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Llama 4 Scout vs Mistral Large 2

How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for Llama 4 Scout and Mistral Large 2.

Meta

Llama 4 Scout

Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input...

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Mistral

Mistral Large 2

Mistral Large 2 is Mistral AI's second-generation flagship commercial model, delivering substantial improvements over the original Mistral Large in reasoning, tool use, multilingual capabilities, and instruction following. With a 1-million-token context window, it processes entire codebases and lengthy documents in a single pass. Mistral Large 2 excels at complex agentic workflows, structured JSON output, and native function calling — making it one of the top choices for European enterprises requiring GDPR-compliant AI infrastructure.

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Technical Specifications

SpecificationLlama 4 ScoutMistral Large 2
ProviderMetaMistral
Context Window10,000,000 tokens1,048,576 tokens
Agent Suitability82/10093/100
Time to First Token (TTFT)350 ms270 ms
Deployment Modelself hostablemanaged api
Production Stabilitybetastable
API AvailableYesYes
Released Date2025-04-052025-07-24

API Pricing Comparison

Input Price per Million Tokens

Llama 4 Scout

$0.10

Mistral Large 2

$0.60

Output Price per Million Tokens

Llama 4 Scout

$0.30

Mistral Large 2

$1.80

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Benchmark Performance Metrics

Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.

MMLUGeneral knowledge & multi-task understanding
8720.0%vsN/A
Llama 4 Scout
Mistral Large 2
HumanEvalPython coding & logic synthesis
8950.0%vsN/A
Llama 4 Scout
Mistral Large 2
MATHComplex mathematical problem solving
8100.0%vsN/A
Llama 4 Scout
Mistral Large 2
GPQAGraduate-level expert reasoning
6680.0%vsN/A
Llama 4 Scout
Mistral Large 2
HellaSwagCommonsense reasoning and inference
9450.0%vsN/A
Llama 4 Scout
Mistral Large 2
MT-BenchMulti-turn conversation flow quality
910.0%vsN/A
Llama 4 Scout
Mistral Large 2

Llama 4 Scout Quirks & Gotchas

  • ▸10M context causes significant VRAM pressure — recommend 4-bit quantization
  • ▸Primarily designed for RAG, not agentic tool calling

Mistral Large 2 Quirks & Gotchas

  • ▸Excellent European data sovereignty — GDPR-compliant infrastructure
  • ▸1M context window enables full codebase analysis in a single pass