# DeepSeek-V3.2-Exp vs Gemini 3.7 Flash

> Gemini 3.7 Flash is the stronger model overall, scoring 59.8 to 44.9 on the Noometry Index. DeepSeek-V3.2-Exp costs 5.2× less per token, which makes it the better buy when Gemini 3.7 Flash's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v3-2-exp-vs-gemini-3-7-flash
- Last updated: 2026-10-10
- Shared benchmarks: 31

## Summary

- They share 31 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 1 category and Gemini 3.7 Flash in 8 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Gemini 3.7 Flash leads 70.1 to 22.0.
- The biggest single-benchmark swing is ARC-AGI-2: 4% for DeepSeek-V3.2-Exp and 84.6% for Gemini 3.7 Flash.
- DeepSeek-V3.2-Exp is cheaper at $0.26 / $0.38 per million input/output tokens, against $0.75 / $3.75 for Gemini 3.7 Flash.
- Gemini 3.7 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 3.7 Flash |
|---|---|---|
| Provider | DeepSeek | Google |
| Noometry Index | 44.9 | 59.8 |
| Rank | 68 | 11 |
| Context | 164K | 1.05M |
| Input $/M | $0.26 | $0.75 |
| Output $/M | $0.38 | $3.75 |
| Weights | Open | Proprietary |

## Coding

- DeepSeek-V3.2-Exp: 46.4 (#61)
- Gemini 3.7 Flash: 55.7 (#19)

| Benchmark | DeepSeek-V3.2-Exp | Gemini 3.7 Flash |
|---|---|---|
| LMArena WebDev | 1362 | 1593 |
| SciCode | 38.9% | 59.8% |
| LMArena Coding | 1454 | 1499 |
| DeepSWE | — | 65.5% |
| FrontierCode | — | 43.6% |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| SWE-bench Multilingual | 59% | — |
| FrontierSWE | — | 20.3% |
| WeirdML | 39.5% | — |
| ALE-Bench | — | 904.3 |

## Agentic & Tool Use

- DeepSeek-V3.2-Exp: 37.9 (#31)
- Gemini 3.7 Flash: 42.2 (#18)

| Benchmark | DeepSeek-V3.2-Exp | Gemini 3.7 Flash |
|---|---|---|
| Terminal-Bench | 39.6% | — |
| APEX-Agents | — | 67.8% |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| Remote Labor Index | — | 5% |
| TheAgentCompany | 42.9% | — |
| GDP.pdf | — | 23.8% |
| Vending-Bench 2 | 1,034 | — |

## Reasoning

- DeepSeek-V3.2-Exp: 22.0 (#185)
- Gemini 3.7 Flash: 70.1 (#12)

| Benchmark | DeepSeek-V3.2-Exp | Gemini 3.7 Flash |
|---|---|---|
| ARC-AGI-2 | 4% | 84.6% |
| NYT Connections (extended) | 36.7% | 94% |
| ARC-AGI-1 | 57% | 95.5% |
| CritPt | 2.9% | 14.3% |
| Chess Puzzles | 14% | 47% |
| LMArena Hard Prompts | 1433 | 1497 |
| DTBench | 87.7% | 96.8% |
| LMCA | 29.1% | 50.4% |
| Epoch Capabilities Index | 146.28 | 157.72 |
| Kagi LLM Benchmark | 52.2% | — |
| Thematic Generalization | 65% | — |
| Mystery Game Puzzles | — | 37% |

## Math

- DeepSeek-V3.2-Exp: 40.8 (#75)
- Gemini 3.7 Flash: 69.5 (#18)

| Benchmark | DeepSeek-V3.2-Exp | Gemini 3.7 Flash |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 87.8% | 97.2% |
| ProofBench | 8% | 58% |
| LMArena Math | 1435 | 1511 |
| FrontierMath (Tiers 1-3) | — | 71.6% |
| FrontierMath Tier 4 | — | 36.6% |
| MathArena Final-Answer Competitions | 57.7% | — |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |

## Knowledge

- DeepSeek-V3.2-Exp: 51.6 (#61)
- Gemini 3.7 Flash: 69.8 (#4)

| Benchmark | DeepSeek-V3.2-Exp | Gemini 3.7 Flash |
|---|---|---|
| GPQA Diamond | 83.4% | 94.8% |
| LMArena Expert | 1436 | 1508 |
| SimpleQA Verified | — | 69.2% |
| Vectara Hallucination Rate | 5.3% | — |

## Multimodal

- DeepSeek-V3.2-Exp: —
- Gemini 3.7 Flash: 36.9 (#69)

| Benchmark | DeepSeek-V3.2-Exp | Gemini 3.7 Flash |
|---|---|---|
| LMArena Vision | — | 1314 |
| Furniture Assembly | — | 26.7% |

## Multilingual

- DeepSeek-V3.2-Exp: 52.4 (#85)
- Gemini 3.7 Flash: 57.8 (#7)

| Benchmark | DeepSeek-V3.2-Exp | Gemini 3.7 Flash |
|---|---|---|
| LMArena Non-English | 1409 | 1485 |
| LMArena Chinese | 1461 | 1553 |
| LMArena French | 1435 | 1512 |
| LMArena German | 1440 | 1485 |
| LMArena Japanese | 1377 | 1506 |
| LMArena Korean | 1372 | 1486 |
| LMArena Russian | 1423 | 1519 |
| LMArena Spanish | 1441 | 1507 |

## Instruction Following

- DeepSeek-V3.2-Exp: 74.7 (#89)
- Gemini 3.7 Flash: 78.0 (#12)

| Benchmark | DeepSeek-V3.2-Exp | Gemini 3.7 Flash |
|---|---|---|
| LMArena Instruction Following | 1413 | 1486 |

## Long Context

- DeepSeek-V3.2-Exp: 47.6 (#15)
- Gemini 3.7 Flash: 45.8 (#28)

| Benchmark | DeepSeek-V3.2-Exp | Gemini 3.7 Flash |
|---|---|---|
| LMArena Longer Query | 1428 | 1493 |
| Fiction.LiveBench | 83.3% | — |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |

## Writing & Preference

- DeepSeek-V3.2-Exp: 62.5 (#73)
- Gemini 3.7 Flash: 71.4 (#19)

| Benchmark | DeepSeek-V3.2-Exp | Gemini 3.7 Flash |
|---|---|---|
| LMArena Text | 1425 | 1487 |
| LMArena Creative Writing | 1403 | 1490 |
| EQ-Bench Creative Writing | 1515 | 1723 |
| LMArena Multi-Turn | 1427 | 1495 |

## FAQ

### Is DeepSeek-V3.2-Exp better than Gemini 3.7 Flash?

Gemini 3.7 Flash is the stronger model overall, scoring 59.8 to 44.9 on the Noometry Index. DeepSeek-V3.2-Exp costs 5.2× less per token, which makes it the better buy when Gemini 3.7 Flash's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek-V3.2-Exp or Gemini 3.7 Flash?

DeepSeek-V3.2-Exp is cheaper. It lists at $0.26 per million input tokens and $0.38 per million output tokens; Gemini 3.7 Flash lists at $0.75 and $3.75.

### Is DeepSeek-V3.2-Exp or Gemini 3.7 Flash better for coding?

Gemini 3.7 Flash scores higher on coding benchmarks: 55.7 versus 46.4 in the Noometry coding category.

### Which has the bigger context window?

Gemini 3.7 Flash does, with 1.05M tokens against 164K.

### How many benchmarks do DeepSeek-V3.2-Exp and Gemini 3.7 Flash share?

31 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 48 scored results on Noometry and Gemini 3.7 Flash has 44.