Compare
Gpt Audio 2025 08 28 vs Meta Llama 3 8b
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.
Gpt Audio 2025 08 28 has about 15.6× the context window of the other in this pair.
Gpt Audio 2025 08 28 has 1462% more context capacity (128K vs 8K tokens). Meta Llama 3 8b is 98% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Gpt Audio 2025 08 28. Its 128K context fits entire documents without chunking (vs 8K).
RAG / high-volume retrieval
Use Meta Llama 3 8b. Input tokens are 98% cheaper — critical when sending large retrieved contexts.
Long output (reports, code files)
Use Gpt Audio 2025 08 28. Its 16K 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 | Gpt Audio 2025 08 28 | Meta Llama 3 8b |
|---|---|---|
| Context window | 128,000 tokens (128K) | 8,192 tokens (8K) |
| Max output tokens | 16,384 tokens (16K) | 8,192 tokens (8K) |
| Speed tier | Balanced | Fast |
| Vision | No | No |
| Function calling | Yes | 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 | Gpt Audio 2025 08 28 in | Gpt Audio 2025 08 28 out | Meta Llama 3 8b in | Meta Llama 3 8b out |
|---|---|---|---|---|
| Anyscale | — | — | $0.150/M | $0.150/M |
| Azure | $2.50/M | $10.00/M | — | — |
| Deepinfra | — | — | $0.030/M | $0.060/M |
| Openai | $2.50/M | $10.00/M | — | — |
Frequently asked questions
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Use a smaller model.
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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