Context Window

Compare

R1 Distill Qwen 32B vs Mistral Large Latest

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.

Alibaba

Model

R1 Distill Qwen 32B

Tool calling

Context window

131K

131,072 tokens · ~98K words

Model page
Mistral

Model

Mistral Large Latest

Tool calling

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.

R1 Distill Qwen 32B131K
Mistral Large Latest32K

R1 Distill Qwen 32B has about 4.1× the context window of the other in this pair.

Token calculator →Memory savings →

R1 Distill Qwen 32B has 309% more context capacity (131K vs 32K tokens). R1 Distill Qwen 32B is 70% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use R1 Distill Qwen 32B. Its 131K context fits entire documents without chunking (vs 32K).

  • RAG / high-volume retrieval

    Use R1 Distill Qwen 32B. Input tokens are 70% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecR1 Distill Qwen 32BMistral Large Latest
Context window131,072 tokens (131K)32,000 tokens (32K)
Max output tokens131,072 tokens (131K)N/A
Speed tierDeepDeep
VisionNoNo
Function callingYesYes
Extended thinkingYesNo
Prompt cachingNoNo
Batch APINoNo
Release dateJan 2025N/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.

ProviderR1 Distill Qwen 32B inR1 Distill Qwen 32B outMistral Large Latest inMistral Large Latest out
Azure——$8.00/M$24.00/M
Deepinfra$0.270/M$0.270/M——
Fireworks$0.900/M$0.900/M——
Google Vertex——$2.00/M$6.00/M
Mistral——$0.500/M$1.50/M
Novita$0.300/M$0.300/M——
Nscale$0.150/M$0.150/M——

Frequently asked questions

R1 Distill Qwen 32B has a larger context window: 131K 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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Use a smaller model.
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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