Context Window

Compare

Mistral Large vs Qwen2 5 7b

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.

Mistral

Model

Mistral Large

Tool calling

Context window

32K

32,000 tokens · ~24K words

Model page
Alibaba

Model

Qwen2 5 7b

Context window

33K

32,768 tokens · ~25K 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.

Mistral Large32K
Qwen2 5 7b33K

Qwen2 5 7b has about 1× the context window of the other in this pair.

Token calculator →Memory savings →

Qwen2 5 7b has 2% more context capacity (32K vs 32K tokens). Qwen2 5 7b is 97% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Qwen2 5 7b. Its 32K context fits entire documents without chunking (vs 32K).

  • RAG / high-volume retrieval

    Use Qwen2 5 7b. Input tokens are 97% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Qwen2 5 7b. Its 32K 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.

SpecMistral LargeQwen2 5 7b
Context window32,000 tokens (32K)32,768 tokens (32K)
Max output tokens8,191 tokens (8K)32,768 tokens (32K)
Speed tierDeepFast
VisionNoNo
Function callingYesNo
Extended thinkingNoNo
Prompt cachingYesNo
Batch APINoNo
Release dateFeb 2024N/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.

ProviderMistral Large inMistral Large outQwen2 5 7b inQwen2 5 7b out
Azure$4.00/M$12.00/M——
Deepinfra——$0.040/M$0.100/M
Ibm Watsonx$3.00/M$10.00/M——
Novita——$0.070/M$0.070/M
Openrouter$8.00/M$24.00/M——
Snowflake————

Frequently asked questions

Qwen2 5 7b has a larger context window: 32K 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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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.

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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