# DeepSeek-V3 vs Gemini 2.5 Pro

> Gemini 2.5 Pro is the stronger model overall, scoring 45.0 to 39.5 on the Noometry Index. DeepSeek-V3 costs 8.5× less per token, which makes it the better buy when Gemini 2.5 Pro's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v3-vs-gemini-2-5-pro
- Last updated: 2026-10-11
- Shared benchmarks: 49

## Summary

- They share 49 benchmarks with published results for both. DeepSeek-V3 scores higher in 0 categories and Gemini 2.5 Pro in 8 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in long context, where Gemini 2.5 Pro leads 59.8 to 34.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 37.8% for DeepSeek-V3 and 84.7% for Gemini 2.5 Pro.
- DeepSeek-V3 is cheaper at $0.24 / $0.90 per million input/output tokens, against $1.25 / $10 for Gemini 2.5 Pro.
- Gemini 2.5 Pro 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 Pro |
|---|---|---|
| Provider | DeepSeek | Google |
| Noometry Index | 39.5 | 45.0 |
| Rank | 166 | 75 |
| Context | 164K | 1.05M |
| Input $/M | $0.24 | $1.25 |
| Output $/M | $0.90 | $10 |
| Weights | Open | Proprietary |

## Coding

- DeepSeek-V3: 42.3 (#106)
- Gemini 2.5 Pro: 42.4 (#101)

| Benchmark | DeepSeek-V3 | Gemini 2.5 Pro |
|---|---|---|
| Aider Polyglot | 55.1% | 83.1% |
| SciCode | 35.8% | 42.8% |
| WeirdML | 36.1% | 54% |
| LiveBench Coding | 70.9% | 85.9% |
| LMArena Coding | 1368 | 1452 |
| SWE-bench Verified | — | 57.6% |
| SWE-bench Verified (bash only) | — | 53.6% |
| LMArena WebDev | — | 1227 |
| GSO | — | 3.9% |
| BigCodeBench Instruct | 50% | — |
| BigCodeBench Complete | 62.2% | — |
| CadEval | — | 64% |
| ALE-Bench | — | 785.52 |
| AlgoTune | — | 1.51 |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73% | — |

## Agentic & Tool Use

- DeepSeek-V3: —
- Gemini 2.5 Pro: 29.2 (#88)

| Benchmark | DeepSeek-V3 | Gemini 2.5 Pro |
|---|---|---|
| METR Time Horizons | 49.6% | 55.4% |
| Terminal-Bench | — | 32.6% |
| GDPval | — | 23.3% |
| Remote Labor Index | — | 0.8% |
| TheAgentCompany | — | 30.3% |
| τ²-bench Banking | — | 13.7% |
| DeepResearch Bench | — | 42.8% |
| BALROG | — | 43.3% |
| LMArena Search | — | 1142 |
| Vending-Bench 2 | — | 573.64 |

## Reasoning

- DeepSeek-V3: 20.5 (#236)
- Gemini 2.5 Pro: 28.8 (#99)

| Benchmark | DeepSeek-V3 | Gemini 2.5 Pro |
|---|---|---|
| SimpleBench | 27.2% | 62.4% |
| Kagi LLM Benchmark | 52.3% | 70.3% |
| CritPt | 0% | 2% |
| LiveBench Reasoning | 65.8% | 89.8% |
| LMArena Hard Prompts | 1365 | 1455 |
| DTBench | 64.8% | 82.4% |
| LiveBench Data Analysis | 60.9% | 79.9% |
| LMCA | 15.5% | 34.8% |
| Epoch Capabilities Index | 135.94 | 145.32 |
| ForecastBench | 59.1 | 61.3 |
| LiveBench | 66.9% | 82.3% |
| ARC-AGI-2 | — | 4.9% |
| ARC-AGI-1 | — | 41% |
| Chess Puzzles | — | 20% |
| EnigmaEval | — | 5.6% |
| BIG-Bench Hard | 87.5% | — |
| HellaSwag | 88.9% | — |
| PIQA | 84.7% | — |
| WinoGrande | 85.2% | — |

