Compare
R1 Distill Llama 70B vs Llama 2 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.
R1 Distill Llama 70B has about 32× the context window of the other in this pair.
R1 Distill Llama 70B has 3100% more context capacity (131K vs 4K tokens). Llama 2 13b Chat is 33% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use R1 Distill Llama 70B. Its 131K context fits entire documents without chunking (vs 4K).
RAG / high-volume retrieval
Use Llama 2 13b Chat. Input tokens are 33% cheaper — critical when sending large retrieved contexts.
Long output (reports, code files)
Use R1 Distill Llama 70B. Its 131K 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 | R1 Distill Llama 70B | Llama 2 13b Chat |
|---|---|---|
| Context window | 131,072 tokens (131K) | 4,096 tokens (4K) |
| Max output tokens | 131,072 tokens (131K) | 4,096 tokens (4K) |
| Speed tier | Deep | Fast |
| Vision | No | No |
| Function calling | No | No |
| Extended thinking | Yes | No |
| Prompt caching | No | No |
| Batch API | No | No |
| Release date | Jan 2025 | 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 | R1 Distill Llama 70B in | R1 Distill Llama 70B out | Llama 2 13b Chat in | Llama 2 13b Chat out |
|---|---|---|---|---|
| Anyscale | — | — | $0.250/M | $0.250/M |
| Deepinfra | $0.200/M | $0.600/M | — | — |
| Fireworks | $0.900/M | $0.900/M | — | — |
| Gradient | $0.990/M | $0.990/M | — | — |
| Nebius | $0.250/M | $0.750/M | — | — |
| Novita | $0.800/M | $0.800/M | — | — |
| Nscale | $0.375/M | $0.375/M | — | — |
| Ovhcloud | $0.670/M | $0.670/M | — | — |
| Sambanova | $0.700/M | $1.40/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