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Llama 2 70b Chat vs Meta Llama2 13b Chat
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 2 70b Chat and Meta Llama2 13b Chat have identical context windows (4K tokens). Meta Llama2 13b Chat is 25% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
RAG / high-volume retrieval
Use Meta Llama2 13b Chat. Input tokens are 25% 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 2 70b Chat | Meta Llama2 13b Chat |
|---|---|---|
| Context window | 4,096 tokens (4K) | 4,096 tokens (4K) |
| Max output tokens | 4,096 tokens (4K) | 4,096 tokens (4K) |
| Speed tier | Deep | 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 | Llama 2 70b Chat in | Llama 2 70b Chat out | Meta Llama2 13b Chat in | Meta Llama2 13b Chat out |
|---|---|---|---|---|
| Anyscale | $1.00/M | $1.00/M | — | — |
| Aws Bedrock | — | — | $0.750/M | $1.00/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