Compare
Meta Llama 3 1 8b vs Mistral Mixtral 8x7b Instruct
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 Mixtral 8x7b Instruct
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.
Meta Llama 3 1 8b has about 4× the context window of the other in this pair.
Meta Llama 3 1 8b has 300% more context capacity (128K vs 32K tokens). Meta Llama 3 1 8b is 94% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Meta Llama 3 1 8b. Its 128K context fits entire documents without chunking (vs 32K).
RAG / high-volume retrieval
Use Meta Llama 3 1 8b. Input tokens are 94% cheaper — critical when sending large retrieved contexts.
Long output (reports, code files)
Use Mistral Mixtral 8x7b Instruct. 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 | Meta Llama 3 1 8b | Mistral Mixtral 8x7b Instruct |
|---|---|---|
| Context window | 128,000 tokens (128K) | 32,000 tokens (32K) |
| Max output tokens | 2,048 tokens (2K) | 8,191 tokens (8K) |
| Speed tier | Fast | Fast |
| 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 | Meta Llama 3 1 8b in | Meta Llama 3 1 8b out | Mistral Mixtral 8x7b Instruct in | Mistral Mixtral 8x7b Instruct out |
|---|---|---|---|---|
| Aws Bedrock | — | — | $0.590/M | $0.910/M |
| Azure | $0.300/M | $0.610/M | — | — |
| Deepinfra | $0.030/M | $0.050/M | — | — |
| Friendliai | $0.100/M | $0.100/M | — | — |
| Hyperbolic | $0.120/M | $0.300/M | — | — |
| Nebius | $0.020/M | $0.060/M | — | — |
| Sambanova | $0.100/M | $0.200/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