Compare
Mistral Large Latest vs Qwen3 32B
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.
Model
Mistral Large Latest
Context window
32K
32,000 tokens · ~24K words
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.
Qwen3 32B has about 1.3× the context window of the other in this pair.
Qwen3 32B has 28% more context capacity (40K vs 32K tokens). Qwen3 32B is 84% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Qwen3 32B. Its 40K context fits entire documents without chunking (vs 32K).
RAG / high-volume retrieval
Use Qwen3 32B. Input tokens are 84% cheaper — critical when sending large retrieved contexts.
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | Mistral Large Latest | Qwen3 32B |
|---|---|---|
| Context window | 32,000 tokens (32K) | 40,960 tokens (40K) |
| Max output tokens | N/A | 40,960 tokens (40K) |
| Speed tier | Deep | Balanced |
| Vision | No | No |
| Function calling | Yes | Yes |
| Extended thinking | No | Yes |
| Prompt caching | No | Yes |
| Batch API | No | No |
| Release date | N/A | Apr 2025 |
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.
| Provider | Mistral Large Latest in | Mistral Large Latest out | Qwen3 32B in | Qwen3 32B out |
|---|---|---|---|---|
| Azure | $8.00/M | $24.00/M | — | — |
| Deepinfra | — | — | $0.100/M | $0.280/M |
| Fireworks | — | — | $0.900/M | $0.900/M |
| Google Vertex | $2.00/M | $6.00/M | — | — |
| Groq | — | — | $0.290/M | $0.590/M |
| Mistral | $0.500/M | $1.50/M | — | — |
| Nebius | — | — | $0.100/M | $0.300/M |
| Ovhcloud | — | — | $0.080/M | $0.230/M |
| Sambanova | — | — | $0.400/M | $0.800/M |
Frequently asked questions
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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.
Example: a multi-turn chat session
80% less to send — works with any model