Model comparison
Gemini 2.5 Pro vs Qwen3.5-9B
Gemini 2.5 Pro is the stronger model overall, scoring 45.0 to 33.8 on the Noometry Index. Qwen3.5-9B costs 31× less per token, which makes it the better buy when Gemini 2.5 Pro's lead doesn't matter for your workload.
Last verified . 9 shared benchmarks.
Google
Alibaba (Qwen)Summary
- They share 9 benchmarks with published results for both. Gemini 2.5 Pro scores higher in 4 categories and Qwen3.5-9B in 1 category; 5 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where Gemini 2.5 Pro leads 29.2 to 14.5.
- The biggest single-benchmark swing is Terminal-Bench: 32.6% for Gemini 2.5 Pro and 9.2% for Qwen3.5-9B.
- Qwen3.5-9B is cheaper at $0.10 / $0.15 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 262K.
- Qwen3.5-9B has downloadable open weights; the other is API-only.
Side by side
| Gemini 2.5 Pro | Qwen3.5-9B | |
|---|---|---|
| Provider | Alibaba (Qwen) | |
| Noometry Index | 45.0 | 33.8 |
| Released | 2025-03-25 | 2026-02-23 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 262K |
| Max output | 66K | 66K |
| Input $ / M tokens | $1.25 | $0.10 |
| Output $ / M tokens | $10 | $0.15 |
| Results tracked | 78 | 10 |
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Category by category
Coding Gemini 2.5 Pro leads
Gemini 2.5 Pro: 42.4 (#101), Qwen3.5-9B: 35.9 (#217)
| Benchmark | Gemini 2.5 Pro | Qwen3.5-9B |
|---|---|---|
| SciCode | 42.8% | 27.5% |
| 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 Gemini 2.5 Pro leads
Gemini 2.5 Pro: 29.2 (#88), Qwen3.5-9B: 14.5 (#151)
| Benchmark | Gemini 2.5 Pro | Qwen3.5-9B |
|---|---|---|
| Terminal-Bench | 32.6% | 9.2% |
| 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 (#99), Qwen3.5-9B: 23.1 (#182)
| Benchmark | Gemini 2.5 Pro | Qwen3.5-9B |
|---|---|---|
| CritPt | 2% | 0.3% |
| Chess Puzzles | 20% | 12% |
| DTBench | 82.4% | 71.2% |
| LMCA | 34.8% | 24.5% |
| Epoch Capabilities Index | 145.32 | 139.46 |
| ARC-AGI-2 | 4.9% | — |
| SimpleBench | 62.4% | — |
| Kagi LLM Benchmark | 70.3% | — |
| 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 Qwen3.5-9B leads
Gemini 2.5 Pro: 32.5 (#213), Qwen3.5-9B: 34.8 (#192)
| Benchmark | Gemini 2.5 Pro | Qwen3.5-9B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 84.7% | 61.7% |
| FrontierMath (Tiers 1-3) | 24.6% | — |
| FrontierMath Tier 4 | 0% | — |
| MathArena Final-Answer Competitions | — | 48.5% |
| 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: 56.0 (#46), Qwen3.5-9B: 46.0 (#84)
| Benchmark | Gemini 2.5 Pro | Qwen3.5-9B |
|---|---|---|
| GPQA Diamond | 85.3% | 79% |
| Humanity's Last Exam | 21.6% | — |
| MMLU-Pro | 86.3% | — |
| Confabulations | 10.6% | — |
| Vectara Hallucination Rate | 7% | — |
| GPQA (HELM) | 74.9% | — |
| LMArena Expert | 1452 | — |
Multimodal Not comparable
Gemini 2.5 Pro: 45.2 (#18), Qwen3.5-9B: —
| Benchmark | Gemini 2.5 Pro | Qwen3.5-9B |
|---|---|---|
| LMArena Vision | 1263 | — |
| GeoBench | 86% | — |
| VPCT | 48% | — |
| LMArena Document | 1421 | — |
| SpatialViz-Bench | 44.7% | — |
Multilingual Not comparable
Gemini 2.5 Pro: 55.3 (#31), Qwen3.5-9B: —
| Benchmark | Gemini 2.5 Pro | Qwen3.5-9B |
|---|---|---|
| LMArena Non-English | 1451 | — |
| LMArena Chinese | 1507 | — |
| LMArena French | 1472 | — |
| LMArena German | 1487 | — |
| LMArena Japanese | 1461 | — |
| LMArena Korean | 1434 | — |
| LMArena Russian | 1461 | — |
| LMArena Spanish | 1473 | — |
Instruction Following Not comparable
Gemini 2.5 Pro: 75.0 (#75), Qwen3.5-9B: —
| Benchmark | Gemini 2.5 Pro | Qwen3.5-9B |
|---|---|---|
| LiveBench Instruction Following | 80.6% | — |
| IFEval | 84% | — |
| LMArena Instruction Following | 1437 | — |
Long Context Not comparable
Gemini 2.5 Pro: 59.8 (#5), Qwen3.5-9B: —
| Benchmark | Gemini 2.5 Pro | Qwen3.5-9B |
|---|---|---|
| Fiction.LiveBench | 91.7% | — |
| LMArena Longer Query | 1449 | — |
Writing & Preference Not comparable
Gemini 2.5 Pro: 63.7 (#62), Qwen3.5-9B: —
| Benchmark | Gemini 2.5 Pro | Qwen3.5-9B |
|---|---|---|
| LMArena Text | 1458 | — |
| LMArena Creative Writing | 1454 | — |
| 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 Qwen3.5-9B?
Gemini 2.5 Pro is the stronger model overall, scoring 45.0 to 33.8 on the Noometry Index. Qwen3.5-9B costs 31× 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 Qwen3.5-9B?
Qwen3.5-9B is cheaper. It lists at $0.10 per million input tokens and $0.15 per million output tokens; Gemini 2.5 Pro lists at $1.25 and $10.
Is Gemini 2.5 Pro or Qwen3.5-9B better for coding?
Gemini 2.5 Pro scores higher on coding benchmarks: 42.4 versus 35.9 in the Noometry coding category.
Which has the bigger context window?
Gemini 2.5 Pro does, with 1.05M tokens against 262K.
How many benchmarks do Gemini 2.5 Pro and Qwen3.5-9B share?
9 benchmarks have published results for both models. Gemini 2.5 Pro has 78 scored results on Noometry and Qwen3.5-9B has 10.