# DeepSeek-V2 (MoE-236B, May 2024) vs GPT-5 Nano

> GPT-5 Nano has enough public results to be ranked (#241); DeepSeek-V2 (MoE-236B, May 2024) does not yet, so treat this comparison as directional.

- Canonical page: https://noometry.com/compare/deepseek-v2-vs-gpt-5-nano
- Last updated: 2026-10-10
- Shared benchmarks: 1

## Summary

- They share 1 benchmark with published results for both. DeepSeek-V2 (MoE-236B, May 2024) scores higher in 1 category and GPT-5 Nano in 0 categories; one gap is clear of the uncertainty.
- The widest gap is in coding, where DeepSeek-V2 (MoE-236B, May 2024) leads 40.4 to 33.6.
- DeepSeek-V2 (MoE-236B, May 2024) has downloadable open weights; the other is API-only.

## Snapshot

| | DeepSeek-V2 (MoE-236B, May 2024) | GPT-5 Nano |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 40.3 | 33.5 |
| Rank | — | 241 |
| Context | — | 400K |
| Input $/M | — | $0.05 |
| Output $/M | — | $0.40 |
| Weights | Open | Proprietary |

## Coding

- DeepSeek-V2 (MoE-236B, May 2024): 40.4 (#139)
- GPT-5 Nano: 33.6 (#254)

| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GPT-5 Nano |
|---|---|---|
| SWE-bench Verified (bash only) | — | 34.8% |
| WeirdML | — | 38.1% |
| BigCodeBench Instruct | 48.9% | — |
| LMArena Coding | — | 1351 |
| BigCodeBench Complete | 59.4% | — |
| ALE-Bench | — | 718.67 |

## Agentic & Tool Use

- DeepSeek-V2 (MoE-236B, May 2024): —
- GPT-5 Nano: 25.8 (#106)

| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GPT-5 Nano |
|---|---|---|
| Terminal-Bench | — | 21.8% |
| Berkeley Function Calling Leaderboard | — | 51.5% |

## Reasoning

- DeepSeek-V2 (MoE-236B, May 2024): —
- GPT-5 Nano: 16.3 (#306)

| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GPT-5 Nano |
|---|---|---|
| Epoch Capabilities Index | 124.77 | 139.38 |
| ARC-AGI-2 | — | 2.6% |
| Kagi LLM Benchmark | — | 62.2% |
| ARC-AGI-1 | — | 20.7% |
| Chess Puzzles | — | 27% |
| LMArena Hard Prompts | — | 1328 |
| Mystery Game Puzzles | — | 9% |
| DTBench | — | 62.7% |
| LMCA | — | 7.9% |
| BIG-Bench Hard | 78.8% | — |
| ForecastBench | — | 59.1 |
| HellaSwag | 87.1% | — |
| PIQA | 83.9% | — |
| WinoGrande | 86.3% | — |

## Math

- DeepSeek-V2 (MoE-236B, May 2024): —
- GPT-5 Nano: 29.4 (#241)

| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GPT-5 Nano |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 20% |
| FrontierMath Tier 4 | — | 2.4% |
| OTIS Mock AIME 2024-2025 | — | 81.1% |
| ProofBench | — | 12% |
| Omni-MATH | — | 54.6% |
| LMArena Math | — | 1317 |
| MATH Level 5 | — | 95.2% |
| FrontierMath (Feb 2025 set) | — | 8.3% |
| FrontierMath Tier 4 (v1) | — | 2.1% |

## Knowledge

- DeepSeek-V2 (MoE-236B, May 2024): —
- GPT-5 Nano: 35.9 (#178)

| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GPT-5 Nano |
|---|---|---|
| GPQA Diamond | — | 69.4% |
| SimpleQA Verified | — | 11.7% |
| MMLU-Pro | — | 77.8% |
| Vectara Hallucination Rate | — | 10.5% |
| GPQA (HELM) | — | 67.9% |
| LMArena Expert | — | 1321 |
| ARC (AI2) Challenge | 92.2% | — |
| MMLU | 78.4% | — |
| TriviaQA | 80% | — |

## Multimodal

- DeepSeek-V2 (MoE-236B, May 2024): —
- GPT-5 Nano: 31.3 (#108)

| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GPT-5 Nano |
|---|---|---|
| LMArena Vision | — | 1159 |
| VPCT | — | 37.2% |

## Multilingual

- DeepSeek-V2 (MoE-236B, May 2024): —
- GPT-5 Nano: 45.3 (#172)

| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GPT-5 Nano |
|---|---|---|
| LMArena Non-English | — | 1313 |
| LMArena Chinese | — | 1356 |
| LMArena German | — | 1327 |
| LMArena Japanese | — | 1226 |
| LMArena Korean | — | 1269 |
| LMArena Russian | — | 1296 |
| LMArena Spanish | — | 1360 |

## Instruction Following

- DeepSeek-V2 (MoE-236B, May 2024): —
- GPT-5 Nano: 75.0 (#79)

| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GPT-5 Nano |
|---|---|---|
| IFEval | — | 93.2% |
| LMArena Instruction Following | — | 1306 |

## Long Context

- DeepSeek-V2 (MoE-236B, May 2024): —
- GPT-5 Nano: 31.3 (#281)

| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GPT-5 Nano |
|---|---|---|
| Fiction.LiveBench | — | 44.4% |
| LMArena Longer Query | — | 1312 |

## Writing & Preference

- DeepSeek-V2 (MoE-236B, May 2024): —
- GPT-5 Nano: 39.1 (#249)

| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GPT-5 Nano |
|---|---|---|
| LMArena Text | — | 1320 |
| LMArena Creative Writing | — | 1249 |
| EQ-Bench Creative Writing | — | 705 |
| WildBench | — | 80.6% |
| LMArena Multi-Turn | — | 1311 |

## FAQ

### Is DeepSeek-V2 (MoE-236B, May 2024) better than GPT-5 Nano?

GPT-5 Nano has enough public results to be ranked (#241); DeepSeek-V2 (MoE-236B, May 2024) does not yet, so treat this comparison as directional.

### Is DeepSeek-V2 (MoE-236B, May 2024) or GPT-5 Nano better for coding?

DeepSeek-V2 (MoE-236B, May 2024) scores higher on coding benchmarks: 40.4 versus 33.6 in the Noometry coding category.

### How many benchmarks do DeepSeek-V2 (MoE-236B, May 2024) and GPT-5 Nano share?

1 benchmark has published results for both models. DeepSeek-V2 (MoE-236B, May 2024) has 10 scored results on Noometry and GPT-5 Nano has 49.