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

> DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 36.4 on the Noometry Index. Gemini 2.5 Flash-Lite costs 1.7× less per token, which makes it the better buy when DeepSeek-V3.2-Exp's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v3-2-exp-vs-gemini-2-5-flash-lite
- Last updated: 2026-10-10
- Shared benchmarks: 25

## Summary

- They share 25 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 9 categories and Gemini 2.5 Flash-Lite in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-V3.2-Exp leads 51.6 to 32.1.
- The biggest single-benchmark swing is Fiction.LiveBench: 83.3% for DeepSeek-V3.2-Exp and 47.2% for Gemini 2.5 Flash-Lite.
- Gemini 2.5 Flash-Lite is cheaper at $0.10 / $0.40 per million input/output tokens, against $0.26 / $0.38 for DeepSeek-V3.2-Exp.
- Gemini 2.5 Flash-Lite 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-Lite |
|---|---|---|
| Provider | DeepSeek | Google |
| Noometry Index | 44.9 | 36.4 |
| Rank | 68 | 208 |
| Context | 164K | 1.05M |
| Input $/M | $0.26 | $0.10 |
| Output $/M | $0.38 | $0.40 |
| Weights | Open | Proprietary |

## Coding

- DeepSeek-V3.2-Exp: 46.4 (#61)
- Gemini 2.5 Flash-Lite: 38.0 (#167)

| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.5 Flash-Lite |
|---|---|---|
| WeirdML | 39.5% | 35.2% |
| LMArena Coding | 1454 | 1373 |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| LMArena WebDev | 1362 | — |
| SWE-bench Multilingual | 59% | — |
| SciCode | 38.9% | — |
| ALE-Bench | — | 325.9 |

## Agentic & Tool Use

- DeepSeek-V3.2-Exp: 37.9 (#31)
- Gemini 2.5 Flash-Lite: 27.8 (#89)

| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.5 Flash-Lite |
|---|---|---|
| Berkeley Function Calling Leaderboard | 56.7% | 36.9% |
| Terminal-Bench | 39.6% | — |
| TheAgentCompany | 42.9% | — |
| Vending-Bench 2 | 1,034 | — |

## Reasoning

- DeepSeek-V3.2-Exp: 22.0 (#185)
- Gemini 2.5 Flash-Lite: 21.5 (#196)

| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.5 Flash-Lite |
|---|---|---|
| Kagi LLM Benchmark | 52.2% | 40.5% |
| LMArena Hard Prompts | 1433 | 1377 |
| DTBench | 87.7% | 62.8% |
| LMCA | 29.1% | 18.1% |
| Epoch Capabilities Index | 146.28 | 133.93 |
| ARC-AGI-2 | 4% | — |
| NYT Connections (extended) | 36.7% | — |
| ARC-AGI-1 | 57% | — |
| CritPt | 2.9% | — |
| Chess Puzzles | 14% | — |
| Thematic Generalization | 65% | — |

## Math

- DeepSeek-V3.2-Exp: 40.8 (#75)
- Gemini 2.5 Flash-Lite: 35.5 (#144)

| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.5 Flash-Lite |
|---|---|---|
| LMArena Math | 1435 | 1373 |
| MathArena Final-Answer Competitions | 57.7% | — |
| OTIS Mock AIME 2024-2025 | 87.8% | — |
| ProofBench | 8% | — |
| Omni-MATH | — | 48% |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |

## Knowledge

- DeepSeek-V3.2-Exp: 51.6 (#61)
- Gemini 2.5 Flash-Lite: 32.1 (#206)

| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.5 Flash-Lite |
|---|---|---|
| Vectara Hallucination Rate | 5.3% | 3.3% |
| LMArena Expert | 1436 | 1373 |
| GPQA Diamond | 83.4% | — |
| MMLU-Pro | — | 53.7% |
| GPQA (HELM) | — | 30.9% |

## Multimodal

- DeepSeek-V3.2-Exp: —
- Gemini 2.5 Flash-Lite: 28.6 (#109)

| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.5 Flash-Lite |
|---|---|---|
| LMArena Vision | — | 1198 |
| VPCT | — | 30% |

## Multilingual

- DeepSeek-V3.2-Exp: 52.4 (#85)
- Gemini 2.5 Flash-Lite: 49.5 (#129)

| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.5 Flash-Lite |
|---|---|---|
| LMArena Non-English | 1409 | 1369 |
| LMArena Chinese | 1461 | 1404 |
| LMArena French | 1435 | 1388 |
| LMArena German | 1440 | 1390 |
| LMArena Japanese | 1377 | 1359 |
| LMArena Korean | 1372 | 1358 |
| LMArena Russian | 1423 | 1373 |
| LMArena Spanish | 1441 | 1396 |

## Instruction Following

- DeepSeek-V3.2-Exp: 74.7 (#89)
- Gemini 2.5 Flash-Lite: 70.2 (#163)

| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.5 Flash-Lite |
|---|---|---|
| LMArena Instruction Following | 1413 | 1366 |
| IFEval | — | 81% |

## Long Context

- DeepSeek-V3.2-Exp: 47.6 (#15)
- Gemini 2.5 Flash-Lite: 33.3 (#257)

| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.5 Flash-Lite |
|---|---|---|
| Fiction.LiveBench | 83.3% | 47.2% |
| LMArena Longer Query | 1428 | 1373 |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |

## Writing & Preference

- DeepSeek-V3.2-Exp: 62.5 (#73)
- Gemini 2.5 Flash-Lite: 57.0 (#130)

| Benchmark | DeepSeek-V3.2-Exp | Gemini 2.5 Flash-Lite |
|---|---|---|
| LMArena Text | 1425 | 1379 |
| LMArena Creative Writing | 1403 | 1366 |
| LMArena Multi-Turn | 1427 | 1367 |
| EQ-Bench Creative Writing | 1515 | — |
| WildBench | — | 81.8% |

## FAQ

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

DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 36.4 on the Noometry Index. Gemini 2.5 Flash-Lite costs 1.7× less per token, which makes it the better buy when DeepSeek-V3.2-Exp's lead doesn't matter for your workload.

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

Gemini 2.5 Flash-Lite is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; DeepSeek-V3.2-Exp lists at $0.26 and $0.38.

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

DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.4 versus 38.0 in the Noometry coding category.

### Which has the bigger context window?

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

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

25 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 48 scored results on Noometry and Gemini 2.5 Flash-Lite has 33.