# DeepSeek-V3 vs GPT-5.2

> GPT-5.2 is the stronger model overall, scoring 54.1 to 39.5 on the Noometry Index. DeepSeek-V3 costs 12× less per token, which makes it the better buy when GPT-5.2's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v3-vs-gpt-5-2
- Last updated: 2026-10-11
- Shared benchmarks: 30

## Summary

- They share 30 benchmarks with published results for both. DeepSeek-V3 scores higher in 0 categories and GPT-5.2 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.2 leads 50.2 to 20.5.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 37.8% for DeepSeek-V3 and 96.1% for GPT-5.2.
- DeepSeek-V3 is cheaper at $0.24 / $0.90 per million input/output tokens, against $1.75 / $14 for GPT-5.2.
- GPT-5.2 accepts more context: 400K tokens versus 164K.
- DeepSeek-V3 has downloadable open weights; the other is API-only.

## Snapshot

| | DeepSeek-V3 | GPT-5.2 |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 39.5 | 54.1 |
| Rank | 166 | 34 |
| Context | 164K | 400K |
| Input $/M | $0.24 | $1.75 |
| Output $/M | $0.90 | $14 |
| Weights | Open | Proprietary |

## Coding

- DeepSeek-V3: 42.3 (#106)
- GPT-5.2: 51.6 (#37)

| Benchmark | DeepSeek-V3 | GPT-5.2 |
|---|---|---|
| WeirdML | 36.1% | 72.2% |
| LMArena Coding | 1368 | 1447 |
| SWE-bench Verified | — | 73.8% |
| SWE-bench Verified (bash only) | — | 72.8% |
| Aider Polyglot | 55.1% | — |
| LMArena WebDev | — | 1416 |
| SWE-bench Multilingual | — | 66.7% |
| SciCode | 35.8% | — |
| GSO | — | 27.4% |
| BigCodeBench Instruct | 50% | — |
| LiveBench Coding | 70.9% | — |
| BigCodeBench Complete | 62.2% | — |
| ALE-Bench | — | 1,294 |
| AlgoTune | — | 2.05 |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73% | — |

## Agentic & Tool Use

- DeepSeek-V3: —
- GPT-5.2: 40.2 (#24)

| Benchmark | DeepSeek-V3 | GPT-5.2 |
|---|---|---|
| METR Time Horizons | 49.6% | 75.3% |
| Terminal-Bench | — | 64.9% |
| 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 |
| Vending-Bench 2 | — | 3,591 |

## Reasoning

- DeepSeek-V3: 20.5 (#236)
- GPT-5.2: 50.2 (#35)

| Benchmark | DeepSeek-V3 | GPT-5.2 |
|---|---|---|
| SimpleBench | 27.2% | 45.8% |
| Kagi LLM Benchmark | 52.3% | 73.3% |
| LMArena Hard Prompts | 1365 | 1445 |
| DTBench | 64.8% | 90.9% |
| LMCA | 15.5% | 43.9% |
| Epoch Capabilities Index | 135.94 | 153.45 |
| ForecastBench | 59.1 | 60.1 |
| ARC-AGI-2 | — | 52.9% |
| NYT Connections (extended) | — | 83.6% |
| ARC-AGI-1 | — | 86.2% |
| CritPt | 0% | — |
| Chess Puzzles | — | 49% |
| EnigmaEval | — | 10.4% |
| EBR-Bench | — | 23% |
| LiveBench Reasoning | 65.8% | — |
| Mystery Game Puzzles | — | 23% |
| LiveBench Data Analysis | 60.9% | — |
| BIG-Bench Hard | 87.5% | — |
| HellaSwag | 88.9% | — |
| LiveBench | 66.9% | — |
| PIQA | 84.7% | — |
| WinoGrande | 85.2% | — |

## Math

- DeepSeek-V3: 32.1 (#219)
- GPT-5.2: 60.0 (#38)

| Benchmark | DeepSeek-V3 | GPT-5.2 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 37.8% | 96.1% |
| LMArena Math | 1373 | 1440 |
| FrontierMath (Feb 2025 set) | 1.7% | 40.7% |
| FrontierMath (Tiers 1-3) | — | 67.4% |
| FrontierMath Tier 4 | — | 31.7% |
| MathArena Final-Answer Competitions | — | 72% |
| ProofBench | — | 15% |
| Omni-MATH | 40.3% | — |
| LiveBench Math | 73.5% | — |
| MATH Level 5 | 75.5% | — |
| FrontierMath Tier 4 (v1) | — | 18.8% |

