Context Window

Compare

Claude Sonnet 4 6 vs Codestral 2405

This page is context-first: how much text each model can take in one request. Full specs adds capabilities and limits; the pricing matrix below is only about $/million tokens from hosts that list both models.

Anthropic

Model

Claude Sonnet 4 6

Image inputTool calling

Context window

1M

1,000,000 tokens · ~750K words

Model page
Mistral

Model

Codestral 2405

Context window

32K

32,000 tokens · ~24K words

Model page

Context window · side by side

Bar length is relative to the larger of the two windows (100% = max of this pair). This is not pricing.

Claude Sonnet 4 61M
Codestral 240532K

Claude Sonnet 4 6 has about 31.3× the context window of the other in this pair.

Token calculator →Memory savings →

Claude Sonnet 4 6 has 3025% more context capacity (1000K vs 32K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Claude Sonnet 4 6. Its 1000K context fits entire documents without chunking (vs 32K).

  • Long output (reports, code files)

    Use Claude Sonnet 4 6. Its 64K max output lets you generate complete artifacts in one request.

Full specs

Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.

SpecClaude Sonnet 4 6Codestral 2405
Context window1,000,000 tokens (1000K)32,000 tokens (32K)
Max output tokens64,000 tokens (64K)8,191 tokens (8K)
Speed tierBalancedBalanced
VisionYesNo
Function callingYesNo
Extended thinkingYesNo
Prompt cachingYesNo
Batch APIYesNo
Release dateN/AN/A

Pricing matrix

Dollar rates only: hosts that list both models, per 1M tokens. For how much text fits, use the context section above — not this table.

ProviderClaude Sonnet 4 6 inClaude Sonnet 4 6 outCodestral 2405 inCodestral 2405 out
Anthropic$3.00/M$15.00/M——
Aws Bedrock$3.00/M$15.00/M——
Azure$3.00/M$15.00/M——
Google Vertex$3.00/M$15.00/M$0.200/M$0.600/M
Mistral————
Openrouter$3.00/M$15.00/M——

Frequently asked questions

Claude Sonnet 4 6 has a larger context window: 1000K tokens vs 32K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

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Example: a multi-turn chat session

Without Mem0~128K tokens sent
Full history
Repeated info
Old context
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