# DeepSeek-V3 vs Gemini 2.5 Flash-Lite

> DeepSeek-V3 is the stronger model overall, scoring 39.5 to 37.0 on the Noometry Index. Gemini 2.5 Flash-Lite costs 2.3× less per token, which makes it the better buy when DeepSeek-V3's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v3-vs-gemini-2-5-flash-lite
- Last updated: 2026-10-10
- Shared benchmarks: 29

## Summary

- They share 29 benchmarks with published results for both. DeepSeek-V3 scores higher in 5 categories and Gemini 2.5 Flash-Lite in 3 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in math, where Gemini 2.5 Flash-Lite leads 38.0 to 32.1.
- The biggest single-benchmark swing is GPQA (HELM): 53.8% for DeepSeek-V3 and 30.9% for Gemini 2.5 Flash-Lite.
- Gemini 2.5 Flash-Lite is cheaper at $0.10 / $0.40 per million input/output tokens, against $0.24 / $0.90 for DeepSeek-V3.
- Gemini 2.5 Flash-Lite accepts more context: 1.05M tokens versus 164K.
- DeepSeek-V3 has downloadable open weights; the other is API-only.

## Snapshot

| | DeepSeek-V3 | Gemini 2.5 Flash-Lite |
|---|---|---|
| Provider | DeepSeek | Google |
| Noometry Index | 39.5 | 37.0 |
| Rank | 166 | 211 |
| Context | 164K | 1.05M |
| Input $/M | $0.24 | $0.10 |
| Output $/M | $0.90 | $0.40 |
| Weights | Open | Proprietary |

## Coding

- DeepSeek-V3: 42.3 (#106)
- Gemini 2.5 Flash-Lite: 38.5 (#173)

| Benchmark | DeepSeek-V3 | Gemini 2.5 Flash-Lite |
|---|---|---|
| WeirdML | 36.1% | 35.2% |
| LMArena Coding | 1368 | 1373 |
| Aider Polyglot | 55.1% | — |
| SciCode | 35.8% | — |
| BigCodeBench Instruct | 50% | — |
| LiveBench Coding | 70.9% | — |
| BigCodeBench Complete | 62.2% | — |
| ALE-Bench | — | 325.9 |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73% | — |

## Agentic & Tool Use

- DeepSeek-V3: —
- Gemini 2.5 Flash-Lite: 28.0 (#96)

| Benchmark | DeepSeek-V3 | Gemini 2.5 Flash-Lite |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 36.9% |
| METR Time Horizons | 49.6% | — |

## Reasoning

- DeepSeek-V3: 20.5 (#236)
- Gemini 2.5 Flash-Lite: 22.2 (#205)

| Benchmark | DeepSeek-V3 | Gemini 2.5 Flash-Lite |
|---|---|---|
| Kagi LLM Benchmark | 52.3% | 40.5% |
| LMArena Hard Prompts | 1365 | 1377 |
| DTBench | 64.8% | 62.8% |
| LMCA | 15.5% | 18.1% |
| Epoch Capabilities Index | 135.94 | 133.94 |
| SimpleBench | 27.2% | — |
| CritPt | 0% | — |
| LiveBench Reasoning | 65.8% | — |
| LiveBench Data Analysis | 60.9% | — |
| BIG-Bench Hard | 87.5% | — |
| ForecastBench | 59.1 | — |
| HellaSwag | 88.9% | — |
| LiveBench | 66.9% | — |
| PIQA | 84.7% | — |
| WinoGrande | 85.2% | — |

## Math

- DeepSeek-V3: 32.1 (#219)
- Gemini 2.5 Flash-Lite: 38.0 (#144)

| Benchmark | DeepSeek-V3 | Gemini 2.5 Flash-Lite |
|---|---|---|
| Omni-MATH | 40.3% | 48% |
| LMArena Math | 1373 | 1373 |
| OTIS Mock AIME 2024-2025 | 37.8% | — |
| LiveBench Math | 73.5% | — |
| MATH Level 5 | 75.5% | — |
| FrontierMath (Feb 2025 set) | 1.7% | — |

## Knowledge

- DeepSeek-V3: 37.5 (#155)
- Gemini 2.5 Flash-Lite: 32.5 (#210)

| Benchmark | DeepSeek-V3 | Gemini 2.5 Flash-Lite |
|---|---|---|
| MMLU-Pro | 72.3% | 53.7% |
| Vectara Hallucination Rate | 6.1% | 3.3% |
| GPQA (HELM) | 53.8% | 30.9% |
| LMArena Expert | 1351 | 1373 |
| GPQA Diamond | 67.6% | — |
| Confabulations | 26.1% | — |
| ARC (AI2) Challenge | 95.3% | — |
| MMLU | 87.2% | — |
| TriviaQA | 82.9% | — |

## Multimodal

- DeepSeek-V3: —
- Gemini 2.5 Flash-Lite: 29.1 (#114)

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

## Multilingual

- DeepSeek-V3: 48.5 (#143)
- Gemini 2.5 Flash-Lite: 49.3 (#134)

| Benchmark | DeepSeek-V3 | Gemini 2.5 Flash-Lite |
|---|---|---|
| LMArena Non-English | 1358 | 1369 |
| LMArena Chinese | 1391 | 1404 |
| LMArena French | 1385 | 1388 |
| LMArena German | 1374 | 1389 |
| LMArena Japanese | 1333 | 1359 |
| LMArena Korean | 1319 | 1360 |
| LMArena Russian | 1373 | 1373 |
| LMArena Spanish | 1358 | 1396 |

## Instruction Following

- DeepSeek-V3: 72.8 (#130)
- Gemini 2.5 Flash-Lite: 70.0 (#168)

| Benchmark | DeepSeek-V3 | Gemini 2.5 Flash-Lite |
|---|---|---|
| IFEval | 83.2% | 81% |
| LMArena Instruction Following | 1345 | 1367 |
| LiveBench Instruction Following | 81.5% | — |

## Long Context

- DeepSeek-V3: 34.0 (#253)
- Gemini 2.5 Flash-Lite: 33.3 (#262)

| Benchmark | DeepSeek-V3 | Gemini 2.5 Flash-Lite |
|---|---|---|
| Fiction.LiveBench | 50% | 47.2% |
| LMArena Longer Query | 1352 | 1373 |

## Writing & Preference

- DeepSeek-V3: 57.4 (#130)
- Gemini 2.5 Flash-Lite: 56.8 (#135)

| Benchmark | DeepSeek-V3 | Gemini 2.5 Flash-Lite |
|---|---|---|
| LMArena Text | 1375 | 1379 |
| LMArena Creative Writing | 1364 | 1367 |
| WildBench | 83% | 81.8% |
| LMArena Multi-Turn | 1389 | 1366 |
| Short-Story Creative Writing | 77% | — |
| EQ-Bench Creative Writing | 1472 | — |
| LiveBench Language | 49.1% | — |

## FAQ

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

DeepSeek-V3 is the stronger model overall, scoring 39.5 to 37.0 on the Noometry Index. Gemini 2.5 Flash-Lite costs 2.3× less per token, which makes it the better buy when DeepSeek-V3's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek-V3 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 lists at $0.24 and $0.90.

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

DeepSeek-V3 scores higher on coding benchmarks: 42.3 versus 38.5 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 and Gemini 2.5 Flash-Lite share?

29 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and Gemini 2.5 Flash-Lite has 33.