Model comparison
DeepSeek-V3.1 vs OLMo 2 Furious 13B
DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 29.2 on the Noometry Index.
Last verified . 0 shared benchmarks.
DeepSeek
Allen Institute for AI (Ai2)Summary
- The widest gap is in knowledge, where DeepSeek-V3.1 leads 43.0 to 18.0.
Side by side
| DeepSeek-V3.1 | OLMo 2 Furious 13B | |
|---|---|---|
| Provider | DeepSeek | Allen Institute for AI (Ai2) |
| Noometry Index | 42.2 | 29.2 |
| Released | 2025-08-21 | 2024-12-31 |
| Weights | Open | Open |
| Context window | 164K | — |
| Max output | 8K | — |
| Input $ / M tokens | $0.25 | — |
| Output $ / M tokens | $0.95 | — |
| Results tracked | 27 | 12 |
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Category by category
Coding DeepSeek-V3.1 leads
DeepSeek-V3.1: 39.8 (#140), OLMo 2 Furious 13B: 28.1 (#309)
| Benchmark | DeepSeek-V3.1 | OLMo 2 Furious 13B |
|---|---|---|
| WeirdML | 38.4% | — |
| LiveBench Coding | — | 10.4% |
| LMArena Coding | 1417 | — |
Reasoning DeepSeek-V3.1 leads
DeepSeek-V3.1: 27.3 (#109), OLMo 2 Furious 13B: 14.8 (#312)
| Benchmark | DeepSeek-V3.1 | OLMo 2 Furious 13B |
|---|---|---|
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| LiveBench Reasoning | — | 16.3% |
| LMArena Hard Prompts | 1417 | — |
| DTBench | 82.7% | — |
| LiveBench Data Analysis | — | 20.6% |
| LMCA | 24.3% | — |
| Epoch Capabilities Index | 139.92 | — |
| ForecastBench | 58 | — |
| LiveBench | — | 22.1% |
Math DeepSeek-V3.1 leads
DeepSeek-V3.1: 36.1 (#125), OLMo 2 Furious 13B: 21.3 (#270)
| Benchmark | DeepSeek-V3.1 | OLMo 2 Furious 13B |
|---|---|---|
| Omni-MATH | — | 15.6% |
| LiveBench Math | — | 13.6% |
| LMArena Math | 1420 | — |
Knowledge DeepSeek-V3.1 leads
DeepSeek-V3.1: 43.0 (#86), OLMo 2 Furious 13B: 18.0 (#278)
| Benchmark | DeepSeek-V3.1 | OLMo 2 Furious 13B |
|---|---|---|
| MMLU-Pro | — | 31% |
| Vectara Hallucination Rate | 5.5% | — |
| GPQA (HELM) | — | 31.6% |
| LMArena Expert | 1403 | — |
Multilingual Not comparable
DeepSeek-V3.1: 51.8 (#100), OLMo 2 Furious 13B: —
| Benchmark | DeepSeek-V3.1 | OLMo 2 Furious 13B |
|---|---|---|
| LMArena Non-English | 1400 | — |
| LMArena Chinese | 1470 | — |
| LMArena French | 1447 | — |
| LMArena German | 1412 | — |
| LMArena Japanese | 1374 | — |
| LMArena Korean | 1337 | — |
| LMArena Russian | 1405 | — |
| LMArena Spanish | 1429 | — |
Instruction Following DeepSeek-V3.1 leads
DeepSeek-V3.1: 74.1 (#105), OLMo 2 Furious 13B: 61.1 (#243)
| Benchmark | DeepSeek-V3.1 | OLMo 2 Furious 13B |
|---|---|---|
| LiveBench Instruction Following | — | 60.6% |
| IFEval | — | 73% |
| LMArena Instruction Following | 1400 | — |
Long Context Not comparable
DeepSeek-V3.1: 36.3 (#227), OLMo 2 Furious 13B: —
| Benchmark | DeepSeek-V3.1 | OLMo 2 Furious 13B |
|---|---|---|
| Fiction.LiveBench | 52.8% | — |
| LMArena Longer Query | 1422 | — |
Writing & Preference DeepSeek-V3.1 leads
DeepSeek-V3.1: 60.5 (#93), OLMo 2 Furious 13B: 43.0 (#226)
| Benchmark | DeepSeek-V3.1 | OLMo 2 Furious 13B |
|---|---|---|
| LMArena Text | 1420 | — |
| LMArena Creative Writing | 1401 | — |
| EQ-Bench Creative Writing | 1436 | — |
| WildBench | — | 68.9% |
| LMArena Multi-Turn | 1408 | — |
| LiveBench Language | — | 11.2% |
Frequently asked questions
Is DeepSeek-V3.1 better than OLMo 2 Furious 13B?
DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 29.2 on the Noometry Index.
Is DeepSeek-V3.1 or OLMo 2 Furious 13B better for coding?
DeepSeek-V3.1 scores higher on coding benchmarks: 39.8 versus 28.1 in the Noometry coding category.
How many benchmarks do DeepSeek-V3.1 and OLMo 2 Furious 13B share?
0 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and OLMo 2 Furious 13B has 12.