Context Window

Compare

Anthropic Claude vs Mistral Large 3

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

Anthropic Claude

Context window

100K

100,000 tokens · ~75K words

Model page
Mistral

Model

Mistral Large 3

Image inputTool calling

Context window

256K

256,000 tokens · ~192K 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.

Anthropic Claude100K
Mistral Large 3256K

Mistral Large 3 has about 2.6× the context window of the other in this pair.

Token calculator →Memory savings →

Mistral Large 3 has 156% more context capacity (256K vs 100K tokens). Mistral Large 3 is 93% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Mistral Large 3. Its 256K context fits entire documents without chunking (vs 100K).

  • RAG / high-volume retrieval

    Use Mistral Large 3. Input tokens are 93% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecAnthropic ClaudeMistral Large 3
Context window100,000 tokens (100K)256,000 tokens (256K)
Max output tokens8,191 tokens (8K)8,191 tokens (8K)
Speed tierBalancedDeep
VisionNoYes
Function callingNoYes
Extended thinkingNoNo
Prompt cachingNoNo
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.

ProviderAnthropic Claude inAnthropic Claude outMistral Large 3 inMistral Large 3 out
Aws Bedrock$8.00/M$24.00/M——
Azure——$0.500/M$1.50/M
Mistral——$0.500/M$1.50/M

Frequently asked questions

Mistral Large 3 has a larger context window: 256K tokens vs 100K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

Powered by Mem0

Use a smaller model.
Get better results.

Mem0 gives your AI long-term memory so you stop re-sending context on every call. That means you can use a smaller, faster, cheaper model — and still get better answers.

Calculate your savingsLearn about Mem0

Example: a multi-turn chat session

Without Mem0~128K tokens sent
Full history
Repeated info
Old context
With Mem0~20K tokens sent
Key memories
Current turn

80% less to send — works with any model