# DeepSeek-V3.2-Exp vs Gemini 2.5 Flash

> DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 39.3 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v3-2-exp-vs-gemini-2-5-flash
- Last updated: 2026-10-10
- Shared benchmarks: 37

## Summary

- They share 37 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 7 categories and Gemini 2.5 Flash in 2 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-V3.2-Exp leads 51.7 to 36.4.
- The biggest single-benchmark swing is SWE-bench Verified (bash only): 70% for DeepSeek-V3.2-Exp and 28.7% for Gemini 2.5 Flash.
- DeepSeek-V3.2-Exp is cheaper at $0.26 / $0.38 per million input/output tokens, against $0.30 / $2.50 for Gemini 2.5 Flash.
- Gemini 2.5 Flash 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 | Gemini 2.5 Flash |
|---|---|---|
| Provider | DeepSeek | Google |
| Noometry Index | 44.3 | 39.3 |
| Rank | 78 | 170 |
| Context | 164K | 1.05M |
| Input $/M | $0.26 | $0.30 |
| Output $/M | $0.38 | $2.50 |
| Weights | Open | Proprietary |

## Coding

- DeepSeek-V3.2-Exp: 46.5 (#65)
- Gemini 2.5 Flash: 35.8 (#220)

| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.5 Flash |
|---|---|---|
| SWE-bench Verified (bash only) | 70% | 28.7% |
| Aider Polyglot | 74.2% | 55.1% |
| WeirdML | 39.5% | 41.9% |
| LMArena Coding | 1454 | 1424 |
| LMArena WebDev | 1362 | — |
| SWE-bench Multilingual | 59% | — |
| SciCode | 38.9% | — |
| ALE-Bench | — | 661.88 |

## Agentic & Tool Use

- DeepSeek-V3.2-Exp: 32.7 (#59)
- Gemini 2.5 Flash: 30.8 (#74)

| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.5 Flash |
|---|---|---|
| Terminal-Bench | 39.6% | 17.1% |
| Berkeley Function Calling Leaderboard | 56.7% | 56.2% |
| TheAgentCompany | 42.9% | 41.1% |
| Vending-Bench 2 | 1,034 | 548.84 |
| APEX-Agents | 21.3% | — |
| BALROG | — | 33.5% |

## Reasoning

- DeepSeek-V3.2-Exp: 22.1 (#208)
- Gemini 2.5 Flash: 18.1 (#286)

| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.5 Flash |
|---|---|---|
| ARC-AGI-2 | 4% | 2.5% |
| Kagi LLM Benchmark | 52.2% | 56.8% |
| ARC-AGI-1 | 57% | 33.3% |
| CritPt | 2.9% | 1.1% |
| LMArena Hard Prompts | 1434 | 1422 |
| DTBench | 87.7% | 76.5% |
| LMCA | 29.1% | 27.5% |
| Epoch Capabilities Index | 146.27 | 143.03 |
| SimpleBench | — | 41.2% |
| NYT Connections (extended) | 36.7% | — |
| Chess Puzzles | 14% | — |
| EnigmaEval | — | 2.7% |
| Thematic Generalization | 65% | — |
| ForecastBench | — | 60.6 |

## Math

- DeepSeek-V3.2-Exp: 41.7 (#87)
- Gemini 2.5 Flash: 39.9 (#98)

| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.5 Flash |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 87.8% | 73.1% |
| LMArena Math | 1435 | 1415 |
| FrontierMath (Feb 2025 set) | 22.1% | 4.8% |
| FrontierMath Tier 4 (v1) | 2.1% | 4.2% |
| MathArena Final-Answer Competitions | 57.7% | — |
| ProofBench | 8% | — |
| Omni-MATH | — | 38.5% |

## Knowledge

- DeepSeek-V3.2-Exp: 51.7 (#66)
- Gemini 2.5 Flash: 36.4 (#168)

| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.5 Flash |
|---|---|---|
| Vectara Hallucination Rate | 5.3% | 7.8% |
| LMArena Expert | 1436 | 1426 |
| GPQA Diamond | 83.4% | — |
| Humanity's Last Exam | — | 12.1% |
| MMLU-Pro | — | 63.9% |
| Confabulations | — | 16.8% |
| GPQA (HELM) | — | 39% |

## Multimodal

- DeepSeek-V3.2-Exp: —
- Gemini 2.5 Flash: 41.8 (#32)

| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.5 Flash |
|---|---|---|
| LMArena Vision | — | 1253 |
| GeoBench | — | 76% |
| VPCT | — | 46.2% |
| SpatialViz-Bench | — | 36.9% |

## Multilingual

- DeepSeek-V3.2-Exp: 52.2 (#90)
- Gemini 2.5 Flash: 52.3 (#88)

| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.5 Flash |
|---|---|---|
| LMArena Non-English | 1409 | 1409 |
| LMArena Chinese | 1461 | 1450 |
| LMArena French | 1433 | 1433 |
| LMArena German | 1440 | 1418 |
| LMArena Japanese | 1374 | 1405 |
| LMArena Korean | 1371 | 1385 |
| LMArena Russian | 1424 | 1415 |
| LMArena Spanish | 1440 | 1421 |

## Instruction Following

- DeepSeek-V3.2-Exp: 74.5 (#93)
- Gemini 2.5 Flash: 75.7 (#54)

| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.5 Flash |
|---|---|---|
| LMArena Instruction Following | 1413 | 1405 |
| IFEval | — | 89.8% |

## Long Context

- DeepSeek-V3.2-Exp: 47.6 (#16)
- Gemini 2.5 Flash: 47.5 (#17)

| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.5 Flash |
|---|---|---|
| Fiction.LiveBench | 83.3% | 77.8% |
| LMArena Longer Query | 1428 | 1419 |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |

## Writing & Preference

- DeepSeek-V3.2-Exp: 62.4 (#77)
- Gemini 2.5 Flash: 53.8 (#157)

| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.5 Flash |
|---|---|---|
| LMArena Text | 1425 | 1417 |
| LMArena Creative Writing | 1403 | 1400 |
| EQ-Bench Creative Writing | 1515 | 1137 |
| LMArena Multi-Turn | 1427 | 1408 |
| Short-Story Creative Writing | — | 76.5% |
| WildBench | — | 81.7% |

## FAQ

### Is DeepSeek-V3.2-Exp better than Gemini 2.5 Flash?

DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 39.3 on the Noometry Index.

### Which is cheaper, DeepSeek-V3.2-Exp or Gemini 2.5 Flash?

DeepSeek-V3.2-Exp is cheaper. It lists at $0.26 per million input tokens and $0.38 per million output tokens; Gemini 2.5 Flash lists at $0.30 and $2.50.

### Is DeepSeek-V3.2-Exp or Gemini 2.5 Flash better for coding?

DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.5 versus 35.8 in the Noometry coding category.

### Which has the bigger context window?

Gemini 2.5 Flash does, with 1.05M tokens against 164K.

### How many benchmarks do DeepSeek-V3.2-Exp and Gemini 2.5 Flash share?

37 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 49 scored results on Noometry and Gemini 2.5 Flash has 54.