# DeepSeek-V3.2-Exp vs GPT-5.3 Chat

> DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 42.1 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v3-2-exp-vs-gpt-5-3-chat
- Last updated: 2026-10-10
- Shared benchmarks: 18

## Summary

- They share 18 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 6 categories and GPT-5.3 Chat in 2 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-V3.2-Exp leads 51.6 to 38.2.
- DeepSeek-V3.2-Exp is cheaper at $0.26 / $0.38 per million input/output tokens, against $1.75 / $14 for GPT-5.3 Chat.
- DeepSeek-V3.2-Exp accepts more context: 164K tokens versus 128K.
- DeepSeek-V3.2-Exp has downloadable open weights; the other is API-only.

## Snapshot

| | DeepSeek-V3.2-Exp | GPT-5.3 Chat |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 44.9 | 42.1 |
| Rank | 68 | 109 |
| Context | 164K | 128K |
| Input $/M | $0.26 | $1.75 |
| Output $/M | $0.38 | $14 |
| Weights | Open | Proprietary |

## Coding

- DeepSeek-V3.2-Exp: 46.4 (#61)
- GPT-5.3 Chat: 40.8 (#124)

| Benchmark | DeepSeek-V3.2-Exp | GPT-5.3 Chat |
|---|---|---|
| LMArena Coding | 1454 | 1408 |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| LMArena WebDev | 1362 | — |
| SWE-bench Multilingual | 59% | — |
| SciCode | 38.9% | — |
| WeirdML | 39.5% | — |

## Agentic & Tool Use

- DeepSeek-V3.2-Exp: 37.9 (#31)
- GPT-5.3 Chat: —

| Benchmark | DeepSeek-V3.2-Exp | GPT-5.3 Chat |
|---|---|---|
| Terminal-Bench | 39.6% | — |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| TheAgentCompany | 42.9% | — |
| Vending-Bench 2 | 1,034 | — |

## Reasoning

- DeepSeek-V3.2-Exp: 22.0 (#185)
- GPT-5.3 Chat: 27.3 (#105)

| Benchmark | DeepSeek-V3.2-Exp | GPT-5.3 Chat |
|---|---|---|
| LMArena Hard Prompts | 1433 | 1399 |
| ARC-AGI-2 | 4% | — |
| Kagi LLM Benchmark | 52.2% | — |
| NYT Connections (extended) | 36.7% | — |
| ARC-AGI-1 | 57% | — |
| CritPt | 2.9% | — |
| Chess Puzzles | 14% | — |
| Thematic Generalization | 65% | — |
| DTBench | 87.7% | — |
| LMCA | 29.1% | — |
| Epoch Capabilities Index | 146.28 | — |

## Math

- DeepSeek-V3.2-Exp: 40.8 (#75)
- GPT-5.3 Chat: 35.4 (#146)

| Benchmark | DeepSeek-V3.2-Exp | GPT-5.3 Chat |
|---|---|---|
| LMArena Math | 1435 | 1389 |
| MathArena Final-Answer Competitions | 57.7% | — |
| OTIS Mock AIME 2024-2025 | 87.8% | — |
| ProofBench | 8% | — |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |

## Knowledge

- DeepSeek-V3.2-Exp: 51.6 (#61)
- GPT-5.3 Chat: 38.2 (#137)

| Benchmark | DeepSeek-V3.2-Exp | GPT-5.3 Chat |
|---|---|---|
| LMArena Expert | 1436 | 1397 |
| GPQA Diamond | 83.4% | — |
| Vectara Hallucination Rate | 5.3% | — |

## Multilingual

- DeepSeek-V3.2-Exp: 52.4 (#85)
- GPT-5.3 Chat: 50.5 (#119)

| Benchmark | DeepSeek-V3.2-Exp | GPT-5.3 Chat |
|---|---|---|
| LMArena Non-English | 1409 | 1383 |
| LMArena Chinese | 1461 | 1433 |
| LMArena French | 1435 | 1398 |
| LMArena German | 1440 | 1384 |
| LMArena Japanese | 1377 | 1351 |
| LMArena Korean | 1372 | 1347 |
| LMArena Russian | 1423 | 1400 |
| LMArena Spanish | 1441 | 1371 |

## Instruction Following

- DeepSeek-V3.2-Exp: 74.7 (#89)
- GPT-5.3 Chat: 73.0 (#124)

| Benchmark | DeepSeek-V3.2-Exp | GPT-5.3 Chat |
|---|---|---|
| LMArena Instruction Following | 1413 | 1378 |

## Long Context

- DeepSeek-V3.2-Exp: 47.6 (#15)
- GPT-5.3 Chat: 42.6 (#115)

| Benchmark | DeepSeek-V3.2-Exp | GPT-5.3 Chat |
|---|---|---|
| LMArena Longer Query | 1428 | 1396 |
| Fiction.LiveBench | 83.3% | — |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |

## Writing & Preference

- DeepSeek-V3.2-Exp: 62.5 (#73)
- GPT-5.3 Chat: 63.3 (#65)

| Benchmark | DeepSeek-V3.2-Exp | GPT-5.3 Chat |
|---|---|---|
| LMArena Text | 1425 | 1389 |
| LMArena Creative Writing | 1403 | 1356 |
| EQ-Bench Creative Writing | 1515 | 1690 |
| LMArena Multi-Turn | 1427 | 1413 |

## FAQ

### Is DeepSeek-V3.2-Exp better than GPT-5.3 Chat?

DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 42.1 on the Noometry Index.

### Which is cheaper, DeepSeek-V3.2-Exp or GPT-5.3 Chat?

DeepSeek-V3.2-Exp is cheaper. It lists at $0.26 per million input tokens and $0.38 per million output tokens; GPT-5.3 Chat lists at $1.75 and $14.

### Is DeepSeek-V3.2-Exp or GPT-5.3 Chat better for coding?

DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.4 versus 40.8 in the Noometry coding category.

### Which has the bigger context window?

DeepSeek-V3.2-Exp does, with 164K tokens against 128K.

### How many benchmarks do DeepSeek-V3.2-Exp and GPT-5.3 Chat share?

18 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 48 scored results on Noometry and GPT-5.3 Chat has 18.