Model comparison
Gemini 2.5 Pro vs Qwen3 14B
Gemini 2.5 Pro is the stronger model overall, scoring 44.9 to 35.0 on the Noometry Index. Qwen3 14B costs 5.6× less per token, which makes it the better buy when Gemini 2.5 Pro's lead doesn't matter for your workload.
Last verified . 11 shared benchmarks.
Google
Alibaba (Qwen)Summary
- They share 11 benchmarks with published results for both. Gemini 2.5 Pro scores higher in 4 categories and Qwen3 14B in 2 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in long context, where Gemini 2.5 Pro leads 59.7 to 38.0.
- The biggest single-benchmark swing is Fiction.LiveBench: 91.7% for Gemini 2.5 Pro and 62.5% for Qwen3 14B.
- Qwen3 14B is cheaper at $0.35 / $1.40 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 131K.
- Qwen3 14B has downloadable open weights; the other is API-only.
Side by side
| Gemini 2.5 Pro | Qwen3 14B | |
|---|---|---|
| Provider | Alibaba (Qwen) | |
| Noometry Index | 44.9 | 35.0 |
| Released | 2025-03-25 | 2025-04 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 131K |
| Max output | 66K | 8K |
| Input $ / M tokens | $1.25 | $0.35 |
| Output $ / M tokens | $10 | $1.40 |
| Results tracked | 78 | 12 |
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Category by category
Coding Gemini 2.5 Pro leads
Gemini 2.5 Pro: 42.8 (#86), Qwen3 14B: 36.7 (#191)
| Benchmark | Gemini 2.5 Pro | Qwen3 14B |
|---|---|---|
| SciCode | 42.8% | 31.6% |
| SWE-bench Verified | 57.6% | — |
| SWE-bench Verified (bash only) | 53.6% | — |
| Aider Polyglot | 83.1% | — |
| LMArena WebDev | 1227 | — |
| GSO | 3.9% | — |
| WeirdML | 54% | — |
| LiveBench Coding | 85.9% | — |
| LMArena Coding | 1452 | — |
| CadEval | 64% | — |
| ALE-Bench | 785.52 | — |
| AlgoTune | 1.51 | — |
Agentic & Tool Use Too close to call
Gemini 2.5 Pro: 29.1 (#81), Qwen3 14B: 29.4 (#78)
| Benchmark | Gemini 2.5 Pro | Qwen3 14B |
|---|---|---|
| Terminal-Bench | 32.6% | — |
| Berkeley Function Calling Leaderboard | — | 41% |
| 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 | — |
| METR Time Horizons | 55.4% | — |
| Vending-Bench 2 | 573.64 | — |
Reasoning Gemini 2.5 Pro leads
Gemini 2.5 Pro: 28.8 (#85), Qwen3 14B: 18.1 (#272)
| Benchmark | Gemini 2.5 Pro | Qwen3 14B |
|---|---|---|
| Kagi LLM Benchmark | 70.3% | 49.1% |
| CritPt | 2% | 0% |
| Chess Puzzles | 20% | 4% |
| DTBench | 82.4% | 64% |
| LMCA | 34.8% | 18.2% |
| Epoch Capabilities Index | 145.32 | 138.23 |
| ARC-AGI-2 | 4.9% | — |
| SimpleBench | 62.4% | — |
| ARC-AGI-1 | 41% | — |
| EnigmaEval | 5.6% | — |
| LiveBench Reasoning | 89.8% | — |
| LMArena Hard Prompts | 1455 | — |
| LiveBench Data Analysis | 79.9% | — |
| ForecastBench | 61.3 | — |
| LiveBench | 82.3% | — |
Math Qwen3 14B leads
Gemini 2.5 Pro: 31.9 (#191), Qwen3 14B: 37.0 (#102)
| Benchmark | Gemini 2.5 Pro | Qwen3 14B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 84.7% | 66.4% |
| FrontierMath (Tiers 1-3) | 24.6% | — |