## Math

- DeepSeek-V3: 32.1 (#219)
- Gemini 2.5 Pro: 32.5 (#213)

| Benchmark | DeepSeek-V3 | Gemini 2.5 Pro |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 37.8% | 84.7% |
| Omni-MATH | 40.3% | 41.6% |
| LiveBench Math | 73.5% | 90.2% |
| LMArena Math | 1373 | 1450 |
| MATH Level 5 | 75.5% | 95.9% |
| FrontierMath (Feb 2025 set) | 1.7% | 14.1% |
| FrontierMath (Tiers 1-3) | — | 24.6% |
| FrontierMath Tier 4 | — | 0% |
| FrontierMath Tier 4 (v1) | — | 4.2% |

## Knowledge

- DeepSeek-V3: 37.5 (#155)
- Gemini 2.5 Pro: 56.0 (#46)

| Benchmark | DeepSeek-V3 | Gemini 2.5 Pro |
|---|---|---|
| GPQA Diamond | 67.6% | 85.3% |
| MMLU-Pro | 72.3% | 86.3% |
| Confabulations | 26.1% | 10.6% |
| Vectara Hallucination Rate | 6.1% | 7% |
| GPQA (HELM) | 53.8% | 74.9% |
| LMArena Expert | 1351 | 1452 |
| Humanity's Last Exam | — | 21.6% |
| ARC (AI2) Challenge | 95.3% | — |
| MMLU | 87.2% | — |
| TriviaQA | 82.9% | — |

## Multimodal

- DeepSeek-V3: —
- Gemini 2.5 Pro: 45.2 (#18)

| Benchmark | DeepSeek-V3 | Gemini 2.5 Pro |
|---|---|---|
| LMArena Vision | — | 1263 |
| GeoBench | — | 86% |
| VPCT | — | 48% |
| LMArena Document | — | 1421 |
| SpatialViz-Bench | — | 44.7% |

## Multilingual

- DeepSeek-V3: 48.5 (#143)
- Gemini 2.5 Pro: 55.3 (#31)

| Benchmark | DeepSeek-V3 | Gemini 2.5 Pro |
|---|---|---|
| LMArena Non-English | 1358 | 1451 |
| LMArena Chinese | 1391 | 1507 |
| LMArena French | 1385 | 1472 |
| LMArena German | 1374 | 1487 |
| LMArena Japanese | 1333 | 1461 |
| LMArena Korean | 1319 | 1434 |
| LMArena Russian | 1373 | 1461 |
| LMArena Spanish | 1358 | 1473 |

## Instruction Following

- DeepSeek-V3: 72.8 (#130)
- Gemini 2.5 Pro: 75.0 (#75)

| Benchmark | DeepSeek-V3 | Gemini 2.5 Pro |
|---|---|---|
| LiveBench Instruction Following | 81.5% | 80.6% |
| IFEval | 83.2% | 84% |
| LMArena Instruction Following | 1345 | 1437 |

## Long Context

- DeepSeek-V3: 34.0 (#253)
- Gemini 2.5 Pro: 59.8 (#5)

| Benchmark | DeepSeek-V3 | Gemini 2.5 Pro |
|---|---|---|
| Fiction.LiveBench | 50% | 91.7% |
| LMArena Longer Query | 1352 | 1449 |

## Writing & Preference

- DeepSeek-V3: 57.4 (#130)
- Gemini 2.5 Pro: 63.7 (#62)

| Benchmark | DeepSeek-V3 | Gemini 2.5 Pro |
|---|---|---|
| LMArena Text | 1375 | 1458 |
| LMArena Creative Writing | 1364 | 1454 |
| Short-Story Creative Writing | 77% | 83.8% |
| EQ-Bench Creative Writing | 1472 | 1421 |
| WildBench | 83% | 85.7% |
| LMArena Multi-Turn | 1389 | 1453 |
| LiveBench Language | 49.1% | 67.8% |

## FAQ

### Is DeepSeek-V3 better than Gemini 2.5 Pro?

Gemini 2.5 Pro is the stronger model overall, scoring 45.0 to 39.5 on the Noometry Index. DeepSeek-V3 costs 8.5× less per token, which makes it the better buy when Gemini 2.5 Pro's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek-V3 or Gemini 2.5 Pro?

DeepSeek-V3 is cheaper. It lists at $0.24 per million input tokens and $0.90 per million output tokens; Gemini 2.5 Pro lists at $1.25 and $10.

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

They score almost the same on coding (42.3 vs 42.4); test both on your own repository before choosing.

### Which has the bigger context window?

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

### How many benchmarks do DeepSeek-V3 and Gemini 2.5 Pro share?

49 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and Gemini 2.5 Pro has 78.