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Llama V3p2 90b Vision vs Mixtral 8x7b Instruct V0 1
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
Mixtral 8x7b Instruct V0 1
Context window
16K
16,384 tokens · ~12K 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.
Llama V3p2 90b Vision and Mixtral 8x7b Instruct V0 1 have identical context windows (16K tokens). Mixtral 8x7b Instruct V0 1 is 83% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
RAG / high-volume retrieval
Use Mixtral 8x7b Instruct V0 1. Input tokens are 83% 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 | Llama V3p2 90b Vision | Mixtral 8x7b Instruct V0 1 |
|---|---|---|
| Context window | 16,384 tokens (16K) | 16,384 tokens (16K) |
| Max output tokens | 16,384 tokens (16K) | 16,384 tokens (16K) |
| Speed tier | Balanced | Fast |
| Vision | Yes | 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 | Llama V3p2 90b Vision in | Llama V3p2 90b Vision out | Mixtral 8x7b Instruct V0 1 in | Mixtral 8x7b Instruct V0 1 out |
|---|---|---|---|---|
| Anyscale | — | — | $0.150/M | $0.150/M |
| Deepinfra | — | — | $0.400/M | $0.400/M |
| Fireworks | $0.900/M | $0.900/M | — | — |
| Ovhcloud | — | — | $0.630/M | $0.630/M |
| Replicate | — | — | $0.300/M | $1.00/M |
| Together Ai | — | — | $0.600/M | $0.600/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