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Gpt 5 2 Chat Latest vs Meta Llama 3 1 405b
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
Gpt 5 2 Chat Latest
Context window
128K
128,000 tokens · ~96K 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.
Gpt 5 2 Chat Latest and Meta Llama 3 1 405b have identical context windows (128K tokens). Meta Llama 3 1 405b is 93% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
RAG / high-volume retrieval
Use Meta Llama 3 1 405b. Input tokens are 93% cheaper — critical when sending large retrieved contexts.
Long output (reports, code files)
Use Gpt 5 2 Chat Latest. 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 5 2 Chat Latest | Meta Llama 3 1 405b |
|---|---|---|
| Context window | 128,000 tokens (128K) | 128,000 tokens (128K) |
| Max output tokens | 16,384 tokens (16K) | 2,048 tokens (2K) |
| Speed tier | Balanced | Deep |
| Vision | Yes | No |
| Function calling | Yes | No |
| Extended thinking | Yes | No |
| Prompt caching | Yes | 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 5 2 Chat Latest in | Gpt 5 2 Chat Latest out | Meta Llama 3 1 405b in | Meta Llama 3 1 405b out |
|---|---|---|---|---|
| Azure | — | — | $5.33/M | $16.00/M |
| Hyperbolic | — | — | $0.120/M | $0.300/M |
| Nebius | — | — | $1.00/M | $3.00/M |
| Openai | $1.75/M | $14.00/M | — | — |
| Sambanova | — | — | $5.00/M | $10.00/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