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Llama V3p1 8b vs Llama V3p2 90b Vision
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.
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 V3p1 8b and Llama V3p2 90b Vision have identical context windows (16K tokens). Llama V3p1 8b is 88% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
RAG / high-volume retrieval
Use Llama V3p1 8b. Input tokens are 88% 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 V3p1 8b | Llama V3p2 90b Vision |
|---|---|---|
| Context window | 16,384 tokens (16K) | 16,384 tokens (16K) |
| Max output tokens | 16,384 tokens (16K) | 16,384 tokens (16K) |
| Speed tier | Fast | Balanced |
| Vision | No | Yes |
| 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 | Llama V3p1 8b in | Llama V3p1 8b out | Llama V3p2 90b Vision in | Llama V3p2 90b Vision out |
|---|---|---|---|---|
| Fireworks | $0.100/M | $0.100/M | $0.900/M | $0.900/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