# DeepSeek-V3 vs Mistral Medium 3.5

> DeepSeek-V3 and Mistral Medium 3.5 score almost the same on the Noometry Index (39.5 vs 40.2), so choose on price, context window or the category you care about most.

- Canonical page: https://noometry.com/compare/deepseek-v3-vs-mistral-medium-3-5
- Last updated: 2026-10-10
- Shared benchmarks: 18

## Summary

- They share 18 benchmarks with published results for both. DeepSeek-V3 scores higher in 2 categories and Mistral Medium 3.5 in 6 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in long context, where Mistral Medium 3.5 leads 43.2 to 34.0.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 52.3% for DeepSeek-V3 and 41.4% for Mistral Medium 3.5.
- DeepSeek-V3 is cheaper at $0.24 / $0.90 per million input/output tokens, against $1.50 / $7.50 for Mistral Medium 3.5.
- Mistral Medium 3.5 accepts more context: 262K tokens versus 164K.

## Snapshot

| | DeepSeek-V3 | Mistral Medium 3.5 |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 39.5 | 40.2 |
| Rank | 166 | 152 |
| Context | 164K | 262K |
| Input $/M | $0.24 | $1.50 |
| Output $/M | $0.90 | $7.50 |
| Weights | Open | Open |

## Coding

- DeepSeek-V3: 42.3 (#106)
- Mistral Medium 3.5: 36.0 (#213)

| Benchmark | DeepSeek-V3 | Mistral Medium 3.5 |
|---|---|---|
| LMArena Coding | 1368 | 1461 |
| Aider Polyglot | 55.1% | — |
| LMArena WebDev | — | 1264 |
| SciCode | 35.8% | — |
| WeirdML | 36.1% | — |
| BigCodeBench Instruct | 50% | — |
| LiveBench Coding | 70.9% | — |
| BigCodeBench Complete | 62.2% | — |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73% | — |

## Agentic & Tool Use

- DeepSeek-V3: —
- Mistral Medium 3.5: —

| Benchmark | DeepSeek-V3 | Mistral Medium 3.5 |
|---|---|---|
| METR Time Horizons | 49.6% | — |

## Reasoning

- DeepSeek-V3: 20.5 (#236)
- Mistral Medium 3.5: 17.3 (#295)

| Benchmark | DeepSeek-V3 | Mistral Medium 3.5 |
|---|---|---|
| Kagi LLM Benchmark | 52.3% | 41.4% |
| LMArena Hard Prompts | 1365 | 1436 |
| Epoch Capabilities Index | 135.94 | 141.35 |
| SimpleBench | 27.2% | — |
| NYT Connections (extended) | — | 12.9% |
| CritPt | 0% | — |
| LiveBench Reasoning | 65.8% | — |
| DTBench | 64.8% | — |
| LiveBench Data Analysis | 60.9% | — |
| LMCA | 15.5% | — |
| BIG-Bench Hard | 87.5% | — |
| ForecastBench | 59.1 | — |
| HellaSwag | 88.9% | — |
| LiveBench | 66.9% | — |
| PIQA | 84.7% | — |
| WinoGrande | 85.2% | — |

## Math

- DeepSeek-V3: 32.1 (#219)
- Mistral Medium 3.5: 39.1 (#113)

| Benchmark | DeepSeek-V3 | Mistral Medium 3.5 |
|---|---|---|
| LMArena Math | 1373 | 1431 |
| OTIS Mock AIME 2024-2025 | 37.8% | — |
| Omni-MATH | 40.3% | — |
| LiveBench Math | 73.5% | — |
| MATH Level 5 | 75.5% | — |
| FrontierMath (Feb 2025 set) | 1.7% | — |

## Knowledge

- DeepSeek-V3: 37.5 (#155)
- Mistral Medium 3.5: 40.0 (#126)

| Benchmark | DeepSeek-V3 | Mistral Medium 3.5 |
|---|---|---|
| LMArena Expert | 1351 | 1432 |
| GPQA Diamond | 67.6% | — |
| MMLU-Pro | 72.3% | — |
| Confabulations | 26.1% | — |
| Vectara Hallucination Rate | 6.1% | — |
| GPQA (HELM) | 53.8% | — |
| ARC (AI2) Challenge | 95.3% | — |
| MMLU | 87.2% | — |
| TriviaQA | 82.9% | — |

## Multimodal

- DeepSeek-V3: —
- Mistral Medium 3.5: 38.3 (#65)

| Benchmark | DeepSeek-V3 | Mistral Medium 3.5 |
|---|---|---|
| LMArena Vision | — | 1223 |

## Multilingual

- DeepSeek-V3: 48.5 (#143)
- Mistral Medium 3.5: 51.9 (#100)

| Benchmark | DeepSeek-V3 | Mistral Medium 3.5 |
|---|---|---|
| LMArena Non-English | 1358 | 1404 |
| LMArena Chinese | 1391 | 1442 |
| LMArena French | 1385 | 1448 |
| LMArena German | 1374 | 1451 |
| LMArena Korean | 1319 | 1385 |
| LMArena Russian | 1373 | 1395 |
| LMArena Spanish | 1358 | 1409 |
| LMArena Japanese | 1333 | — |

## Instruction Following

- DeepSeek-V3: 72.8 (#130)
- Mistral Medium 3.5: 74.6 (#90)

| Benchmark | DeepSeek-V3 | Mistral Medium 3.5 |
|---|---|---|
| LMArena Instruction Following | 1345 | 1415 |
| LiveBench Instruction Following | 81.5% | — |
| IFEval | 83.2% | — |

## Long Context

- DeepSeek-V3: 34.0 (#253)
- Mistral Medium 3.5: 43.2 (#103)

| Benchmark | DeepSeek-V3 | Mistral Medium 3.5 |
|---|---|---|
| LMArena Longer Query | 1352 | 1415 |
| Fiction.LiveBench | 50% | — |

## Writing & Preference

- DeepSeek-V3: 57.4 (#130)
- Mistral Medium 3.5: 58.5 (#117)

| Benchmark | DeepSeek-V3 | Mistral Medium 3.5 |
|---|---|---|
| LMArena Text | 1375 | 1421 |
| LMArena Creative Writing | 1364 | 1374 |
| LMArena Multi-Turn | 1389 | 1423 |
| Short-Story Creative Writing | 77% | — |
| EQ-Bench Creative Writing | 1472 | — |
| WildBench | 83% | — |
| EQ-Bench 4 | — | 993 |
| LiveBench Language | 49.1% | — |

## FAQ

### Is DeepSeek-V3 better than Mistral Medium 3.5?

DeepSeek-V3 and Mistral Medium 3.5 score almost the same on the Noometry Index (39.5 vs 40.2), so choose on price, context window or the category you care about most.

### Which is cheaper, DeepSeek-V3 or Mistral Medium 3.5?

DeepSeek-V3 is cheaper. It lists at $0.24 per million input tokens and $0.90 per million output tokens; Mistral Medium 3.5 lists at $1.50 and $7.50.

### Is DeepSeek-V3 or Mistral Medium 3.5 better for coding?

DeepSeek-V3 scores higher on coding benchmarks: 42.3 versus 36.0 in the Noometry coding category.

### Which has the bigger context window?

Mistral Medium 3.5 does, with 262K tokens against 164K.

### How many benchmarks do DeepSeek-V3 and Mistral Medium 3.5 share?

18 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and Mistral Medium 3.5 has 22.