# DeepSeek-V3.1 vs Olmo 3 32b Think

> DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 38.1 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v3-1-vs-olmo-3-32b-think
- Last updated: 2026-10-10
- Shared benchmarks: 14

## Summary

- They share 14 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 7 categories and Olmo 3 32b Think in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.1 leads 60.5 to 49.2.

## Snapshot

| | DeepSeek-V3.1 | Olmo 3 32b Think |
|---|---|---|
| Provider | DeepSeek | Allen Institute for AI (Ai2) |
| Noometry Index | 42.2 | 38.1 |
| Rank | 106 | 182 |
| Context | 164K | — |
| Input $/M | $0.25 | — |
| Output $/M | $0.95 | — |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.1: 39.8 (#140)
- Olmo 3 32b Think: 38.0 (#169)

| Benchmark | DeepSeek-V3.1 | Olmo 3 32b Think |
|---|---|---|
| LMArena Coding | 1417 | 1318 |
| WeirdML | 38.4% | — |

## Reasoning

- DeepSeek-V3.1: 27.3 (#109)
- Olmo 3 32b Think: 24.9 (#138)

| Benchmark | DeepSeek-V3.1 | Olmo 3 32b Think |
|---|---|---|
| LMArena Hard Prompts | 1417 | 1302 |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| DTBench | 82.7% | — |
| LMCA | 24.3% | — |
| Epoch Capabilities Index | 139.92 | — |
| ForecastBench | 58 | — |

## Math

- DeepSeek-V3.1: 36.1 (#125)
- Olmo 3 32b Think: 33.8 (#165)

| Benchmark | DeepSeek-V3.1 | Olmo 3 32b Think |
|---|---|---|
| LMArena Math | 1420 | 1315 |

## Knowledge

- DeepSeek-V3.1: 43.0 (#86)
- Olmo 3 32b Think: 34.4 (#189)

| Benchmark | DeepSeek-V3.1 | Olmo 3 32b Think |
|---|---|---|
| LMArena Expert | 1403 | 1272 |
| Vectara Hallucination Rate | 5.5% | — |

## Multilingual

- DeepSeek-V3.1: 51.8 (#100)
- Olmo 3 32b Think: 41.4 (#205)

| Benchmark | DeepSeek-V3.1 | Olmo 3 32b Think |
|---|---|---|
| LMArena Non-English | 1400 | 1255 |
| LMArena Chinese | 1470 | 1300 |
| LMArena French | 1447 | 1291 |
| LMArena German | 1412 | 1290 |
| LMArena Russian | 1405 | 1255 |
| LMArena Japanese | 1374 | — |
| LMArena Korean | 1337 | — |
| LMArena Spanish | 1429 | — |

## Instruction Following

- DeepSeek-V3.1: 74.1 (#105)
- Olmo 3 32b Think: 67.4 (#193)

| Benchmark | DeepSeek-V3.1 | Olmo 3 32b Think |
|---|---|---|
| LMArena Instruction Following | 1400 | 1275 |

## Long Context

- DeepSeek-V3.1: 36.3 (#227)
- Olmo 3 32b Think: 39.4 (#177)

| Benchmark | DeepSeek-V3.1 | Olmo 3 32b Think |
|---|---|---|
| LMArena Longer Query | 1422 | 1296 |
| Fiction.LiveBench | 52.8% | — |

## Writing & Preference

- DeepSeek-V3.1: 60.5 (#93)
- Olmo 3 32b Think: 49.2 (#188)

| Benchmark | DeepSeek-V3.1 | Olmo 3 32b Think |
|---|---|---|
| LMArena Text | 1420 | 1300 |
| LMArena Creative Writing | 1401 | 1256 |
| LMArena Multi-Turn | 1408 | 1289 |
| EQ-Bench Creative Writing | 1436 | — |

## FAQ

### Is DeepSeek-V3.1 better than Olmo 3 32b Think?

DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 38.1 on the Noometry Index.

### Is DeepSeek-V3.1 or Olmo 3 32b Think better for coding?

DeepSeek-V3.1 scores higher on coding benchmarks: 39.8 versus 38.0 in the Noometry coding category.

### How many benchmarks do DeepSeek-V3.1 and Olmo 3 32b Think share?

14 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Olmo 3 32b Think has 14.