# DeepSeek-V3.1 vs Tulu 3 (Tülu 3) 70B

> DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 33.0 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v3-1-vs-tulu-3-70b
- Last updated: 2026-10-10
- Shared benchmarks: 11

## Summary

- They share 11 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 7 categories and Tulu 3 (Tülu 3) 70B in 1 category; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek-V3.1 leads 38.9 to 14.2.

## Snapshot

| | DeepSeek-V3.1 | Tulu 3 (Tülu 3) 70B |
|---|---|---|
| Provider | DeepSeek | Allen Institute for AI (Ai2) |
| Noometry Index | 42.8 | 33.0 |
| Rank | 108 | 251 |
| Context | 164K | — |
| Input $/M | $0.25 | — |
| Output $/M | $0.95 | — |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.1: 40.3 (#144)
- Tulu 3 (Tülu 3) 70B: 36.0 (#214)

| Benchmark | DeepSeek-V3.1 | Tulu 3 (Tülu 3) 70B |
|---|---|---|
| LMArena Coding | 1417 | 1235 |
| WeirdML | 38.4% | — |

## Reasoning

- DeepSeek-V3.1: 27.9 (#110)
- Tulu 3 (Tülu 3) 70B: 23.9 (#169)

| Benchmark | DeepSeek-V3.1 | Tulu 3 (Tülu 3) 70B |
|---|---|---|
| LMArena Hard Prompts | 1417 | 1220 |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| DTBench | 82.7% | — |
| LMCA | 24.3% | — |
| Epoch Capabilities Index | 139.92 | — |
| ForecastBench | 58 | — |

## Math

- DeepSeek-V3.1: 38.9 (#122)
- Tulu 3 (Tülu 3) 70B: 14.2 (#303)

| Benchmark | DeepSeek-V3.1 | Tulu 3 (Tülu 3) 70B |
|---|---|---|
| LMArena Math | 1420 | 1242 |
| OTIS Mock AIME 2024-2025 | — | 4.4% |
| MATH Level 5 | — | 42.7% |

## Knowledge

- DeepSeek-V3.1: 43.7 (#90)
- Tulu 3 (Tülu 3) 70B: 25.0 (#264)

| Benchmark | DeepSeek-V3.1 | Tulu 3 (Tülu 3) 70B |
|---|---|---|
| GPQA Diamond | — | 46.3% |
| Vectara Hallucination Rate | 5.5% | — |
| LMArena Expert | 1405 | — |

## Multilingual

- DeepSeek-V3.1: 51.6 (#106)
- Tulu 3 (Tülu 3) 70B: 39.9 (#222)

| Benchmark | DeepSeek-V3.1 | Tulu 3 (Tülu 3) 70B |
|---|---|---|
| LMArena Non-English | 1400 | 1236 |
| LMArena Chinese | 1469 | 1249 |
| LMArena Russian | 1405 | 1246 |
| LMArena French | 1447 | — |
| LMArena German | 1411 | — |
| LMArena Japanese | 1378 | — |
| LMArena Korean | 1337 | — |
| LMArena Spanish | 1431 | — |

## Instruction Following

- DeepSeek-V3.1: 73.9 (#110)
- Tulu 3 (Tülu 3) 70B: 64.8 (#227)

| Benchmark | DeepSeek-V3.1 | Tulu 3 (Tülu 3) 70B |
|---|---|---|
| LMArena Instruction Following | 1400 | 1233 |

## Long Context

- DeepSeek-V3.1: 36.3 (#232)
- Tulu 3 (Tülu 3) 70B: 37.1 (#222)

| Benchmark | DeepSeek-V3.1 | Tulu 3 (Tülu 3) 70B |
|---|---|---|
| LMArena Longer Query | 1422 | 1224 |
| Fiction.LiveBench | 52.8% | — |

## Writing & Preference

- DeepSeek-V3.1: 60.3 (#98)
- Tulu 3 (Tülu 3) 70B: 45.6 (#223)

| Benchmark | DeepSeek-V3.1 | Tulu 3 (Tülu 3) 70B |
|---|---|---|
| LMArena Text | 1420 | 1256 |
| LMArena Creative Writing | 1401 | 1231 |
| LMArena Multi-Turn | 1408 | 1252 |
| EQ-Bench Creative Writing | 1436 | — |

## FAQ

### Is DeepSeek-V3.1 better than Tulu 3 (Tülu 3) 70B?

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 33.0 on the Noometry Index.

### Is DeepSeek-V3.1 or Tulu 3 (Tülu 3) 70B better for coding?

DeepSeek-V3.1 scores higher on coding benchmarks: 40.3 versus 36.0 in the Noometry coding category.

### How many benchmarks do DeepSeek-V3.1 and Tulu 3 (Tülu 3) 70B share?

11 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Tulu 3 (Tülu 3) 70B has 14.