# DeepSeek-V3 vs GPT-5.3 Chat

> GPT-5.3 Chat is the stronger model overall, scoring 42.8 to 39.5 on the Noometry Index. DeepSeek-V3 costs 12× less per token, which makes it the better buy when GPT-5.3 Chat's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v3-vs-gpt-5-3-chat
- Last updated: 2026-10-10
- Shared benchmarks: 18

## Summary

- They share 18 benchmarks with published results for both. DeepSeek-V3 scores higher in 1 category and GPT-5.3 Chat in 7 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in long context, where GPT-5.3 Chat leads 42.6 to 34.0.
- DeepSeek-V3 is cheaper at $0.24 / $0.90 per million input/output tokens, against $1.75 / $14 for GPT-5.3 Chat.
- DeepSeek-V3 accepts more context: 164K tokens versus 128K.
- DeepSeek-V3 has downloadable open weights; the other is API-only.

## Snapshot

| | DeepSeek-V3 | GPT-5.3 Chat |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 39.5 | 42.8 |
| Rank | 166 | 109 |
| Context | 164K | 128K |
| Input $/M | $0.24 | $1.75 |
| Output $/M | $0.90 | $14 |
| Weights | Open | Proprietary |

## Coding

- DeepSeek-V3: 42.3 (#106)
- GPT-5.3 Chat: 41.4 (#124)

| Benchmark | DeepSeek-V3 | GPT-5.3 Chat |
|---|---|---|
| LMArena Coding | 1368 | 1408 |
| Aider Polyglot | 55.1% | — |
| SciCode | 35.8% | — |
| WeirdML | 36.1% | — |
| BigCodeBench Instruct | 50% | — |
| LiveBench Coding | 70.9% | — |
| BigCodeBench Complete | 62.2% | — |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73% | — |

## Agentic & Tool Use

- DeepSeek-V3: —
- GPT-5.3 Chat: —

| Benchmark | DeepSeek-V3 | GPT-5.3 Chat |
|---|---|---|
| METR Time Horizons | 49.6% | — |

## Reasoning

- DeepSeek-V3: 20.5 (#236)
- GPT-5.3 Chat: 28.5 (#102)

| Benchmark | DeepSeek-V3 | GPT-5.3 Chat |
|---|---|---|
| LMArena Hard Prompts | 1365 | 1399 |
| SimpleBench | 27.2% | — |
| Kagi LLM Benchmark | 52.3% | — |
| CritPt | 0% | — |
| LiveBench Reasoning | 65.8% | — |
| DTBench | 64.8% | — |
| LiveBench Data Analysis | 60.9% | — |
| LMCA | 15.5% | — |
| BIG-Bench Hard | 87.5% | — |
| Epoch Capabilities Index | 135.94 | — |
| ForecastBench | 59.1 | — |
| HellaSwag | 88.9% | — |
| LiveBench | 66.9% | — |
| PIQA | 84.7% | — |
| WinoGrande | 85.2% | — |

## Math

- DeepSeek-V3: 32.1 (#219)
- GPT-5.3 Chat: 38.2 (#142)

| Benchmark | DeepSeek-V3 | GPT-5.3 Chat |
|---|---|---|
| LMArena Math | 1373 | 1389 |
| OTIS Mock AIME 2024-2025 | 37.8% | — |
| Omni-MATH | 40.3% | — |
| LiveBench Math | 73.5% | — |
| MATH Level 5 | 75.5% | — |
| FrontierMath (Feb 2025 set) | 1.7% | — |

## Knowledge

- DeepSeek-V3: 37.5 (#155)
- GPT-5.3 Chat: 38.8 (#140)

| Benchmark | DeepSeek-V3 | GPT-5.3 Chat |
|---|---|---|
| LMArena Expert | 1351 | 1397 |
| GPQA Diamond | 67.6% | — |
| MMLU-Pro | 72.3% | — |
| Confabulations | 26.1% | — |
| Vectara Hallucination Rate | 6.1% | — |
| GPQA (HELM) | 53.8% | — |
| ARC (AI2) Challenge | 95.3% | — |
| MMLU | 87.2% | — |
| TriviaQA | 82.9% | — |

## Multilingual

- DeepSeek-V3: 48.5 (#143)
- GPT-5.3 Chat: 50.3 (#124)

| Benchmark | DeepSeek-V3 | GPT-5.3 Chat |
|---|---|---|
| LMArena Non-English | 1358 | 1382 |
| LMArena Chinese | 1391 | 1432 |
| LMArena French | 1385 | 1397 |
| LMArena German | 1374 | 1384 |
| LMArena Japanese | 1333 | 1352 |
| LMArena Korean | 1319 | 1346 |
| LMArena Russian | 1373 | 1400 |
| LMArena Spanish | 1358 | 1371 |

## Instruction Following

- DeepSeek-V3: 72.8 (#130)
- GPT-5.3 Chat: 72.8 (#129)

| Benchmark | DeepSeek-V3 | GPT-5.3 Chat |
|---|---|---|
| LMArena Instruction Following | 1345 | 1378 |
| LiveBench Instruction Following | 81.5% | — |
| IFEval | 83.2% | — |

## Long Context

- DeepSeek-V3: 34.0 (#253)
- GPT-5.3 Chat: 42.6 (#120)

| Benchmark | DeepSeek-V3 | GPT-5.3 Chat |
|---|---|---|
| LMArena Longer Query | 1352 | 1396 |
| Fiction.LiveBench | 50% | — |

## Writing & Preference

- DeepSeek-V3: 57.4 (#130)
- GPT-5.3 Chat: 63.1 (#68)

| Benchmark | DeepSeek-V3 | GPT-5.3 Chat |
|---|---|---|
| LMArena Text | 1375 | 1389 |
| LMArena Creative Writing | 1364 | 1355 |
| EQ-Bench Creative Writing | 1472 | 1690 |
| LMArena Multi-Turn | 1389 | 1412 |
| Short-Story Creative Writing | 77% | — |
| WildBench | 83% | — |
| LiveBench Language | 49.1% | — |

## FAQ

### Is DeepSeek-V3 better than GPT-5.3 Chat?

GPT-5.3 Chat is the stronger model overall, scoring 42.8 to 39.5 on the Noometry Index. DeepSeek-V3 costs 12× less per token, which makes it the better buy when GPT-5.3 Chat's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek-V3 or GPT-5.3 Chat?

DeepSeek-V3 is cheaper. It lists at $0.24 per million input tokens and $0.90 per million output tokens; GPT-5.3 Chat lists at $1.75 and $14.

### Is DeepSeek-V3 or GPT-5.3 Chat better for coding?

They score almost the same on coding (42.3 vs 41.4); test both on your own repository before choosing.

### Which has the bigger context window?

DeepSeek-V3 does, with 164K tokens against 128K.

### How many benchmarks do DeepSeek-V3 and GPT-5.3 Chat share?

18 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and GPT-5.3 Chat has 18.