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Mistral Small 3 1 24b Instruct 2503 vs Open Mistral 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.
Model
Mistral Small 3 1 24b Instruct 2503
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.
Same context window size for both models.
Mistral Small 3 1 24b Instruct 2503 and Open Mistral 7b have identical context windows (32K tokens). Mistral Small 3 1 24b Instruct 2503 is 60% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
RAG / high-volume retrieval
Use Mistral Small 3 1 24b Instruct 2503. Input tokens are 60% cheaper — critical when sending large retrieved contexts.
Long output (reports, code files)
Use Mistral Small 3 1 24b Instruct 2503. 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.
| Spec | Mistral Small 3 1 24b Instruct 2503 | Open Mistral 7b |
|---|---|---|
| Context window | 32,000 tokens (32K) | 32,000 tokens (32K) |
| Max output tokens | 32,000 tokens (32K) | 8,191 tokens (8K) |
| Speed tier | Balanced | Fast |
| Vision | No | No |
| Function calling | Yes | 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 Small 3 1 24b Instruct 2503 in | Mistral Small 3 1 24b Instruct 2503 out | Open Mistral 7b in | Open Mistral 7b out |
|---|---|---|---|---|
| Ibm Watsonx | $0.100/M | $0.300/M | — | — |
| Mistral | — | — | $0.250/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