## Knowledge

- DeepSeek-V3: 37.5 (#155)
- GPT-5.2: 59.3 (#32)

| Benchmark | DeepSeek-V3 | GPT-5.2 |
|---|---|---|
| GPQA Diamond | 67.6% | 91.4% |
| Vectara Hallucination Rate | 6.1% | 8.4% |
| LMArena Expert | 1351 | 1445 |
| Humanity's Last Exam | — | 27.8% |
| SimpleQA Verified | — | 37.1% |
| MMLU-Pro | 72.3% | — |
| Confabulations | 26.1% | — |
| GPQA (HELM) | 53.8% | — |
| ARC (AI2) Challenge | 95.3% | — |
| MMLU | 87.2% | — |
| TriviaQA | 82.9% | — |

## Multimodal

- DeepSeek-V3: —
- GPT-5.2: 51.3 (#7)

| Benchmark | DeepSeek-V3 | GPT-5.2 |
|---|---|---|
| LMArena Vision | — | 1268 |
| VPCT | — | 84% |
| Furniture Assembly | — | 38.3% |
| LMArena Document | — | 1405 |

## Multilingual

- DeepSeek-V3: 48.5 (#143)
- GPT-5.2: 53.4 (#67)

| Benchmark | DeepSeek-V3 | GPT-5.2 |
|---|---|---|
| LMArena Non-English | 1358 | 1425 |
| LMArena Chinese | 1391 | 1460 |
| LMArena French | 1385 | 1455 |
| LMArena German | 1374 | 1448 |
| LMArena Japanese | 1333 | 1420 |
| LMArena Korean | 1319 | 1392 |
| LMArena Russian | 1373 | 1440 |
| LMArena Spanish | 1358 | 1433 |

## Instruction Following

- DeepSeek-V3: 72.8 (#130)
- GPT-5.2: 74.7 (#89)

| Benchmark | DeepSeek-V3 | GPT-5.2 |
|---|---|---|
| LMArena Instruction Following | 1345 | 1417 |
| LiveBench Instruction Following | 81.5% | — |
| IFEval | 83.2% | — |

## Long Context

- DeepSeek-V3: 34.0 (#253)
- GPT-5.2: 44.0 (#78)

| Benchmark | DeepSeek-V3 | GPT-5.2 |
|---|---|---|
| LMArena Longer Query | 1352 | 1428 |
| Fiction.LiveBench | 50% | — |
| CL-bench | — | 18.2% |

## Writing & Preference

- DeepSeek-V3: 57.4 (#130)
- GPT-5.2: 66.8 (#32)

| Benchmark | DeepSeek-V3 | GPT-5.2 |
|---|---|---|
| LMArena Text | 1375 | 1439 |
| LMArena Creative Writing | 1364 | 1401 |
| EQ-Bench Creative Writing | 1472 | 1703 |
| LMArena Multi-Turn | 1389 | 1458 |
| Short-Story Creative Writing | 77% | — |
| WildBench | 83% | — |
| LiveBench Language | 49.1% | — |

## FAQ

### Is DeepSeek-V3 better than GPT-5.2?

GPT-5.2 is the stronger model overall, scoring 54.1 to 39.5 on the Noometry Index. DeepSeek-V3 costs 12× less per token, which makes it the better buy when GPT-5.2's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek-V3 or GPT-5.2?

DeepSeek-V3 is cheaper. It lists at $0.24 per million input tokens and $0.90 per million output tokens; GPT-5.2 lists at $1.75 and $14.

### Is DeepSeek-V3 or GPT-5.2 better for coding?

GPT-5.2 scores higher on coding benchmarks: 51.6 versus 42.3 in the Noometry coding category.

### Which has the bigger context window?

GPT-5.2 does, with 400K tokens against 164K.

### How many benchmarks do DeepSeek-V3 and GPT-5.2 share?

30 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and GPT-5.2 has 67.