# DeepSeek V4 Flash vs GPT-5 Nano

> DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 33.4 on the Noometry Index. GPT-5 Nano costs 1.9× less per token, which makes it the better buy when DeepSeek V4 Flash's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v4-flash-vs-gpt-5-nano
- Last updated: 2026-10-10
- Shared benchmarks: 32

## Summary

- They share 32 benchmarks with published results for both. DeepSeek V4 Flash scores higher in 7 categories and GPT-5 Nano in 1 category; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek V4 Flash leads 53.7 to 16.4.
- The biggest single-benchmark swing is ARC-AGI-1: 89% for DeepSeek V4 Flash and 20.7% for GPT-5 Nano.
- GPT-5 Nano is cheaper at $0.05 / $0.40 per million input/output tokens, against $0.15 / $0.60 for DeepSeek V4 Flash.
- DeepSeek V4 Flash accepts more context: 1M tokens versus 400K.
- DeepSeek V4 Flash has downloadable open weights; the other is API-only.

## Snapshot

| | DeepSeek V4 Flash | GPT-5 Nano |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 53.6 | 33.4 |
| Rank | 32 | 234 |
| Context | 1M | 400K |
| Input $/M | $0.15 | $0.05 |
| Output $/M | $0.60 | $0.40 |
| Weights | Open | Proprietary |

## Coding

- DeepSeek V4 Flash: 47.6 (#56)
- GPT-5 Nano: 33.3 (#248)

| Benchmark | DeepSeek V4 Flash | GPT-5 Nano |
|---|---|---|
| WeirdML | 63% | 38.1% |
| LMArena Coding | 1457 | 1351 |
| ALE-Bench | 1,306 | 718.67 |
| FrontierCode | 18.8% | — |
| SWE-bench Verified (bash only) | — | 34.8% |
| LMArena WebDev | 1581 | — |
| SciCode | 49.9% | — |

## Agentic & Tool Use

- DeepSeek V4 Flash: —
- GPT-5 Nano: 25.6 (#99)

| Benchmark | DeepSeek V4 Flash | GPT-5 Nano |
|---|---|---|
| Terminal-Bench | — | 21.8% |
| Berkeley Function Calling Leaderboard | — | 51.5% |

## Reasoning

- DeepSeek V4 Flash: 53.7 (#25)
- GPT-5 Nano: 16.4 (#291)

| Benchmark | DeepSeek V4 Flash | GPT-5 Nano |
|---|---|---|
| ARC-AGI-2 | 61.4% | 2.6% |
| Kagi LLM Benchmark | 52.2% | 62.2% |
| ARC-AGI-1 | 89% | 20.7% |
| Chess Puzzles | 33% | 27% |
| LMArena Hard Prompts | 1443 | 1329 |
| Mystery Game Puzzles | 34% | 9% |
| DTBench | 90.9% | 62.7% |
| LMCA | 41.7% | 7.9% |
| Epoch Capabilities Index | 154.51 | 139.39 |
| SimpleBench | 61.1% | — |
| NYT Connections (extended) | 89.6% | — |
| CritPt | 16.6% | — |
| ForecastBench | — | 59.1 |

## Math

- DeepSeek V4 Flash: 60.0 (#31)
- GPT-5 Nano: 29.1 (#221)

| Benchmark | DeepSeek V4 Flash | GPT-5 Nano |
|---|---|---|
| FrontierMath (Tiers 1-3) | 57.5% | 20% |
| FrontierMath Tier 4 | 24.4% | 2.4% |
| OTIS Mock AIME 2024-2025 | 94.4% | 81.1% |
| ProofBench | 56% | 12% |
| LMArena Math | 1427 | 1318 |
| MathArena Final-Answer Competitions | 76.5% | — |
| Omni-MATH | — | 54.6% |
| MATH Level 5 | — | 95.2% |
| FrontierMath (Feb 2025 set) | — | 8.3% |
| FrontierMath Tier 4 (v1) | — | 2.1% |

## Knowledge

- DeepSeek V4 Flash: 55.5 (#43)
- GPT-5 Nano: 35.8 (#166)

| Benchmark | DeepSeek V4 Flash | GPT-5 Nano |
|---|---|---|
| GPQA Diamond | 91% | 69.4% |
| SimpleQA Verified | 33.6% | 11.7% |
| LMArena Expert | 1441 | 1321 |
| MMLU-Pro | — | 77.8% |
| Vectara Hallucination Rate | — | 10.5% |
| GPQA (HELM) | — | 67.9% |

## Multimodal

- DeepSeek V4 Flash: —
- GPT-5 Nano: 30.7 (#102)

| Benchmark | DeepSeek V4 Flash | GPT-5 Nano |
|---|---|---|
| LMArena Vision | — | 1159 |
| VPCT | — | 37.2% |

## Multilingual

- DeepSeek V4 Flash: 53.2 (#71)
- GPT-5 Nano: 45.5 (#167)

| Benchmark | DeepSeek V4 Flash | GPT-5 Nano |
|---|---|---|
| LMArena Non-English | 1420 | 1313 |
| LMArena Chinese | 1467 | 1355 |
| LMArena German | 1418 | 1329 |
| LMArena Korean | 1383 | 1269 |
| LMArena Russian | 1428 | 1296 |
| LMArena Spanish | 1437 | 1360 |
| LMArena French | 1438 | — |
| LMArena Japanese | 1403 | — |

## Instruction Following

- DeepSeek V4 Flash: 75.1 (#76)
- GPT-5 Nano: 75.2 (#72)

| Benchmark | DeepSeek V4 Flash | GPT-5 Nano |
|---|---|---|
| LMArena Instruction Following | 1421 | 1306 |
| IFEval | — | 93.2% |

## Long Context

- DeepSeek V4 Flash: 43.8 (#81)
- GPT-5 Nano: 31.3 (#275)

| Benchmark | DeepSeek V4 Flash | GPT-5 Nano |
|---|---|---|
| LMArena Longer Query | 1435 | 1313 |
| Fiction.LiveBench | — | 44.4% |

## Writing & Preference

- DeepSeek V4 Flash: 64.0 (#59)
- GPT-5 Nano: 39.4 (#244)

| Benchmark | DeepSeek V4 Flash | GPT-5 Nano |
|---|---|---|
| LMArena Text | 1432 | 1320 |
| LMArena Creative Writing | 1403 | 1250 |
| EQ-Bench Creative Writing | 1559 | 705 |
| LMArena Multi-Turn | 1449 | 1311 |
| WildBench | — | 80.6% |

## FAQ

### Is DeepSeek V4 Flash better than GPT-5 Nano?

DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 33.4 on the Noometry Index. GPT-5 Nano costs 1.9× less per token, which makes it the better buy when DeepSeek V4 Flash's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek V4 Flash or GPT-5 Nano?

GPT-5 Nano is cheaper. It lists at $0.05 per million input tokens and $0.40 per million output tokens; DeepSeek V4 Flash lists at $0.15 and $0.60.

### Is DeepSeek V4 Flash or GPT-5 Nano better for coding?

DeepSeek V4 Flash scores higher on coding benchmarks: 47.6 versus 33.3 in the Noometry coding category.

### Which has the bigger context window?

DeepSeek V4 Flash does, with 1M tokens against 400K.

### How many benchmarks do DeepSeek V4 Flash and GPT-5 Nano share?

32 benchmarks have published results for both models. DeepSeek V4 Flash has 41 scored results on Noometry and GPT-5 Nano has 48.