Model comparison
Gemini 2.5 Flash vs Qwen3.6 27B
Qwen3.6 27B is the stronger model overall, scoring 41.9 to 39.0 on the Noometry Index. Gemini 2.5 Flash costs 1.6× less per token, which makes it the better buy when Qwen3.6 27B's lead doesn't matter for your workload.
Last verified . 5 shared benchmarks.
Google
Alibaba (Qwen)Summary
- They share 5 benchmarks with published results for both. Gemini 2.5 Flash scores higher in 1 category and Qwen3.6 27B in 4 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Qwen3.6 27B leads 52.4 to 36.1.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 73.1% for Gemini 2.5 Flash and 91.1% for Qwen3.6 27B.
- Gemini 2.5 Flash is cheaper at $0.30 / $2.50 per million input/output tokens, against $0.60 / $3.60 for Qwen3.6 27B.
- Gemini 2.5 Flash accepts more context: 1.05M tokens versus 262K.
- Qwen3.6 27B has downloadable open weights; the other is API-only.
Side by side
| Gemini 2.5 Flash | Qwen3.6 27B | |
|---|---|---|
| Provider | Alibaba (Qwen) | |
| Noometry Index | 39.0 | 41.9 |
| Released | 2025-04-17 | 2026-04-22 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 262K |
| Max output | 66K | 66K |
| Input $ / M tokens | $0.30 | $0.60 |
| Output $ / M tokens | $2.50 | $3.60 |
| Results tracked | 54 | 11 |
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Category by category
Coding Qwen3.6 27B leads
Gemini 2.5 Flash: 35.5 (#210), Qwen3.6 27B: 38.5 (#162)
| Benchmark | Gemini 2.5 Flash | Qwen3.6 27B |
|---|---|---|
| SWE-bench Verified (bash only) | 28.7% | — |
| Aider Polyglot | 55.1% | — |
| SciCode | — | 37.3% |
| WeirdML | 41.9% | — |
| LMArena Coding | 1424 | — |
| ALE-Bench | 661.88 | — |
Agentic & Tool Use Not comparable
Gemini 2.5 Flash: 30.6 (#67), Qwen3.6 27B: —
| Benchmark | Gemini 2.5 Flash | Qwen3.6 27B |
|---|---|---|
| Terminal-Bench | 17.1% | — |
| Berkeley Function Calling Leaderboard | 56.2% | — |
| TheAgentCompany | 41.1% | — |
| BALROG | 33.5% | — |
| Vending-Bench 2 | 548.84 | — |
Reasoning Qwen3.6 27B leads
Gemini 2.5 Flash: 18.2 (#271), Qwen3.6 27B: 24.4 (#148)
| Benchmark | Gemini 2.5 Flash | Qwen3.6 27B |
|---|---|---|
| CritPt | 1.1% | 0.9% |
| DTBench | 76.5% | 78.1% |
| LMCA | 27.5% | 34.5% |
| Epoch Capabilities Index | 143.04 | 146.47 |
| ARC-AGI-2 | 2.5% | — |
| SimpleBench | 41.2% | — |
| Kagi LLM Benchmark | 56.8% | — |
| ARC-AGI-1 | 33.3% | — |
| Chess Puzzles | — | 22% |
| EnigmaEval | 2.7% | — |
| LMArena Hard Prompts | 1422 | — |
| Mystery Game Puzzles | — | 7% |
| ForecastBench | 60.6 | — |
Math Qwen3.6 27B leads
Gemini 2.5 Flash: 38.1 (#87), Qwen3.6 27B: 47.5 (#56)
| Benchmark | Gemini 2.5 Flash | Qwen3.6 27B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 73.1% | 91.1% |
| FrontierMath (Tiers 1-3) | — | 35.1% |
| Omni-MATH | 38.5% | — |
| LMArena Math | 1415 | — |
