Compare
Claude Instant vs Mistral Devstral 2 123b
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 Devstral 2 123b
Context window
256K
256,000 tokens · ~192K 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.
Mistral Devstral 2 123b has about 2.6× the context window of the other in this pair.
Mistral Devstral 2 123b has 156% more context capacity (256K vs 100K tokens). Mistral Devstral 2 123b is 50% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Mistral Devstral 2 123b. Its 256K context fits entire documents without chunking (vs 100K).
RAG / high-volume retrieval
Use Mistral Devstral 2 123b. Input tokens are 50% cheaper — critical when sending large retrieved contexts.
Long output (reports, code files)
Use Mistral Devstral 2 123b. Its 8K 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 | Claude Instant | Mistral Devstral 2 123b |
|---|---|---|
| Context window | 100,000 tokens (100K) | 256,000 tokens (256K) |
| Max output tokens | 8,191 tokens (8K) | 8,192 tokens (8K) |
| Speed tier | Balanced | Fast |
| Vision | No | No |
| Function calling | No | Yes |
| Extended thinking | No | No |
| Prompt caching | No | No |
| Batch API | Yes | 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 | Claude Instant in | Claude Instant out | Mistral Devstral 2 123b in | Mistral Devstral 2 123b out |
|---|---|---|---|---|
| Aws Bedrock | $0.800/M | $2.40/M | $0.400/M | $2.00/M |
Frequently asked questions
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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