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Mistral 7b V0 1 vs Qwen2p5 Coder 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.
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.
Same context window size for both models.
Mistral 7b V0 1 and Qwen2p5 Coder 32b have identical context windows (4K tokens). Mistral 7b V0 1 is 94% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
RAG / high-volume retrieval
Use Mistral 7b V0 1. Input tokens are 94% 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 7b V0 1 | Qwen2p5 Coder 32b |
|---|---|---|
| Context window | 4,096 tokens (4K) | 4,096 tokens (4K) |
| Max output tokens | 4,096 tokens (4K) | 4,096 tokens (4K) |
| Speed tier | Fast | Balanced |
| Vision | No | No |
| Function calling | No | No |
| Extended thinking | No | No |
| Prompt caching | No | No |
| Batch API | No | No |
| Release date | N/A | N/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.
| Provider | Mistral 7b V0 1 in | Mistral 7b V0 1 out | Qwen2p5 Coder 32b in | Qwen2p5 Coder 32b out |
|---|---|---|---|---|
| Fireworks | — | — | $0.900/M | $0.900/M |
| Replicate | $0.050/M | $0.250/M | — | — |
Frequently asked questions
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.
Example: a multi-turn chat session
80% less to send — works with any model