Compare / DeepSeek-R1-Distill-Llama-70B vs Llama 3.3 70B
DeepSeek-R1-Distill-Llama-70B vs Llama 3.3 70B
Updated November 2026
Verdict
DeepSeek-R1-Distill-Llama-70B scores higher on the Small LM Index: 50.1 vs 40.8, although their likely ranges overlap, so the lead is not certain. On the 1 benchmark both labs report, DeepSeek-R1-Distill-Llama-70B wins 1 and Llama 3.3 70B wins 0. Both need about 47.4 GB of memory at 4-bit quantization. Only Llama 3.3 70B has a hosted API on OpenRouter ($0.15 per million tokens); the other has to be self-hosted.
Pick DeepSeek-R1-Distill-Llama-70B if…
- you want the strongest model overall
- you need a permissive license (MIT)
Pick Llama 3.3 70B if…
- you want a hosted API rather than running it yourself
- you want the more widely used model (more quantized builds and community support)
Specifications
| DeepSeek-R1-Distill-Llama-70B | Llama 3.3 70B | |
|---|---|---|
| Small LM Index | 50.1 (likely 45–55) | 40.8 (likely 31–51) |
| Lab | DeepSeek · China | Meta · United States |
| Released | 2025-01-20 | 2024-12-06 |
| Parameters | 70.6B (dense) | 70.6B (dense) |
| Memory at 4-bit | ~47.4 GB | ~47.4 GB |
| Context window | 128K tokens | 128K tokens |
| Input | text | text |
| Reasoning | Always thinks | No thinking |
| License | MIT | Llama 3.3 Community |
| API price / 1M tokens | Self-host only | $0.15 ($0.10 in, $0.32 out) |
| Hugging Face downloads (30 days) | 30.5K | 363.3K |
Benchmarks
Higher is better. Bold marks the winner where both labs report a score. ≈ marks the index model's estimate for a score that wasn't reported, ✓ an independent measurement and * a score measured by another lab.
| Benchmark | DeepSeek-R1-Distill-Llama-70B | Llama 3.3 70B |
|---|---|---|
| MMLU-Pro Knowledge | ≈ 72 | 68.9 |
| GPQA Diamond Knowledge | 65.2 | 50.5 |
| AIME Math | 70.0 | ≈ 54 |
| LiveCodeBench Coding | 57.5 | ≈ 45 |
Scores are mostly reported by each lab with its own settings, so small gaps are noise. See the methodology. Open in the interactive comparison to add more models.
Questions
Which is better, DeepSeek-R1-Distill-Llama-70B or Llama 3.3 70B?
DeepSeek-R1-Distill-Llama-70B scores higher on the Small LM Index: 50.1 vs 40.8, although their likely ranges overlap, so the lead is not certain. On the 1 benchmark both labs report, DeepSeek-R1-Distill-Llama-70B wins 1 and Llama 3.3 70B wins 0.
Which is cheaper to run?
Only Llama 3.3 70B has a hosted API on OpenRouter ($0.15 per million tokens); the other has to be self-hosted. Both need about 47.4 GB of memory at 4-bit quantization.
Which has the longer context window?
Both have a 128K-token context window.
Can I use DeepSeek-R1-Distill-Llama-70B and Llama 3.3 70B commercially?
DeepSeek-R1-Distill-Llama-70B is released under MIT and Llama 3.3 70B under Llama 3.3 Community. Apache 2.0 and MIT allow commercial use; other licenses can add conditions, so read the license before shipping a product.
Related comparisons
- DeepSeek-R1-Distill-Llama-70B vs Nemotron 3.5 Bolt 74B-A9B
- DeepSeek-R1-Distill-Llama-70B vs Kolibri-1
- DeepSeek-R1-Distill-Llama-70B vs Qwen3-Next 80B-A3B Thinking
- DeepSeek-R1-Distill-Llama-70B vs K2-V2 Instruct
- Llama 3.3 70B vs Nemotron 3.5 Bolt 74B-A9B
- Kolibri-1 vs Llama 3.3 70B
- Llama 3.3 70B vs Qwen3-Next 80B-A3B Thinking
- K2-V2 Instruct vs Llama 3.3 70B