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Jamba 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.
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.
Jamba has about 2.2× the context window of the other in this pair.
Jamba has 118% more context capacity (70K vs 32K tokens). Mistral Large Latest is 0% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Jamba. Its 70K context fits entire documents without chunking (vs 32K).
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | Jamba | Mistral Large Latest |
|---|---|---|
| Context window | 70,000 tokens (70K) | 32,000 tokens (32K) |
| Max output tokens | 4,096 tokens (4K) | N/A |
| Speed tier | Balanced | Deep |
| Vision | No | No |
| Function calling | No | Yes |
| 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 | Jamba in | Jamba out | Mistral Large Latest in | Mistral Large Latest out |
|---|---|---|---|---|
| Azure | $0.500/M | $0.700/M | $8.00/M | $24.00/M |
| Google Vertex | — | — | $2.00/M | $6.00/M |
| Mistral | — | — | $0.500/M | $1.50/M |
| Snowflake | — | — | — | — |
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