Model comparison
Gemini 3.5 Flash vs Qwen2.5 7B Instruct
Gemini 3.5 Flash is the stronger model overall, scoring 54.2 to 29.0 on the Noometry Index. Qwen2.5 7B Instruct costs 11× less per token, which makes it the better buy when Gemini 3.5 Flash's lead doesn't matter for your workload.
Last verified . 6 shared benchmarks.
Google
Alibaba (Qwen)Summary
- They share 6 benchmarks with published results for both. Gemini 3.5 Flash scores higher in 7 categories and Qwen2.5 7B Instruct in 0 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Gemini 3.5 Flash leads 66.3 to 17.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 95.6% for Gemini 3.5 Flash and 2.5% for Qwen2.5 7B Instruct.
- Qwen2.5 7B Instruct is cheaper at $0.17 / $0.70 per million input/output tokens, against $1.50 / $9 for Gemini 3.5 Flash.
- Gemini 3.5 Flash accepts more context: 1.05M tokens versus 131K.
- Qwen2.5 7B Instruct has downloadable open weights; the other is API-only.
Side by side
| Gemini 3.5 Flash | Qwen2.5 7B Instruct | |
|---|---|---|
| Provider | Alibaba (Qwen) | |
| Noometry Index | 54.2 | 29.0 |
| Released | 2026-05-19 | 2024-09 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 131K |
| Max output | 66K | 8K |
| Input $ / M tokens | $1.50 | $0.17 |
| Output $ / M tokens | $9 | $0.70 |
| Results tracked | 54 | 15 |
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Category by category
Coding Gemini 3.5 Flash leads
Gemini 3.5 Flash: 49.4 (#49), Qwen2.5 7B Instruct: 36.5 (#208)
| Benchmark | Gemini 3.5 Flash | Qwen2.5 7B Instruct |
|---|---|---|
| SWE-bench Verified | 79.3% | — |
| DeepSWE | 37.4% | — |
| LMArena WebDev | 1499 | — |
| SciCode | 53.1% | — |
| WeirdML | 62.6% | — |
| BigCodeBench Instruct | — | 37.6% |
| LMArena Coding | 1492 | — |
| BigCodeBench Complete | — | 46.1% |
| ALE-Bench | 911.02 | — |
Agentic & Tool Use Too close to call
Gemini 3.5 Flash: 24.7 (#114), Qwen2.5 7B Instruct: 23.8 (#124)
| Benchmark | Gemini 3.5 Flash | Qwen2.5 7B Instruct |
|---|---|---|
| APEX-Agents | 27.5% | — |
| BALROG | — | 7.8% |
| GBAEval | 6.7% | — |
| GDP.pdf | 14% | — |
| Vending-Bench 2 | 5,396 | — |
Reasoning Gemini 3.5 Flash leads
Gemini 3.5 Flash: 62.8 (#18), Qwen2.5 7B Instruct: 14.8 (#322)
| Benchmark | Gemini 3.5 Flash | Qwen2.5 7B Instruct |
|---|---|---|
| Chess Puzzles | 50% | 0% |
| DTBench | 94.7% | 47.7% |
| LMCA | 47.1% | 6.4% |
| Epoch Capabilities Index | 154.46 | 118.51 |
| ARC-AGI-2 | 72.1% | — |
| SimpleBench | 76.7% | — |
| NYT Connections (extended) | 92.6% | — |
| ARC-AGI-1 | 92.5% | — |
| CritPt | 13.1% | — |
| EnigmaEval | 25.4% | — |
| EBR-Bench | 4.8% | — |
| LMArena Hard Prompts | 1488 | — |
| Mystery Game Puzzles | 32% | — |
| Surface Evolver Bench | 58.1% | — |
| ForecastBench | 59 | — |
Math Gemini 3.5 Flash leads
Gemini 3.5 Flash: 60.7 (#36), Qwen2.5 7B Instruct: 12.6 (#306)
| Benchmark | Gemini 3.5 Flash | Qwen2.5 7B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 95.6% | 2.5% |
| FrontierMath (Tiers 1-3) | 62.8% | — |
