# DeepSeek-V3.2-Exp vs GPT-5.2 Codex

> DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 42.2 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v3-2-exp-vs-gpt-5-2-codex
- Last updated: 2026-10-10
- Shared benchmarks: 4

## Summary

- They share 4 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 1 category and GPT-5.2 Codex in 1 category; one gap is clear of the uncertainty.
- The biggest single-benchmark swing is Terminal-Bench: 39.6% for DeepSeek-V3.2-Exp and 66.5% for GPT-5.2 Codex.
- DeepSeek-V3.2-Exp is cheaper at $0.26 / $0.38 per million input/output tokens, against $1.75 / $14 for GPT-5.2 Codex.
- GPT-5.2 Codex accepts more context: 400K tokens versus 164K.
- DeepSeek-V3.2-Exp has downloadable open weights; the other is API-only.

## Snapshot

| | DeepSeek-V3.2-Exp | GPT-5.2 Codex |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 44.9 | 42.2 |
| Rank | 68 | 107 |
| Context | 164K | 400K |
| Input $/M | $0.26 | $1.75 |
| Output $/M | $0.38 | $14 |
| Weights | Open | Proprietary |

## Coding

- DeepSeek-V3.2-Exp: 46.4 (#61)
- GPT-5.2 Codex: 45.4 (#66)

| Benchmark | DeepSeek-V3.2-Exp | GPT-5.2 Codex |
|---|---|---|
| SWE-bench Verified (bash only) | 70% | 72.8% |
| LMArena WebDev | 1362 | 1339 |
| SWE-bench Multilingual | 59% | 66.3% |
| Aider Polyglot | 74.2% | — |
| SciCode | 38.9% | — |
| WeirdML | 39.5% | — |
| LMArena Coding | 1454 | — |
| ALE-Bench | — | 1,300 |

## Agentic & Tool Use

- DeepSeek-V3.2-Exp: 37.9 (#31)
- GPT-5.2 Codex: 40.7 (#20)

| Benchmark | DeepSeek-V3.2-Exp | GPT-5.2 Codex |
|---|---|---|
| Terminal-Bench | 39.6% | 66.5% |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| TheAgentCompany | 42.9% | — |
| Vending-Bench 2 | 1,034 | — |

## Reasoning

- DeepSeek-V3.2-Exp: 22.0 (#185)
- GPT-5.2 Codex: —

| Benchmark | DeepSeek-V3.2-Exp | GPT-5.2 Codex |
|---|---|---|
| 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% | — |
| LMArena Hard Prompts | 1433 | — |
| DTBench | 87.7% | — |
| LMCA | 29.1% | — |
| Epoch Capabilities Index | 146.28 | — |

## Math

- DeepSeek-V3.2-Exp: 40.8 (#75)
- GPT-5.2 Codex: —

| Benchmark | DeepSeek-V3.2-Exp | GPT-5.2 Codex |
|---|---|---|
| MathArena Final-Answer Competitions | 57.7% | — |
| OTIS Mock AIME 2024-2025 | 87.8% | — |
| ProofBench | 8% | — |
| LMArena Math | 1435 | — |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |

## Knowledge

- DeepSeek-V3.2-Exp: 51.6 (#61)
- GPT-5.2 Codex: —

| Benchmark | DeepSeek-V3.2-Exp | GPT-5.2 Codex |
|---|---|---|
| GPQA Diamond | 83.4% | — |
| Vectara Hallucination Rate | 5.3% | — |
| LMArena Expert | 1436 | — |

## Multilingual

- DeepSeek-V3.2-Exp: 52.4 (#85)
- GPT-5.2 Codex: —

| Benchmark | DeepSeek-V3.2-Exp | GPT-5.2 Codex |
|---|---|---|
| LMArena Non-English | 1409 | — |
| LMArena Chinese | 1461 | — |
| LMArena French | 1435 | — |
| LMArena German | 1440 | — |
| LMArena Japanese | 1377 | — |
| LMArena Korean | 1372 | — |
| LMArena Russian | 1423 | — |
| LMArena Spanish | 1441 | — |

## Instruction Following

- DeepSeek-V3.2-Exp: 74.7 (#89)
- GPT-5.2 Codex: —

| Benchmark | DeepSeek-V3.2-Exp | GPT-5.2 Codex |
|---|---|---|
| LMArena Instruction Following | 1413 | — |

## Long Context

- DeepSeek-V3.2-Exp: 47.6 (#15)
- GPT-5.2 Codex: —

| Benchmark | DeepSeek-V3.2-Exp | GPT-5.2 Codex |
|---|---|---|
| Fiction.LiveBench | 83.3% | — |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |
| LMArena Longer Query | 1428 | — |

## Writing & Preference

- DeepSeek-V3.2-Exp: 62.5 (#73)
- GPT-5.2 Codex: —

| Benchmark | DeepSeek-V3.2-Exp | GPT-5.2 Codex |
|---|---|---|
| LMArena Text | 1425 | — |
| LMArena Creative Writing | 1403 | — |
| EQ-Bench Creative Writing | 1515 | — |
| LMArena Multi-Turn | 1427 | — |

## FAQ

### Is DeepSeek-V3.2-Exp better than GPT-5.2 Codex?

DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 42.2 on the Noometry Index.

### Which is cheaper, DeepSeek-V3.2-Exp or GPT-5.2 Codex?

DeepSeek-V3.2-Exp is cheaper. It lists at $0.26 per million input tokens and $0.38 per million output tokens; GPT-5.2 Codex lists at $1.75 and $14.

### Is DeepSeek-V3.2-Exp or GPT-5.2 Codex better for coding?

They score almost the same on coding (46.4 vs 45.4); test both on your own repository before choosing.

### Which has the bigger context window?

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

### How many benchmarks do DeepSeek-V3.2-Exp and GPT-5.2 Codex share?

4 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 48 scored results on Noometry and GPT-5.2 Codex has 5.