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R1 Distill Llama 70B vs Gemma 3 4B
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.
R1 Distill Llama 70B and Gemma 3 4B have identical context windows (131K tokens). Gemma 3 4B is 86% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
RAG / high-volume retrieval
Use Gemma 3 4B. Input tokens are 86% 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 | R1 Distill Llama 70B | Gemma 3 4B |
|---|---|---|
| Context window | 131,072 tokens (131K) | 131,072 tokens (131K) |
| Max output tokens | 131,072 tokens (131K) | 131,072 tokens (131K) |
| Speed tier | Deep | Balanced |
| Vision | No | Yes |
| Function calling | No | Yes |
| Extended thinking | Yes | No |
| Prompt caching | No | Yes |
| Batch API | No | No |
| Release date | Jan 2025 | Mar 2025 |
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 | Gemma 3 4B in | Gemma 3 4B out |
|---|---|---|---|---|
| Deepinfra | $0.200/M | $0.600/M | $0.050/M | $0.100/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 | — | — |
| Openrouter | $0.800/M | $0.800/M | $0.050/M | $0.100/M |
| Ovhcloud | $0.670/M | $0.670/M | — | — |
| Sambanova | $0.700/M | $1.40/M | — | — |
| Together Ai | $2.00/M | $2.00/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