| FrontierMath Tier 4 | 26.8% | — |
| MathArena Final-Answer Competitions | 76.3% | — |
| ProofBench | 31% | — |
| Omni-MATH | — | 29.4% |
| LMArena Math | 1504 | — |
| FrontierMath (Feb 2025 set) | 39% | — |
| FrontierMath Tier 4 (v1) | 14.6% | — |
Knowledge Gemini 3.5 Flash leads
Gemini 3.5 Flash: 66.3 (#11), Qwen2.5 7B Instruct: 17.0 (#286)
| Benchmark | Gemini 3.5 Flash | Qwen2.5 7B Instruct |
|---|---|---|
| GPQA Diamond | 92.8% | 35.5% |
| SimpleQA Verified | 66.2% | — |
| MMLU-Pro | — | 53.9% |
| GPQA (HELM) | — | 34.1% |
| LMArena Expert | 1495 | — |
| MMLU | — | 72.9% |
Multimodal Not comparable
Gemini 3.5 Flash: 45.7 (#15), Qwen2.5 7B Instruct: —
| Benchmark | Gemini 3.5 Flash | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Vision | 1310 | — |
| Blueprint-Bench 2 | 33.6% | — |
| LMArena Document | 1463 | — |
Multilingual Not comparable
Gemini 3.5 Flash: 57.0 (#13), Qwen2.5 7B Instruct: —
| Benchmark | Gemini 3.5 Flash | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Non-English | 1476 | — |
| LMArena Chinese | 1526 | — |
| LMArena French | 1490 | — |
| LMArena German | 1492 | — |
| LMArena Japanese | 1486 | — |
| LMArena Korean | 1451 | — |
| LMArena Russian | 1493 | — |
| LMArena Spanish | 1480 | — |
Instruction Following Gemini 3.5 Flash leads
Gemini 3.5 Flash: 77.0 (#30), Qwen2.5 7B Instruct: 63.2 (#231)
| Benchmark | Gemini 3.5 Flash | Qwen2.5 7B Instruct |
|---|---|---|
| IFEval | — | 74.1% |
| LMArena Instruction Following | 1467 | — |
Long Context Not comparable
Gemini 3.5 Flash: 45.4 (#38), Qwen2.5 7B Instruct: —
| Benchmark | Gemini 3.5 Flash | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Longer Query | 1482 | — |
Writing & Preference Gemini 3.5 Flash leads
Gemini 3.5 Flash: 65.5 (#47), Qwen2.5 7B Instruct: 48.8 (#195)
| Benchmark | Gemini 3.5 Flash | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Text | 1482 | — |
| LMArena Creative Writing | 1470 | — |
| WildBench | — | 73.1% |
| EQ-Bench 4 | 1087 | — |
| LMArena Multi-Turn | 1481 | — |
Frequently asked questions
Is Gemini 3.5 Flash better than Qwen2.5 7B Instruct?
Gemini 3.5 Flash is the stronger model overall, scoring 54.2 to 29.0 on the Noometry Index. Qwen2.5 7B Instruct costs 11× less per token, which makes it the better buy when Gemini 3.5 Flash's lead doesn't matter for your workload.
Which is cheaper, Gemini 3.5 Flash or Qwen2.5 7B Instruct?
Qwen2.5 7B Instruct is cheaper. It lists at $0.17 per million input tokens and $0.70 per million output tokens; Gemini 3.5 Flash lists at $1.50 and $9.
Is Gemini 3.5 Flash or Qwen2.5 7B Instruct better for coding?
Gemini 3.5 Flash scores higher on coding benchmarks: 49.4 versus 36.5 in the Noometry coding category.
Which has the bigger context window?
Gemini 3.5 Flash does, with 1.05M tokens against 131K.
How many benchmarks do Gemini 3.5 Flash and Qwen2.5 7B Instruct share?
6 benchmarks have published results for both models. Gemini 3.5 Flash has 54 scored results on Noometry and Qwen2.5 7B Instruct has 15.