# DeepSeek V4 Pro vs GPT-5.2

> DeepSeek V4 Pro and GPT-5.2 score almost the same on the Noometry Index (54.3 vs 54.1), so choose on price, context window or the category you care about most.

- Canonical page: https://noometry.com/compare/deepseek-v4-pro-vs-gpt-5-2
- Last updated: 2026-10-10
- Shared benchmarks: 41

## Summary

- They share 41 benchmarks with published results for both. DeepSeek V4 Pro scores higher in 7 categories and GPT-5.2 in 2 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where GPT-5.2 leads 40.2 to 32.8.
- The biggest single-benchmark swing is ProofBench: 50% for DeepSeek V4 Pro and 15% for GPT-5.2.
- DeepSeek V4 Pro is cheaper at $0.66 / $1.98 per million input/output tokens, against $1.75 / $14 for GPT-5.2.
- DeepSeek V4 Pro accepts more context: 1M tokens versus 400K.
- DeepSeek V4 Pro has downloadable open weights; the other is API-only.

## Snapshot

| | DeepSeek V4 Pro | GPT-5.2 |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 54.3 | 54.1 |
| Rank | 31 | 34 |
| Context | 1M | 400K |
| Input $/M | $0.66 | $1.75 |
| Output $/M | $1.98 | $14 |
| Weights | Open | Proprietary |

## Coding

- DeepSeek V4 Pro: 52.4 (#34)
- GPT-5.2: 51.6 (#37)

| Benchmark | DeepSeek V4 Pro | GPT-5.2 |
|---|---|---|
| SWE-bench Verified | 77.6% | 73.8% |
| LMArena WebDev | 1582 | 1416 |
| WeirdML | 66.2% | 72.2% |
| LMArena Coding | 1470 | 1447 |
| ALE-Bench | 1,403 | 1,294 |
| FrontierCode | 28.6% | — |
| SWE-bench Verified (bash only) | — | 72.8% |
| SWE-bench Multilingual | — | 66.7% |
| SciCode | 51% | — |
| GSO | — | 27.4% |
| AlgoTune | — | 2.05 |

## Agentic & Tool Use

- DeepSeek V4 Pro: 32.8 (#58)
- GPT-5.2: 40.2 (#24)

| Benchmark | DeepSeek V4 Pro | GPT-5.2 |
|---|---|---|
| Vending-Bench 2 | 3,285 | 3,591 |
| Terminal-Bench | — | 64.9% |
| APEX-Agents | 47.3% | — |
| Berkeley Function Calling Leaderboard | — | 55.9% |
| GDPval | — | 49.7% |
| Remote Labor Index | — | 2.5% |
| τ²-bench Airline | — | 83% |
| τ²-bench Banking | — | 32.2% |
| τ²-bench Retail | — | 81.6% |
| τ²-bench Telecom | — | 89.7% |
| DeepResearch Bench | — | 41.1% |
| LMArena Search | — | 1207 |
| METR Time Horizons | — | 75.3% |

## Reasoning

- DeepSeek V4 Pro: 56.5 (#24)
- GPT-5.2: 50.2 (#35)

| Benchmark | DeepSeek V4 Pro | GPT-5.2 |
|---|---|---|
| ARC-AGI-2 | 61.3% | 52.9% |
| Kagi LLM Benchmark | 53.5% | 73.3% |
| NYT Connections (extended) | 91.3% | 83.6% |
| ARC-AGI-1 | 90.5% | 86.2% |
| Chess Puzzles | 47% | 49% |
| LMArena Hard Prompts | 1461 | 1445 |
| Mystery Game Puzzles | 43% | 23% |
| DTBench | 93.9% | 90.9% |
| LMCA | 45.5% | 43.9% |
| Epoch Capabilities Index | 155.31 | 153.45 |
| ForecastBench | 56.1 | 60.1 |
| SimpleBench | — | 45.8% |
| CritPt | 18% | — |
| EnigmaEval | — | 10.4% |
| EBR-Bench | — | 23% |
| Surface Evolver Bench | 40% | — |

