# DeepSeek-V3.2-Exp vs GPT-4.1 mini

> DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 33.5 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v3-2-exp-vs-gpt-4-1-mini
- Last updated: 2026-10-10
- Shared benchmarks: 35

## Summary

- They share 35 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 9 categories and GPT-4.1 mini in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek-V3.2-Exp leads 40.8 to 23.1.
- The biggest single-benchmark swing is ARC-AGI-1: 57% for DeepSeek-V3.2-Exp and 3.5% for GPT-4.1 mini.
- DeepSeek-V3.2-Exp is cheaper at $0.26 / $0.38 per million input/output tokens, against $0.40 / $1.60 for GPT-4.1 mini.
- GPT-4.1 mini accepts more context: 1.05M tokens versus 164K.
- DeepSeek-V3.2-Exp has downloadable open weights; the other is API-only.

## Snapshot

| | DeepSeek-V3.2-Exp | GPT-4.1 mini |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 44.9 | 33.5 |
| Rank | 68 | 232 |
| Context | 164K | 1.05M |
| Input $/M | $0.26 | $0.40 |
| Output $/M | $0.38 | $1.60 |
| Weights | Open | Proprietary |

## Coding

- DeepSeek-V3.2-Exp: 46.4 (#61)
- GPT-4.1 mini: 30.4 (#286)

| Benchmark | DeepSeek-V3.2-Exp | GPT-4.1 mini |
|---|---|---|
| SWE-bench Verified (bash only) | 70% | 23.9% |
| Aider Polyglot | 74.2% | 32.4% |
| SciCode | 38.9% | 40.4% |
| WeirdML | 39.5% | 37.6% |
| LMArena Coding | 1454 | 1367 |
| LMArena WebDev | 1362 | — |
| SWE-bench Multilingual | 59% | — |
| BigCodeBench Instruct | — | 48.9% |
| CadEval | — | 16% |

## Agentic & Tool Use

- DeepSeek-V3.2-Exp: 37.9 (#31)
- GPT-4.1 mini: 33.0 (#52)

| Benchmark | DeepSeek-V3.2-Exp | GPT-4.1 mini |
|---|---|---|
| Berkeley Function Calling Leaderboard | 56.7% | 50.5% |
| Terminal-Bench | 39.6% | — |
| TheAgentCompany | 42.9% | — |
| Vending-Bench 2 | 1,034 | — |

## Reasoning

- DeepSeek-V3.2-Exp: 22.0 (#185)
- GPT-4.1 mini: 10.9 (#335)

| Benchmark | DeepSeek-V3.2-Exp | GPT-4.1 mini |
|---|---|---|
| ARC-AGI-2 | 4% | 0% |
| Kagi LLM Benchmark | 52.2% | 48.6% |
| ARC-AGI-1 | 57% | 3.5% |
| CritPt | 2.9% | 0% |
| Chess Puzzles | 14% | 7% |
| LMArena Hard Prompts | 1433 | 1349 |
| DTBench | 87.7% | 68.8% |
| LMCA | 29.1% | 21.1% |
| Epoch Capabilities Index | 146.28 | 135.01 |
| NYT Connections (extended) | 36.7% | — |
| Thematic Generalization | 65% | — |
| Mystery Game Puzzles | — | 7% |

## Math

- DeepSeek-V3.2-Exp: 40.8 (#75)
- GPT-4.1 mini: 23.1 (#264)

| Benchmark | DeepSeek-V3.2-Exp | GPT-4.1 mini |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 87.8% | 44.7% |
| LMArena Math | 1435 | 1343 |
| FrontierMath (Feb 2025 set) | 22.1% | 4.5% |
| FrontierMath (Tiers 1-3) | — | 6.7% |
| MathArena Final-Answer Competitions | 57.7% | — |
| ProofBench | 8% | — |
| Omni-MATH | — | 49.1% |
| MATH Level 5 | — | 87.3% |
| FrontierMath Tier 4 (v1) | 2.1% | — |

## Knowledge

- DeepSeek-V3.2-Exp: 51.6 (#61)
- GPT-4.1 mini: 34.7 (#183)

| Benchmark | DeepSeek-V3.2-Exp | GPT-4.1 mini |
|---|---|---|
| GPQA Diamond | 83.4% | 65.8% |
| LMArena Expert | 1436 | 1338 |
| SimpleQA Verified | — | 12.7% |
| MMLU-Pro | — | 78.3% |
| Vectara Hallucination Rate | 5.3% | — |
| GPQA (HELM) | — | 61.4% |

## Multimodal

- DeepSeek-V3.2-Exp: —
- GPT-4.1 mini: 35.2 (#77)

| Benchmark | DeepSeek-V3.2-Exp | GPT-4.1 mini |
|---|---|---|
| LMArena Vision | — | 1181 |

## Multilingual

- DeepSeek-V3.2-Exp: 52.4 (#85)
- GPT-4.1 mini: 45.9 (#161)

| Benchmark | DeepSeek-V3.2-Exp | GPT-4.1 mini |
|---|---|---|
| LMArena Non-English | 1409 | 1318 |
| LMArena Chinese | 1461 | 1329 |
| LMArena French | 1435 | 1357 |
| LMArena German | 1440 | 1349 |
| LMArena Japanese | 1377 | 1291 |
| LMArena Korean | 1372 | 1298 |
| LMArena Russian | 1423 | 1324 |
| LMArena Spanish | 1441 | 1319 |

## Instruction Following

- DeepSeek-V3.2-Exp: 74.7 (#89)
- GPT-4.1 mini: 73.9 (#113)

| Benchmark | DeepSeek-V3.2-Exp | GPT-4.1 mini |
|---|---|---|
| LMArena Instruction Following | 1413 | 1332 |
| IFEval | — | 90.4% |

## Long Context

- DeepSeek-V3.2-Exp: 47.6 (#15)
- GPT-4.1 mini: 31.8 (#270)

| Benchmark | DeepSeek-V3.2-Exp | GPT-4.1 mini |
|---|---|---|
| Fiction.LiveBench | 83.3% | 44.4% |
| LMArena Longer Query | 1428 | 1344 |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |

## Writing & Preference

- DeepSeek-V3.2-Exp: 62.5 (#73)
- GPT-4.1 mini: 48.8 (#193)

| Benchmark | DeepSeek-V3.2-Exp | GPT-4.1 mini |
|---|---|---|
| LMArena Text | 1425 | 1341 |
| LMArena Creative Writing | 1403 | 1300 |
| EQ-Bench Creative Writing | 1515 | 1147 |
| LMArena Multi-Turn | 1427 | 1354 |
| WildBench | — | 83.8% |

## FAQ

### Is DeepSeek-V3.2-Exp better than GPT-4.1 mini?

DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 33.5 on the Noometry Index.

### Which is cheaper, DeepSeek-V3.2-Exp or GPT-4.1 mini?

DeepSeek-V3.2-Exp is cheaper. It lists at $0.26 per million input tokens and $0.38 per million output tokens; GPT-4.1 mini lists at $0.40 and $1.60.

### Is DeepSeek-V3.2-Exp or GPT-4.1 mini better for coding?

DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.4 versus 30.4 in the Noometry coding category.

### Which has the bigger context window?

GPT-4.1 mini does, with 1.05M tokens against 164K.

### How many benchmarks do DeepSeek-V3.2-Exp and GPT-4.1 mini share?

35 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 48 scored results on Noometry and GPT-4.1 mini has 47.