Model comparison
DeepSeek-R1-Distill-Llama-70B vs Olmo 7b Instruct
DeepSeek-R1-Distill-Llama-70B is the stronger model overall, scoring 37.8 to 30.3 on the Noometry Index.
Last verified . 0 shared benchmarks.
Summary
- The widest gap is in writing & preference, where DeepSeek-R1-Distill-Llama-70B leads 49.0 to 25.8.
Side by side
| DeepSeek-R1-Distill-Llama-70B | Olmo 7b Instruct | |
|---|---|---|
| Provider | DeepSeek | Allen Institute for AI (Ai2) |
| Noometry Index | 37.8 | 30.3 |
| Released | 2025-01-20 | — |
| Weights | Open | Open |
| Context window | — | — |
| Max output | — | — |
| Input $ / M tokens | — | — |
| Output $ / M tokens | — | — |
| Results tracked | 13 | 10 |
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Category by category
Coding DeepSeek-R1-Distill-Llama-70B leads
DeepSeek-R1-Distill-Llama-70B: 36.8 (#202), Olmo 7b Instruct: 29.6 (#303)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | Olmo 7b Instruct |
|---|---|---|
| BigCodeBench Instruct | 35.3% | — |
| LiveBench Coding | 51.6% | — |
| LMArena Coding | — | 1016 |
| BigCodeBench Complete | 49.9% | — |
Reasoning DeepSeek-R1-Distill-Llama-70B leads
DeepSeek-R1-Distill-Llama-70B: 24.9 (#156), Olmo 7b Instruct: 18.8 (#274)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | Olmo 7b Instruct |
|---|---|---|
| Kagi LLM Benchmark | 52.3% | — |
| LiveBench Reasoning | 67.6% | — |
| LMArena Hard Prompts | — | 993 |
| LiveBench Data Analysis | 55.9% | — |
| LiveBench | 54.5% | — |
Math DeepSeek-R1-Distill-Llama-70B leads
DeepSeek-R1-Distill-Llama-70B: 36.0 (#176), Olmo 7b Instruct: 30.2 (#237)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | Olmo 7b Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 51.4% | — |
| LiveBench Math | 58.1% | — |
| LMArena Math | — | 1018 |
| MATH Level 5 | 89.9% | — |
Knowledge Not comparable
DeepSeek-R1-Distill-Llama-70B: 30.7 (#225), Olmo 7b Instruct: —
| Benchmark | DeepSeek-R1-Distill-Llama-70B | Olmo 7b Instruct |
|---|---|---|
| GPQA Diamond | 55.7% | — |
Multilingual Not comparable
DeepSeek-R1-Distill-Llama-70B: —, Olmo 7b Instruct: 24.0 (#291)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | Olmo 7b Instruct |
|---|---|---|
| LMArena Non-English | — | 977 |
| LMArena Chinese | — | 1014 |
| LMArena Russian | — | 947 |
Instruction Following DeepSeek-R1-Distill-Llama-70B leads
DeepSeek-R1-Distill-Llama-70B: 68.2 (#190), Olmo 7b Instruct: 49.0 (#301)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | Olmo 7b Instruct |
|---|---|---|
| LiveBench Instruction Following | 69.9% | — |
| LMArena Instruction Following | — | 978 |
Writing & Preference DeepSeek-R1-Distill-Llama-70B leads
DeepSeek-R1-Distill-Llama-70B: 49.0 (#194), Olmo 7b Instruct: 25.8 (#303)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | Olmo 7b Instruct |
|---|---|---|
| LMArena Text | — | 1032 |
| LMArena Creative Writing | — | 990 |
| LMArena Multi-Turn | — | 1007 |
| LiveBench Language | 23.8% | — |
Frequently asked questions
Is DeepSeek-R1-Distill-Llama-70B better than Olmo 7b Instruct?
DeepSeek-R1-Distill-Llama-70B is the stronger model overall, scoring 37.8 to 30.3 on the Noometry Index.
Is DeepSeek-R1-Distill-Llama-70B or Olmo 7b Instruct better for coding?
DeepSeek-R1-Distill-Llama-70B scores higher on coding benchmarks: 36.8 versus 29.6 in the Noometry coding category.
How many benchmarks do DeepSeek-R1-Distill-Llama-70B and Olmo 7b Instruct share?
0 benchmarks have published results for both models. DeepSeek-R1-Distill-Llama-70B has 13 scored results on Noometry and Olmo 7b Instruct has 10.