable self-hosted long-context workflows and analysis pipelines. The right choice depends on your specific use case.

Which is cheaper — Mistral Small 3.1 or Llama 4 Scout?

Mistral Small 3.1 is cheaper at $0.1/1M input and $0.3/1M output. Llama 4 Scout costs $0.5/1M input and $1.2/1M output.

Which has a larger context window — Mistral Small 3.1 or Llama 4 Scout?

Llama 4 Scout has the larger context window at 512K tokens vs Mistral Small 3.1's 128K. For large document analysis, Llama 4 Scout is the stronger pick.

Is Mistral Small 3.1 or Llama 4 Scout better for coding?

Mistral Small 3.1 is better for coding with a score of 55 vs Llama 4 Scout's 54 (out of 100). Claude Fable 5 is the overall coding leader in this directory at 100/100.

Which is faster — Mistral Small 3.1 or Llama 4 Scout?

Mistral Small 3.1 is faster with a very fast speed rating (score: 5) vs Llama 4 Scout's fast rating (score: 4).