# DeepSeek V4 Flash vs GPT-5.6 Luna

> DeepSeek V4 Flash and GPT-5.6 Luna score almost the same on the Noometry Index (53.6 vs 54.6), so choose on price, context window or the category you care about most.

- Canonical page: https://noometry.com/compare/deepseek-v4-flash-vs-gpt-5-6-luna
- Last updated: 2026-10-12
- Shared benchmarks: 40

## Summary

- They share 40 benchmarks with published results for both. DeepSeek V4 Flash scores higher in 2 categories and GPT-5.6 Luna in 6 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Luna leads 77.7 to 60.3.
- The biggest single-benchmark swing is FrontierMath Tier 4: 24.4% for DeepSeek V4 Flash and 61% for GPT-5.6 Luna.
- DeepSeek V4 Flash is cheaper at $0.15 / $0.60 per million input/output tokens, against $0.20 / $1.20 for GPT-5.6 Luna.
- GPT-5.6 Luna accepts more context: 1.05M tokens versus 1M.
- DeepSeek V4 Flash has downloadable open weights; the other is API-only.

## Snapshot

| | DeepSeek V4 Flash | GPT-5.6 Luna |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 53.6 | 54.6 |
| Rank | 35 | 30 |
| Context | 1M | 1.05M |
| Input $/M | $0.15 | $0.20 |
| Output $/M | $0.60 | $1.20 |
| Weights | Open | Proprietary |

## Coding

- DeepSeek V4 Flash: 47.9 (#59)
- GPT-5.6 Luna: 54.5 (#28)

| Benchmark | DeepSeek V4 Flash | GPT-5.6 Luna |
|---|---|---|
| FrontierCode | 18.8% | 39.8% |
| LMArena WebDev | 1582 | 1519 |
| SciCode | 49.9% | 53.6% |
| WeirdML | 63% | 60.9% |
| LMArena Coding | 1457 | 1466 |
| ALE-Bench | 1,306 | 1,667 |
| DeepSWE | — | 67.2% |
| CursorBench | — | 35.9% |

## Agentic & Tool Use

- DeepSeek V4 Flash: —
- GPT-5.6 Luna: 34.4 (#45)

| Benchmark | DeepSeek V4 Flash | GPT-5.6 Luna |
|---|---|---|
| APEX-Agents | — | 43% |
| BALROG | — | 45.6% |
| GDP.pdf | — | 22.7% |
| Vending-Bench 2 | — | 4,095 |

## Reasoning

- DeepSeek V4 Flash: 53.7 (#30)
- GPT-5.6 Luna: 47.6 (#43)

| Benchmark | DeepSeek V4 Flash | GPT-5.6 Luna |
|---|---|---|
| ARC-AGI-2 | 61.4% | 59.5% |
| SimpleBench | 61.1% | 46.8% |
| Kagi LLM Benchmark | 52.2% | 49.1% |
| NYT Connections (extended) | 89.6% | 69.4% |
| ARC-AGI-1 | 89% | 88% |
| CritPt | 16.6% | 20.6% |
| Chess Puzzles | 33% | 40% |
| LMArena Hard Prompts | 1444 | 1451 |
| Mystery Game Puzzles | 34% | 21% |
| DTBench | 90.9% | 89.1% |
| LMCA | 41.7% | 48.5% |
| Epoch Capabilities Index | 154.49 | 156.39 |
| Surface Evolver Bench | — | 61.9% |

## Math

- DeepSeek V4 Flash: 60.3 (#37)
- GPT-5.6 Luna: 77.7 (#14)

| Benchmark | DeepSeek V4 Flash | GPT-5.6 Luna |
|---|---|---|
| FrontierMath (Tiers 1-3) | 57.5% | 82.1% |
| FrontierMath Tier 4 | 24.4% | 61% |
| OTIS Mock AIME 2024-2025 | 94.4% | 98.3% |
| ProofBench | 56% | 60% |
| LMArena Math | 1427 | 1458 |
| MathArena Final-Answer Competitions | 76.5% | — |

## Knowledge

- DeepSeek V4 Flash: 55.4 (#48)
- GPT-5.6 Luna: 58.5 (#34)

| Benchmark | DeepSeek V4 Flash | GPT-5.6 Luna |
|---|---|---|
| GPQA Diamond | 91% | 91.6% |
| SimpleQA Verified | 33.6% | 41% |
| LMArena Expert | 1441 | 1478 |

## Multimodal

- DeepSeek V4 Flash: —
- GPT-5.6 Luna: 42.7 (#28)

| Benchmark | DeepSeek V4 Flash | GPT-5.6 Luna |
|---|---|---|
| LMArena Vision | — | 1258 |
| Blueprint-Bench 2 | — | 22.6% |
| Furniture Assembly | — | 42.5% |
| LMArena Document | — | 1457 |

## Multilingual

- DeepSeek V4 Flash: 53.0 (#72)
- GPT-5.6 Luna: 52.8 (#78)

| Benchmark | DeepSeek V4 Flash | GPT-5.6 Luna |
|---|---|---|
| LMArena Non-English | 1420 | 1417 |
| LMArena Chinese | 1468 | 1470 |
| LMArena French | 1439 | 1456 |
| LMArena German | 1418 | 1454 |
| LMArena Japanese | 1406 | 1411 |
| LMArena Korean | 1384 | 1415 |
| LMArena Russian | 1428 | 1428 |
| LMArena Spanish | 1436 | 1448 |

## Instruction Following

- DeepSeek V4 Flash: 74.9 (#81)
- GPT-5.6 Luna: 75.6 (#57)

| Benchmark | DeepSeek V4 Flash | GPT-5.6 Luna |
|---|---|---|
| LMArena Instruction Following | 1421 | 1437 |

## Long Context

- DeepSeek V4 Flash: 43.8 (#85)
- GPT-5.6 Luna: 43.9 (#82)

| Benchmark | DeepSeek V4 Flash | GPT-5.6 Luna |
|---|---|---|
| LMArena Longer Query | 1434 | 1436 |

## Writing & Preference

- DeepSeek V4 Flash: 63.8 (#61)
- GPT-5.6 Luna: 68.0 (#29)

| Benchmark | DeepSeek V4 Flash | GPT-5.6 Luna |
|---|---|---|
| LMArena Text | 1432 | 1431 |
| LMArena Creative Writing | 1403 | 1396 |
| EQ-Bench Creative Writing | 1559 | 1829 |
| LMArena Multi-Turn | 1449 | 1434 |
| EQ-Bench 4 | — | 1156 |

## FAQ

### Is DeepSeek V4 Flash better than GPT-5.6 Luna?

DeepSeek V4 Flash and GPT-5.6 Luna score almost the same on the Noometry Index (53.6 vs 54.6), so choose on price, context window or the category you care about most.

### Which is cheaper, DeepSeek V4 Flash or GPT-5.6 Luna?

DeepSeek V4 Flash is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; GPT-5.6 Luna lists at $0.20 and $1.20.

### Is DeepSeek V4 Flash or GPT-5.6 Luna better for coding?

GPT-5.6 Luna scores higher on coding benchmarks: 54.5 versus 47.9 in the Noometry coding category.

### Which has the bigger context window?

GPT-5.6 Luna does, with 1.05M tokens against 1M.

### How many benchmarks do DeepSeek V4 Flash and GPT-5.6 Luna share?

40 benchmarks have published results for both models. DeepSeek V4 Flash has 41 scored results on Noometry and GPT-5.6 Luna has 52.