Model comparison
DeepSeek-V3.1 vs Olmo 3.1 32b Instruct
DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 39.4 on the Noometry Index.
Last verified . 16 shared benchmarks.
DeepSeek
Allen Institute for AI (Ai2)Summary
- They share 16 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 7 categories and Olmo 3.1 32b Instruct in 1 category; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.1 leads 60.3 to 50.2.
Side by side
| DeepSeek-V3.1 | Olmo 3.1 32b Instruct | |
|---|---|---|
| Provider | DeepSeek | Allen Institute for AI (Ai2) |
| Noometry Index | 42.8 | 39.4 |
| Released | 2025-08-21 | — |
| Weights | Open | Open |
| Context window | 164K | — |
| Max output | 8K | — |
| Input $ / M tokens | $0.25 | — |
| Output $ / M tokens | $0.95 | — |
| Results tracked | 27 | 16 |
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Category by category
Coding Too close to call
DeepSeek-V3.1: 40.3 (#144), Olmo 3.1 32b Instruct: 39.5 (#157)
| Benchmark | DeepSeek-V3.1 | Olmo 3.1 32b Instruct |
|---|---|---|
| LMArena Coding | 1417 | 1347 |
| WeirdML | 38.4% | — |
Reasoning DeepSeek-V3.1 leads
DeepSeek-V3.1: 27.9 (#110), Olmo 3.1 32b Instruct: 26.4 (#132)
| Benchmark | DeepSeek-V3.1 | Olmo 3.1 32b Instruct |
|---|---|---|
| LMArena Hard Prompts | 1417 | 1322 |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| DTBench | 82.7% | — |
| LMCA | 24.3% | — |
| Epoch Capabilities Index | 139.92 | — |
| ForecastBench | 58 | — |
Math DeepSeek-V3.1 leads
DeepSeek-V3.1: 38.9 (#122), Olmo 3.1 32b Instruct: 36.3 (#167)
| Benchmark | DeepSeek-V3.1 | Olmo 3.1 32b Instruct |
|---|---|---|
| LMArena Math | 1420 | 1305 |
Knowledge DeepSeek-V3.1 leads
DeepSeek-V3.1: 43.7 (#90), Olmo 3.1 32b Instruct: 36.1 (#175)
| Benchmark | DeepSeek-V3.1 | Olmo 3.1 32b Instruct |
|---|---|---|
| LMArena Expert | 1405 | 1308 |
| Vectara Hallucination Rate | 5.5% | — |
Multilingual DeepSeek-V3.1 leads
DeepSeek-V3.1: 51.6 (#106), Olmo 3.1 32b Instruct: 42.6 (#191)
| Benchmark | DeepSeek-V3.1 | Olmo 3.1 32b Instruct |
|---|---|---|
| LMArena Non-English | 1400 | 1275 |
| LMArena Chinese | 1469 | 1304 |
| LMArena French | 1447 | 1328 |
| LMArena German | 1411 | 1282 |
| LMArena Korean | 1337 | 1206 |
| LMArena Russian | 1405 | 1268 |
| LMArena Spanish | 1431 | 1336 |
| LMArena Japanese | 1378 | — |
Instruction Following DeepSeek-V3.1 leads
DeepSeek-V3.1: 73.9 (#110), Olmo 3.1 32b Instruct: 68.6 (#187)
| Benchmark | DeepSeek-V3.1 | Olmo 3.1 32b Instruct |
|---|---|---|
| LMArena Instruction Following | 1400 | 1299 |
Long Context Olmo 3.1 32b Instruct leads
DeepSeek-V3.1: 36.3 (#232), Olmo 3.1 32b Instruct: 39.9 (#166)
| Benchmark | DeepSeek-V3.1 | Olmo 3.1 32b Instruct |
|---|---|---|
| LMArena Longer Query | 1422 | 1312 |
| Fiction.LiveBench | 52.8% | — |
Writing & Preference DeepSeek-V3.1 leads
DeepSeek-V3.1: 60.3 (#98), Olmo 3.1 32b Instruct: 50.2 (#185)
| Benchmark | DeepSeek-V3.1 | Olmo 3.1 32b Instruct |
|---|---|---|
| LMArena Text | 1420 | 1311 |
| LMArena Creative Writing | 1401 | 1264 |
| LMArena Multi-Turn | 1408 | 1309 |
| EQ-Bench Creative Writing | 1436 | — |
Frequently asked questions
Is DeepSeek-V3.1 better than Olmo 3.1 32b Instruct?
DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 39.4 on the Noometry Index.
Is DeepSeek-V3.1 or Olmo 3.1 32b Instruct better for coding?
They score almost the same on coding (40.3 vs 39.5); test both on your own repository before choosing.
How many benchmarks do DeepSeek-V3.1 and Olmo 3.1 32b Instruct share?
16 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Olmo 3.1 32b Instruct has 16.