| FrontierMath (Feb 2025 set) | 4.8% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |
Knowledge Qwen3.6 27B leads
Gemini 2.5 Flash: 36.1 (#162), Qwen3.6 27B: 52.4 (#58)
| Benchmark | Gemini 2.5 Flash | Qwen3.6 27B |
|---|---|---|
| GPQA Diamond | — | 85.9% |
| Humanity's Last Exam | 12.1% | — |
| MMLU-Pro | 63.9% | — |
| Confabulations | 16.8% | — |
| Vectara Hallucination Rate | 7.8% | — |
| GPQA (HELM) | 39% | — |
| LMArena Expert | 1426 | — |
Multimodal Not comparable
Gemini 2.5 Flash: 41.2 (#31), Qwen3.6 27B: —
| Benchmark | Gemini 2.5 Flash | Qwen3.6 27B |
|---|---|---|
| LMArena Vision | 1254 | — |
| GeoBench | 76% | — |
| VPCT | 46.2% | — |
| SpatialViz-Bench | 36.9% | — |
Multilingual Not comparable
Gemini 2.5 Flash: 52.5 (#83), Qwen3.6 27B: —
| Benchmark | Gemini 2.5 Flash | Qwen3.6 27B |
|---|---|---|
| LMArena Non-English | 1410 | — |
| LMArena Chinese | 1450 | — |
| LMArena French | 1433 | — |
| LMArena German | 1419 | — |
| LMArena Japanese | 1403 | — |
| LMArena Korean | 1385 | — |
| LMArena Russian | 1415 | — |
| LMArena Spanish | 1421 | — |
Instruction Following Not comparable
Gemini 2.5 Flash: 75.9 (#50), Qwen3.6 27B: —
| Benchmark | Gemini 2.5 Flash | Qwen3.6 27B |
|---|---|---|
| IFEval | 89.8% | — |
| LMArena Instruction Following | 1405 | — |
Long Context Not comparable
Gemini 2.5 Flash: 47.5 (#16), Qwen3.6 27B: —
| Benchmark | Gemini 2.5 Flash | Qwen3.6 27B |
|---|---|---|
| Fiction.LiveBench | 77.8% | — |
| LMArena Longer Query | 1419 | — |
Writing & Preference Gemini 2.5 Flash leads
Gemini 2.5 Flash: 54.0 (#153), Qwen3.6 27B: 50.3 (#180)
| Benchmark | Gemini 2.5 Flash | Qwen3.6 27B |
|---|---|---|
| LMArena Text | 1417 | — |
| LMArena Creative Writing | 1400 | — |
| Short-Story Creative Writing | 76.5% | — |
| EQ-Bench Creative Writing | 1137 | — |
| WildBench | 81.7% | — |
| EQ-Bench 4 | — | 1026 |
| LMArena Multi-Turn | 1408 | — |
Frequently asked questions
Is Gemini 2.5 Flash better than Qwen3.6 27B?
Qwen3.6 27B is the stronger model overall, scoring 41.9 to 39.0 on the Noometry Index. Gemini 2.5 Flash costs 1.6× less per token, which makes it the better buy when Qwen3.6 27B's lead doesn't matter for your workload.
Which is cheaper, Gemini 2.5 Flash or Qwen3.6 27B?
Gemini 2.5 Flash is cheaper. It lists at $0.30 per million input tokens and $2.50 per million output tokens; Qwen3.6 27B lists at $0.60 and $3.60.
Is Gemini 2.5 Flash or Qwen3.6 27B better for coding?
Qwen3.6 27B scores higher on coding benchmarks: 38.5 versus 35.5 in the Noometry coding category.
Which has the bigger context window?
Gemini 2.5 Flash does, with 1.05M tokens against 262K.
How many benchmarks do Gemini 2.5 Flash and Qwen3.6 27B share?
5 benchmarks have published results for both models. Gemini 2.5 Flash has 54 scored results on Noometry and Qwen3.6 27B has 11.