## Math

- DeepSeek V4 Pro: 64.8 (#30)
- GPT-5.2: 60.0 (#38)

| Benchmark | DeepSeek V4 Pro | GPT-5.2 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 64.6% | 67.4% |
| FrontierMath Tier 4 | 26.8% | 31.7% |
| MathArena Final-Answer Competitions | 76.6% | 72% |
| OTIS Mock AIME 2024-2025 | 98.6% | 96.1% |
| ProofBench | 50% | 15% |
| LMArena Math | 1455 | 1440 |
| FrontierMath (Feb 2025 set) | — | 40.7% |
| FrontierMath Tier 4 (v1) | — | 18.8% |

## Knowledge

- DeepSeek V4 Pro: 59.5 (#31)
- GPT-5.2: 59.3 (#32)

| Benchmark | DeepSeek V4 Pro | GPT-5.2 |
|---|---|---|
| GPQA Diamond | 91.7% | 91.4% |
| SimpleQA Verified | 52.9% | 37.1% |
| Vectara Hallucination Rate | 8.6% | 8.4% |
| LMArena Expert | 1464 | 1445 |
| Humanity's Last Exam | — | 27.8% |

## Multimodal

- DeepSeek V4 Pro: —
- GPT-5.2: 51.3 (#7)

| Benchmark | DeepSeek V4 Pro | GPT-5.2 |
|---|---|---|
| LMArena Vision | — | 1268 |
| VPCT | — | 84% |
| Furniture Assembly | — | 38.3% |
| LMArena Document | — | 1405 |

## Multilingual

- DeepSeek V4 Pro: 54.4 (#45)
- GPT-5.2: 53.4 (#67)

| Benchmark | DeepSeek V4 Pro | GPT-5.2 |
|---|---|---|
| LMArena Non-English | 1439 | 1425 |
| LMArena Chinese | 1486 | 1460 |
| LMArena French | 1472 | 1455 |
| LMArena German | 1458 | 1448 |
| LMArena Japanese | 1445 | 1420 |
| LMArena Korean | 1447 | 1392 |
| LMArena Russian | 1453 | 1440 |
| LMArena Spanish | 1458 | 1433 |

## Instruction Following

- DeepSeek V4 Pro: 76.1 (#47)
- GPT-5.2: 74.7 (#89)

| Benchmark | DeepSeek V4 Pro | GPT-5.2 |
|---|---|---|
| LMArena Instruction Following | 1448 | 1417 |

## Long Context

- DeepSeek V4 Pro: 45.0 (#51)
- GPT-5.2: 44.0 (#78)

| Benchmark | DeepSeek V4 Pro | GPT-5.2 |
|---|---|---|
| LMArena Longer Query | 1458 | 1428 |
| CL-bench | — | 18.2% |
| CL-bench Life | 13.5% | — |

## Writing & Preference

- DeepSeek V4 Pro: 65.5 (#46)
- GPT-5.2: 66.8 (#32)

| Benchmark | DeepSeek V4 Pro | GPT-5.2 |
|---|---|---|
| LMArena Text | 1451 | 1439 |
| LMArena Creative Writing | 1446 | 1401 |
| EQ-Bench Creative Writing | 1553 | 1703 |
| LMArena Multi-Turn | 1467 | 1458 |
| EQ-Bench 4 | 1166 | — |

## FAQ

### Is DeepSeek V4 Pro better than GPT-5.2?

DeepSeek V4 Pro and GPT-5.2 score almost the same on the Noometry Index (54.3 vs 54.1), so choose on price, context window or the category you care about most.

### Which is cheaper, DeepSeek V4 Pro or GPT-5.2?

DeepSeek V4 Pro is cheaper. It lists at $0.66 per million input tokens and $1.98 per million output tokens; GPT-5.2 lists at $1.75 and $14.

### Is DeepSeek V4 Pro or GPT-5.2 better for coding?

They score almost the same on coding (52.4 vs 51.6); test both on your own repository before choosing.

### Which has the bigger context window?

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

### How many benchmarks do DeepSeek V4 Pro and GPT-5.2 share?

41 benchmarks have published results for both models. DeepSeek V4 Pro has 48 scored results on Noometry and GPT-5.2 has 67.