Model comparison
Gemini 3.8 Flash vs Qwen2.5-Coder-32B
Gemini 3.8 Flash is the stronger model overall, scoring 61.8 to 33.4 on the Noometry Index. Qwen2.5-Coder-32B costs 2.0× less per token, which makes it the better buy when Gemini 3.8 Flash's lead doesn't matter for your workload.
Last verified . 13 shared benchmarks.
Google
Alibaba (Qwen)Summary
- They share 13 benchmarks with published results for both. Gemini 3.8 Flash scores higher in 8 categories and Qwen2.5-Coder-32B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Gemini 3.8 Flash leads 76.9 to 21.2.
- Qwen2.5-Coder-32B is cheaper at $0.66 / $1 per million input/output tokens, against $0.75 / $3.75 for Gemini 3.8 Flash.
- Gemini 3.8 Flash accepts more context: 1.05M tokens versus 33K.
- Qwen2.5-Coder-32B has downloadable open weights; the other is API-only.
Side by side
| Gemini 3.8 Flash | Qwen2.5-Coder-32B | |
|---|---|---|
| Provider | Alibaba (Qwen) | |
| Noometry Index | 61.8 | 33.4 |
| Released | 2026-09-02 | 2024-09-18 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 33K |
| Max output | 66K | 29K |
| Input $ / M tokens | $0.75 | $0.66 |
| Output $ / M tokens | $3.75 | $1 |
| Results tracked | 50 | 31 |
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Category by category
Coding Gemini 3.8 Flash leads
Gemini 3.8 Flash: 59.2 (#15), Qwen2.5-Coder-32B: 22.6 (#333)
| Benchmark | Gemini 3.8 Flash | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Coding | 1510 | 1276 |
| DeepSWE | 73.8% | — |
| FrontierCode | 41.2% | — |
| SWE-bench Verified (bash only) | — | 9% |
| Aider Polyglot | — | 16.4% |
| CursorBench | 39.6% | — |
| LMArena WebDev | 1584 | — |
| FrontierSWE | 19.6% | — |
| SciCode | 56.6% | — |
| WeirdML | 84.8% | — |
| BigCodeBench Instruct | — | 49% |
| LiveBench Coding | — | 56.9% |
| BigCodeBench Complete | — | 58% |
| ALE-Bench | 1,270 | — |
| HumanEval+ | — | 87.2% |
| MBPP+ | — | 77% |
Agentic & Tool Use Not comparable
Gemini 3.8 Flash: 41.8 (#21), Qwen2.5-Coder-32B: —
| Benchmark | Gemini 3.8 Flash | Qwen2.5-Coder-32B |
|---|---|---|
| APEX-Agents | 64.3% | — |
| Remote Labor Index | 5.8% | — |
| GDP.pdf | 23.4% | — |
| Vending-Bench 2 | 5,094 | — |
Reasoning Gemini 3.8 Flash leads
Gemini 3.8 Flash: 76.9 (#5), Qwen2.5-Coder-32B: 21.2 (#225)
| Benchmark | Gemini 3.8 Flash | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Hard Prompts | 1508 | 1251 |
| Epoch Capabilities Index | 156.71 | 119.49 |
| ARC-AGI-2 | 89.2% | — |
| NYT Connections (extended) | 97.4% | — |
| ARC-AGI-1 | 98.5% | — |
| CritPt | 18.3% | — |
| Chess Puzzles | 61% | — |
| LiveBench Reasoning | — | 42.1% |
| Mystery Game Puzzles | 47% | — |
| DTBench | 95.7% | — |
| LiveBench Data Analysis | — | 49.9% |
| LMCA | 52.9% | — |
| Surface Evolver Bench | 76.9% | — |
| HellaSwag | — | 83% |
| LiveBench | — | 46.2% |
| WinoGrande | — | 80.8% |
Math Gemini 3.8 Flash leads
Gemini 3.8 Flash: 65.3 (#28), Qwen2.5-Coder-32B: 33.3 (#204)
| Benchmark | Gemini 3.8 Flash | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Math | 1528 | 1251 |
