R1 Distill Llama 70B
DeepSeek · released Jan 23, 2025
DeepSeek R1 Distill Llama 70B is a distilled large language model based on [Llama-3.3-70B-Instruct](/meta-llama/llama-3.3-70b-instruct), using outputs from [DeepSeek R1](/deepseek/deepseek-r1). The model combines advanced distillation techniques to achieve high performance across...
Specification
- Context window
- 8K
- Max output
- 8K
- Knowledge cutoff
- Jul 31, 2024
- Parameters
- 70.6B
- Licence
- mit
- Serving providers
- 1
- Moderated
- No
- Uptime
- 100.0%
Intelligence
9.8
35th percentile
Coding
—
Coding Index
Agentic
—
Agentic Index
Output speed
29 t/s
Median across providers
Latency
609ms
Time to first token
Cost per task
$0.10
Estimated
Benchmarks
Where the score comes from
The Intelligence Index is a composite. These are the underlying evaluations this model was actually measured on.
Evaluation scores
Percentage correct · higher is better
- MMLU-Pro79.5%
- AIME 202553.7%
- GPQA Diamond40.2%
- SciCode31.3%
- IFBench27.6%
- LiveCodeBench26.6%
- τ²-bench (Telecom)21.9%
- AA-LCR (long context)9.3%
- Humanity's Last Exam5.1%
- Terminal-Bench Hard1.5%
An evaluation missing from this list was not run for this model — it is not a zero.
View as table
| Evaluation | Score |
|---|---|
| MMLU-Pro | 79.5% |
| AIME 2025 | 53.7% |
| GPQA Diamond | 40.2% |
| SciCode | 31.3% |
| IFBench | 27.6% |
| LiveCodeBench | 26.6% |
| τ²-bench (Telecom) | 21.9% |
| AA-LCR (long context) | 9.3% |
| Humanity's Last Exam | 5.1% |
| Terminal-Bench Hard | 1.5% |
Against its peers
Intelligence Index · this model highlighted, nearest peers in grey
- Llama 4 ScoutMeta10.3
- 9.9
- R1 Distill Llama 70BDeepSeek9.8
- GPT-4.1 NanoOpenAI9.6
- SonarPerplexity9.4
- 9.4
- GPT-4o (2024-08-06)OpenAI9.4
- Sonar ProPerplexity9.1
Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.
View as table
| Model | Intelligence |
|---|---|
| Llama 4 Scout | 10.3 |
| Qwen3 VL 30B A3B Instruct | 9.9 |
| R1 Distill Llama 70B | 9.8 |
| GPT-4.1 Nano | 9.6 |
| Sonar | 9.4 |
| Qwen2.5 72B Instruct | 9.4 |
| GPT-4o (2024-08-06) | 9.4 |
| Sonar Pro | 9.1 |
Percentile among all indexed models
Pricing
What it costs to run
List prices per million tokens, plus what one representative task works out to.
List price
- Input / 1M tokens
- $0.8
- Output / 1M tokens
- $0.8
- Cached input / 1M
- Not offered
- Blended 3:1
- $0.8
One task, estimated
$0.10
- Input tokens
- 50,000
- Output tokens
- 80,000
- Profile
- Reasoning
Estimated from list pricing: 50K input tokens plus 80K output tokens for reasoning models (25K for non-reasoning).
Serving providers
Speed and latency figures are medians across these providers, so a widely-served model reports a blend rather than any single endpoint.