| FrontierMath Tier 4 | 0% | — |
| Omni-MATH | 41.6% | — |
| LiveBench Math | 90.2% | — |
| LMArena Math | 1450 | — |
| MATH Level 5 | 95.9% | — |
| FrontierMath (Feb 2025 set) | 14.1% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |
Knowledge Gemini 2.5 Pro leads
Gemini 2.5 Pro: 55.8 (#41), Qwen3 14B: 39.2 (#124)
| Benchmark | Gemini 2.5 Pro | Qwen3 14B |
|---|---|---|
| GPQA Diamond | 85.3% | 63.8% |
| Vectara Hallucination Rate | 7% | 5.4% |
| Humanity's Last Exam | 21.6% | — |
| MMLU-Pro | 86.3% | — |
| Confabulations | 10.6% | — |
| GPQA (HELM) | 74.9% | — |
| LMArena Expert | 1452 | — |
Multimodal Not comparable
Gemini 2.5 Pro: 44.5 (#16), Qwen3 14B: —
| Benchmark | Gemini 2.5 Pro | Qwen3 14B |
|---|---|---|
| LMArena Vision | 1263 | — |
| GeoBench | 86% | — |
| VPCT | 48% | — |
| LMArena Document | 1421 | — |
| SpatialViz-Bench | 44.7% | — |
Multilingual Not comparable
Gemini 2.5 Pro: 55.5 (#30), Qwen3 14B: —
| Benchmark | Gemini 2.5 Pro | Qwen3 14B |
|---|---|---|
| LMArena Non-English | 1452 | — |
| LMArena Chinese | 1507 | — |
| LMArena French | 1473 | — |
| LMArena German | 1487 | — |
| LMArena Japanese | 1462 | — |
| LMArena Korean | 1435 | — |
| LMArena Russian | 1460 | — |
| LMArena Spanish | 1473 | — |
Instruction Following Not comparable
Gemini 2.5 Pro: 75.2 (#71), Qwen3 14B: —
| Benchmark | Gemini 2.5 Pro | Qwen3 14B |
|---|---|---|
| LiveBench Instruction Following | 80.6% | — |
| IFEval | 84% | — |
| LMArena Instruction Following | 1436 | — |
Long Context Gemini 2.5 Pro leads
Gemini 2.5 Pro: 59.7 (#5), Qwen3 14B: 38.0 (#201)
| Benchmark | Gemini 2.5 Pro | Qwen3 14B |
|---|---|---|
| Fiction.LiveBench | 91.7% | 62.5% |
| LMArena Longer Query | 1449 | — |
Writing & Preference Not comparable
Gemini 2.5 Pro: 63.9 (#60), Qwen3 14B: —
| Benchmark | Gemini 2.5 Pro | Qwen3 14B |
|---|---|---|
| LMArena Text | 1458 | — |
| LMArena Creative Writing | 1453 | — |
| Short-Story Creative Writing | 83.8% | — |
| EQ-Bench Creative Writing | 1421 | — |
| WildBench | 85.7% | — |
| LMArena Multi-Turn | 1453 | — |
| LiveBench Language | 67.8% | — |
Frequently asked questions
Is Gemini 2.5 Pro better than Qwen3 14B?
Gemini 2.5 Pro is the stronger model overall, scoring 44.9 to 35.0 on the Noometry Index. Qwen3 14B costs 5.6× 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, Gemini 2.5 Pro or Qwen3 14B?
Qwen3 14B is cheaper. It lists at $0.35 per million input tokens and $1.40 per million output tokens; Gemini 2.5 Pro lists at $1.25 and $10.
Is Gemini 2.5 Pro or Qwen3 14B better for coding?
Gemini 2.5 Pro scores higher on coding benchmarks: 42.8 versus 36.7 in the Noometry coding category.
Which has the bigger context window?
Gemini 2.5 Pro does, with 1.05M tokens against 131K.
How many benchmarks do Gemini 2.5 Pro and Qwen3 14B share?
11 benchmarks have published results for both models. Gemini 2.5 Pro has 78 scored results on Noometry and Qwen3 14B has 12.