latentbrief

Model comparison

Claude Sonnet 4.6 vs Mistral Large

Claude Sonnet 4.6 supports multimodal inputs and has an extremely large token context, while Mistral Large emphasizes cost efficiency and open accessibility within a smaller context window.

Specs

MetricClaude Sonnet 4.6Mistral Large
Context window1M tokens128K tokens
Input $/1M tokens$3.00$2.00
Output $/1M tokens$15.00$6.00
ModalitiesText · ImageText
Open weightsNoNo

Capability differences

CapabilityClaude Sonnet 4.6Mistral Large
VisionYesNo
Extended thinkingYesNo
Prompt cachingYesNo

How they differ

Context handling

Claude Sonnet 4.6

Claude Sonnet 4.6 supports a 1,000,000 token context, allowing for processing exceptionally large text spans.

Mistral Large

Mistral Large has a 128,000 token context, balancing performance and scalability for moderately sized inputs.

Cost profile

Claude Sonnet 4.6

Claude Sonnet 4.6 has higher token costs, at $3.0/1M input and $15.0/1M output.

Mistral Large

Mistral Large is more cost-effective, with costs at $2.0/1M input and $6.0/1M output.

Vision

Claude Sonnet 4.6

Claude Sonnet 4.6 supports multimodal input, processing both text and images.

Mistral Large

Mistral Large processes text-only inputs without multimodal capabilities.

Open weights

Claude Sonnet 4.6

Claude Sonnet 4.6 does not provide open weights, limiting user transparency and customization.

Mistral Large

Mistral Large offers open weights, enabling developers to modify and deploy the model independently.

Claude Sonnet 4.6 — what sets it apart

  • +Supports multimodal inputs, including images.
  • +Handles up to 1,000,000 tokens, far exceeding typical model limits.

Mistral Large — what sets it apart

  • +Offers open-source weights for customization and independent deployment.
  • +Focuses on affordability with lower token costs.

The most consequential difference lies in Claude Sonnet 4.6's larger token context and multimodal capabilities versus Mistral Large's cost-efficiency and open accessibility.

Analysis synthesized from gpt-4o, llama-4-maverick, phi-4, etc.