| FrontierMath (Tiers 1-3) | 68.4% | — |
| FrontierMath Tier 4 | 22% | — |
| OTIS Mock AIME 2024-2025 | 98.9% | — |
| ProofBench | 48% | — |
| LiveBench Math | — | 46.6% |
| GSM8K | — | 93% |
Knowledge Gemini 3.8 Flash leads
Gemini 3.8 Flash: 74.8 (#2), Qwen2.5-Coder-32B: 33.4 (#203)
| Benchmark | Gemini 3.8 Flash | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Expert | 1524 | 1221 |
| GPQA Diamond | 95.4% | — |
| Humanity's Last Exam | 44.5% | — |
| SimpleQA Verified | 69.7% | — |
| ARC (AI2) Challenge | — | 70.5% |
| MMLU | — | 79.1% |
Multimodal Not comparable
Gemini 3.8 Flash: 40.7 (#45), Qwen2.5-Coder-32B: —
| Benchmark | Gemini 3.8 Flash | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Vision | 1314 | — |
| Blueprint-Bench 2 | 38.6% | — |
| Furniture Assembly | 31.7% | — |
Multilingual Gemini 3.8 Flash leads
Gemini 3.8 Flash: 58.0 (#5), Qwen2.5-Coder-32B: 37.8 (#235)
| Benchmark | Gemini 3.8 Flash | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Non-English | 1491 | 1205 |
| LMArena Chinese | 1554 | 1222 |
| LMArena Russian | 1515 | 1228 |
| LMArena French | 1498 | — |
| LMArena German | 1493 | — |
| LMArena Japanese | 1502 | — |
| LMArena Korean | 1459 | — |
| LMArena Spanish | 1485 | — |
Instruction Following Gemini 3.8 Flash leads
Gemini 3.8 Flash: 78.0 (#13), Qwen2.5-Coder-32B: 61.4 (#245)
| Benchmark | Gemini 3.8 Flash | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Instruction Following | 1490 | 1223 |
| LiveBench Instruction Following | — | 58.7% |
Long Context Gemini 3.8 Flash leads
Gemini 3.8 Flash: 46.3 (#24), Qwen2.5-Coder-32B: 38.0 (#208)
| Benchmark | Gemini 3.8 Flash | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Longer Query | 1508 | 1251 |
Writing & Preference Gemini 3.8 Flash leads
Gemini 3.8 Flash: 72.2 (#15), Qwen2.5-Coder-32B: 41.6 (#240)
| Benchmark | Gemini 3.8 Flash | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Text | 1499 | 1230 |
| LMArena Creative Writing | 1492 | 1174 |
| LMArena Multi-Turn | 1501 | 1222 |
| EQ-Bench Creative Writing | 1748 | — |
| LiveBench Language | — | 23.3% |
Frequently asked questions
Is Gemini 3.8 Flash better than Qwen2.5-Coder-32B?
Gemini 3.8 Flash is the stronger model overall, scoring 61.8 to 33.4 on the Noometry Index. Qwen2.5-Coder-32B costs 2.0× less per token, which makes it the better buy when Gemini 3.8 Flash's lead doesn't matter for your workload.
Which is cheaper, Gemini 3.8 Flash or Qwen2.5-Coder-32B?
Qwen2.5-Coder-32B is cheaper. It lists at $0.66 per million input tokens and $1 per million output tokens; Gemini 3.8 Flash lists at $0.75 and $3.75.
Is Gemini 3.8 Flash or Qwen2.5-Coder-32B better for coding?
Gemini 3.8 Flash scores higher on coding benchmarks: 59.2 versus 22.6 in the Noometry coding category.
Which has the bigger context window?
Gemini 3.8 Flash does, with 1.05M tokens against 33K.
How many benchmarks do Gemini 3.8 Flash and Qwen2.5-Coder-32B share?
13 benchmarks have published results for both models. Gemini 3.8 Flash has 50 scored results on Noometry and Qwen2.5-Coder-32B has 31.