Context Window

Compare

Mistral Large Latest vs Qwen2p5 0p5b

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 Latest

Tool calling

Context window

32K

32,000 tokens · ~24K words

Model page
Alibaba

Model

Qwen2p5 0p5b

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 Large Latest32K
Qwen2p5 0p5b33K

Qwen2p5 0p5b has about 1× the context window of the other in this pair.

Token calculator →Memory savings →

Qwen2p5 0p5b has 2% more context capacity (32K vs 32K tokens). Qwen2p5 0p5b is 80% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Qwen2p5 0p5b. Its 32K context fits entire documents without chunking (vs 32K).

  • RAG / high-volume retrieval

    Use Qwen2p5 0p5b. Input tokens are 80% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecMistral Large LatestQwen2p5 0p5b
Context window32,000 tokens (32K)32,768 tokens (32K)
Max output tokensN/A32,768 tokens (32K)
Speed tierDeepBalanced
VisionNoNo
Function callingYesNo
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoNo
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.

ProviderMistral Large Latest inMistral Large Latest outQwen2p5 0p5b inQwen2p5 0p5b out
Azure$8.00/M$24.00/M——
Fireworks——$0.100/M$0.100/M
Google Vertex$2.00/M$6.00/M——
Mistral$0.500/M$1.50/M——

Frequently asked questions

Qwen2p5 0p5b 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.

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