# DeepSeek V4 Flash vs GPT-5

> DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 50.9 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v4-flash-vs-gpt-5
- Last updated: 2026-10-10
- Shared benchmarks: 38

## Summary

- They share 38 benchmarks with published results for both. DeepSeek V4 Flash scores higher in 5 categories and GPT-5 in 3 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in long context, where GPT-5 leads 69.5 to 43.8.
- The biggest single-benchmark swing is ARC-AGI-2: 61.4% for DeepSeek V4 Flash and 9.9% for GPT-5.
- DeepSeek V4 Flash is cheaper at $0.15 / $0.60 per million input/output tokens, against $1.25 / $10 for GPT-5.
- 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 |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 53.6 | 50.9 |
| Rank | 35 | 45 |
| Context | 1M | 400K |
| Input $/M | $0.15 | $1.25 |
| Output $/M | $0.60 | $10 |
| Weights | Open | Proprietary |

## Coding

- DeepSeek V4 Flash: 47.9 (#59)
- GPT-5: 50.3 (#47)

| Benchmark | DeepSeek V4 Flash | GPT-5 |
|---|---|---|
| LMArena WebDev | 1582 | 1418 |
| SciCode | 49.9% | 42.9% |
| WeirdML | 63% | 60.7% |
| LMArena Coding | 1457 | 1436 |
| ALE-Bench | 1,306 | 1,162 |
| SWE-bench Verified | — | 73.6% |
| FrontierCode | 18.8% | — |
| SWE-bench Verified (bash only) | — | 65% |
| Aider Polyglot | — | 88% |
| GSO | — | 6.9% |
| AlgoTune | — | 1.67 |

## Agentic & Tool Use

- DeepSeek V4 Flash: —
- GPT-5: 33.1 (#56)

| Benchmark | DeepSeek V4 Flash | GPT-5 |
|---|---|---|
| Terminal-Bench | — | 49.6% |
| GDPval | — | 34.8% |
| Remote Labor Index | — | 1.7% |
| DeepResearch Bench | — | 49.6% |
| BALROG | — | 32.8% |
| LMArena Search | — | 1133 |
| METR Time Horizons | — | 69.6% |

## Reasoning

- DeepSeek V4 Flash: 53.7 (#30)
- GPT-5: 38.3 (#64)

| Benchmark | DeepSeek V4 Flash | GPT-5 |
|---|---|---|
| ARC-AGI-2 | 61.4% | 9.9% |
| SimpleBench | 61.1% | 56.7% |
| Kagi LLM Benchmark | 52.2% | 72.7% |
| ARC-AGI-1 | 89% | 65.7% |
| CritPt | 16.6% | 12.6% |
| Chess Puzzles | 33% | 37% |
| LMArena Hard Prompts | 1444 | 1416 |
| Mystery Game Puzzles | 34% | 23% |
| DTBench | 90.9% | 90.7% |
| LMCA | 41.7% | 40% |
| Epoch Capabilities Index | 154.49 | 150 |
| NYT Connections (extended) | 89.6% | — |
| EnigmaEval | — | 10.5% |
| EBR-Bench | — | 12.7% |
| ForecastBench | — | 61.4 |

## Math

- DeepSeek V4 Flash: 60.3 (#37)
- GPT-5: 55.0 (#44)

| Benchmark | DeepSeek V4 Flash | GPT-5 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 57.5% | 55.4% |
| FrontierMath Tier 4 | 24.4% | 22% |
| OTIS Mock AIME 2024-2025 | 94.4% | 91.4% |
| ProofBench | 56% | 18% |
| LMArena Math | 1427 | 1407 |
| MathArena Final-Answer Competitions | 76.5% | — |
| Omni-MATH | — | 64.7% |
| MATH Level 5 | — | 98.1% |
| FrontierMath (Feb 2025 set) | — | 32.4% |
| FrontierMath Tier 4 (v1) | — | 12.5% |

## Knowledge

- DeepSeek V4 Flash: 55.4 (#48)
- GPT-5: 56.6 (#43)

| Benchmark | DeepSeek V4 Flash | GPT-5 |
|---|---|---|
| GPQA Diamond | 91% | 86.2% |
| SimpleQA Verified | 33.6% | 50.1% |
| LMArena Expert | 1441 | 1419 |
| Humanity's Last Exam | — | 25.3% |
| MMLU-Pro | — | 86.3% |
| Confabulations | — | 10.3% |
| Vectara Hallucination Rate | — | 14.7% |
| GPQA (HELM) | — | 79.2% |

## Multimodal

- DeepSeek V4 Flash: —
- GPT-5: 46.8 (#13)

| Benchmark | DeepSeek V4 Flash | GPT-5 |
|---|---|---|
| LMArena Vision | — | 1232 |
| GeoBench | — | 81% |
| VPCT | — | 66% |

## Multilingual

- DeepSeek V4 Flash: 53.0 (#72)
- GPT-5: 51.4 (#110)

| Benchmark | DeepSeek V4 Flash | GPT-5 |
|---|---|---|
| LMArena Non-English | 1420 | 1397 |
| LMArena Chinese | 1468 | 1422 |
| LMArena French | 1439 | 1410 |
| LMArena German | 1418 | 1416 |
| LMArena Japanese | 1406 | 1409 |
| LMArena Korean | 1384 | 1360 |
| LMArena Russian | 1428 | 1406 |
| LMArena Spanish | 1436 | 1399 |

## Instruction Following

- DeepSeek V4 Flash: 74.9 (#81)
- GPT-5: 73.8 (#113)

| Benchmark | DeepSeek V4 Flash | GPT-5 |
|---|---|---|
| LMArena Instruction Following | 1421 | 1388 |
| IFEval | — | 87.5% |

## Long Context

- DeepSeek V4 Flash: 43.8 (#85)
- GPT-5: 69.5 (#2)

| Benchmark | DeepSeek V4 Flash | GPT-5 |
|---|---|---|
| LMArena Longer Query | 1434 | 1399 |
| Fiction.LiveBench | — | 97.2% |

## Writing & Preference

- DeepSeek V4 Flash: 63.8 (#61)
- GPT-5: 63.4 (#65)

| Benchmark | DeepSeek V4 Flash | GPT-5 |
|---|---|---|
| LMArena Text | 1432 | 1406 |
| LMArena Creative Writing | 1403 | 1365 |
| EQ-Bench Creative Writing | 1559 | 1627 |
| LMArena Multi-Turn | 1449 | 1426 |
| Short-Story Creative Writing | — | 86% |
| WildBench | — | 85.7% |

## FAQ

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

DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 50.9 on the Noometry Index.

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

DeepSeek V4 Flash is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; GPT-5 lists at $1.25 and $10.

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

GPT-5 scores higher on coding benchmarks: 50.3 versus 47.9 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 share?

38 benchmarks have published results for both models. DeepSeek V4 Flash has 41 scored results on Noometry and GPT-5 has 69.