Model comparison
DeepSeek-R1-Distill-Llama-70B vs Olmo 2 0325 32b Instruct
DeepSeek-R1-Distill-Llama-70B is the stronger model overall, scoring 37.8 to 32.7 on the Noometry Index.
Last verified . 0 shared benchmarks.
DeepSeek
Allen Institute for AI (Ai2)Summary
- The widest gap is in knowledge, where DeepSeek-R1-Distill-Llama-70B leads 30.7 to 19.5.
Side by side
| DeepSeek-R1-Distill-Llama-70B | Olmo 2 0325 32b Instruct | |
|---|---|---|
| Provider | DeepSeek | Allen Institute for AI (Ai2) |
| Noometry Index | 37.8 | 32.7 |
| Released | 2025-01-20 | — |
| Weights | Open | Open |
| Context window | — | — |
| Max output | — | — |
| Input $ / M tokens | — | — |
| Output $ / M tokens | — | — |
| Results tracked | 13 | 16 |
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Category by category
Coding DeepSeek-R1-Distill-Llama-70B leads
DeepSeek-R1-Distill-Llama-70B: 36.8 (#202), Olmo 2 0325 32b Instruct: 35.2 (#227)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | Olmo 2 0325 32b Instruct |
|---|---|---|
| BigCodeBench Instruct | 35.3% | — |
| LiveBench Coding | 51.6% | — |
| LMArena Coding | — | 1210 |
| BigCodeBench Complete | 49.9% | — |
Reasoning DeepSeek-R1-Distill-Llama-70B leads
DeepSeek-R1-Distill-Llama-70B: 24.9 (#156), Olmo 2 0325 32b Instruct: 23.6 (#175)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | Olmo 2 0325 32b Instruct |
|---|---|---|
| Kagi LLM Benchmark | 52.3% | — |
| LiveBench Reasoning | 67.6% | — |
| LMArena Hard Prompts | — | 1208 |
| LiveBench Data Analysis | 55.9% | — |
| LiveBench | 54.5% | — |
Math DeepSeek-R1-Distill-Llama-70B leads
DeepSeek-R1-Distill-Llama-70B: 36.0 (#176), Olmo 2 0325 32b Instruct: 26.8 (#255)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | Olmo 2 0325 32b Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 51.4% | — |
| Omni-MATH | — | 16.1% |
| LiveBench Math | 58.1% | — |
| LMArena Math | — | 1208 |
| MATH Level 5 | 89.9% | — |
Knowledge DeepSeek-R1-Distill-Llama-70B leads
DeepSeek-R1-Distill-Llama-70B: 30.7 (#225), Olmo 2 0325 32b Instruct: 19.5 (#279)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | Olmo 2 0325 32b Instruct |
|---|---|---|
| GPQA Diamond | 55.7% | — |
| MMLU-Pro | — | 41.4% |
| GPQA (HELM) | — | 28.7% |
Multilingual Not comparable
DeepSeek-R1-Distill-Llama-70B: —, Olmo 2 0325 32b Instruct: 34.8 (#248)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | Olmo 2 0325 32b Instruct |
|---|---|---|
| LMArena Non-English | — | 1160 |
| LMArena Chinese | — | 1192 |
| LMArena Russian | — | 1187 |
Instruction Following DeepSeek-R1-Distill-Llama-70B leads
DeepSeek-R1-Distill-Llama-70B: 68.2 (#190), Olmo 2 0325 32b Instruct: 61.5 (#244)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | Olmo 2 0325 32b Instruct |
|---|---|---|
| LiveBench Instruction Following | 69.9% | — |
| IFEval | — | 78% |
| LMArena Instruction Following | — | 1186 |
Long Context Not comparable
DeepSeek-R1-Distill-Llama-70B: —, Olmo 2 0325 32b Instruct: 36.2 (#234)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | Olmo 2 0325 32b Instruct |
|---|---|---|
| LMArena Longer Query | — | 1194 |
Writing & Preference DeepSeek-R1-Distill-Llama-70B leads
DeepSeek-R1-Distill-Llama-70B: 49.0 (#194), Olmo 2 0325 32b Instruct: 42.1 (#236)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | Olmo 2 0325 32b Instruct |
|---|---|---|
| LMArena Text | — | 1218 |
| LMArena Creative Writing | — | 1199 |
| WildBench | — | 73.4% |
| LMArena Multi-Turn | — | 1221 |
| LiveBench Language | 23.8% | — |
Frequently asked questions
Is DeepSeek-R1-Distill-Llama-70B better than Olmo 2 0325 32b Instruct?
DeepSeek-R1-Distill-Llama-70B is the stronger model overall, scoring 37.8 to 32.7 on the Noometry Index.
Is DeepSeek-R1-Distill-Llama-70B or Olmo 2 0325 32b Instruct better for coding?
DeepSeek-R1-Distill-Llama-70B scores higher on coding benchmarks: 36.8 versus 35.2 in the Noometry coding category.
How many benchmarks do DeepSeek-R1-Distill-Llama-70B and Olmo 2 0325 32b Instruct share?
0 benchmarks have published results for both models. DeepSeek-R1-Distill-Llama-70B has 13 scored results on Noometry and Olmo 2 0325 32b Instruct has 16.