Qwen 3.6 vs Gemma 4
Qwen 3.6 leads Gemma 4 across coding, terminal, math, and frontend benchmarks
Head-to-head comparison of the Qwen 3.6 and Google Gemma 4 model families. Qwen 3.6 35B A3B outperforms Gemma 4 26B A4B on SWE-bench Verified (73.4% vs 52.0%), Terminal-Bench 2.0 (51.5 vs 42.9), and AIME 2025 (92.7% vs 88.3%). The 27B dense model extends the lead further with 77.2% SWE-bench, 59.3 Terminal-Bench, 83.9 LiveCodeBench, and 48.2 SkillsBench (beating Claude 4.5 Opus at 45.3). Both families offer open-weight MoE and dense variants under permissive licenses.
Benchmarks
Qwen 3.6 vs Gemma 4 - detailed benchmark comparison across 8 evaluations
Comprehensive benchmark results comparing both model families across software engineering, coding, terminal operations, mathematical reasoning, frontend generation, and practical coding skills.
Qwen 3.6 demonstrates a significant and consistent performance advantage over Gemma 4 across all available benchmarks. The gap is particularly pronounced on SWE-bench Verified, where Qwen 3.6 leads by over 20 percentage points in the MoE comparison and over 25 points with the 27B dense model. The SkillsBench result (48.2 for 27B, beating Claude 4.5 Opus at 45.3) highlights Qwen's strength in practical engineering judgment beyond raw code generation.
SWE-bench Verified: Qwen 3.6 27B 77.2% vs Gemma 4 26B A4B 52.0% (+25.2pp)
Terminal-Bench 2.0: Qwen 3.6 27B 59.3 vs Gemma 4 26B A4B 42.9 (+38%)
AIME 2025: Qwen 3.6 35B A3B 92.7% vs Gemma 4 26B A4B 88.3%
SkillsBench: Qwen 3.6 27B 48.2 beats Claude 4.5 Opus (45.3)
QwenWebBench: Qwen 3.6 27B 1487 - frontend code generation leader
Benchmark table
Qwen 3.6 vs Gemma 4 - full results across all evaluations
Side-by-side benchmark comparison of Qwen 3.6 and Gemma 4 model variants across software engineering, coding, math, and practical skill evaluations.
| Benchmark | Qwen 3.6 27B Dense Top performer | Qwen 3.6 35B A3B MoE 3B active | Gemma 4 26B A4B MoE 4B active | Gemma 4 31B Dense |
|---|---|---|---|---|
SWE-bench Verified Real-world software engineering | 77.2% | 73.4% | 52.0% | - |
Terminal-Bench 2.0 Terminal operations and system admin | 59.3 | 51.5 | 42.9 | - |
AIME 2025 Competition mathematics | 94.1% | 92.7% | 88.3% | - |
LiveCodeBench Competitive code generation | 83.9 | 80.4 | - | - |
SkillsBench Practical coding skills | 48.2 | - | - | - |
QwenWebBench Frontend code generation | 1487 | 1397 | - | - |
NL2Repo Natural language to repository | 36.2 | - | - | - |
Claw-Eval Avg End-to-end agentic coding | 72.4 | 68.7 | - | - |
Active parameters Parameters computed per token | 27B (all) | 3B (of 35B) | 4B (of 26B) | 31B (all) |
Benchmark results from official model releases. Qwen 3.6 data from Alibaba (March 2026), Gemma 4 data from Google. SkillsBench and QwenWebBench results from Qwen official benchmarks.
Benchmark Lead
25+ points ahead on SWE-bench - the clear choice for software engineering
Qwen 3.6 Plus scores 78.8% on SWE-bench Verified while Gemma 4 scores 50.4% - a 28-point gap on the most important agentic coding benchmark. The Qwen 3.6 27B open-weight model at 77.2% SWE-bench outperforms Gemma 4's top tier by over 26 points. For software engineering, debugging, and code generation tasks, Qwen 3.6 delivers significantly better results.
- Qwen 3.6 Plus: 78.8% SWE-bench vs Gemma 4: 50.4% - 28-point gap
- Qwen 3.6 27B: 77.2% SWE-bench, beats Gemma 4 by over 26 points
- 61.6 Terminal-Bench 2.0, 48.2 SkillsBench (27B, beats Claude 4.5 Opus)
Open Weight
Apache 2.0 models for local deployment on your hardware
Qwen 3.6 offers open-weight models under Apache 2.0: the 27B dense model runs on 16GB VRAM (IQ4_XS) and the 35B A3B MoE runs on Mac M4 16GB. Gemma 4 also offers open-weight models, but Qwen 3.6 delivers substantially higher benchmark performance in comparable model sizes, particularly for coding and agentic tasks.
- 27B dense: 16GB VRAM, 77.2% SWE-bench, Apache 2.0 licensed
- 35B A3B MoE: Mac M4 16GB, 73.4% SWE-bench, 68.7 Claw-Eval
- Deploy with Ollama, vLLM, llama.cpp, or SGLang
Try Qwen 3.6
Start using Qwen 3.6 today
Try the free chat, integrate via API, or deploy open-weight models locally.
Compare and deploy
Explore both model families
Compare Qwen and Gemma 4, or deploy Qwen open-weight models locally.
Qwen ecosystem
Choose the model family that leads on the benchmarks that matter most
Qwen 3.6 delivers significantly stronger performance than Gemma 4 on software engineering (+25pp SWE-bench), terminal operations (+38% Terminal-Bench), and mathematical reasoning. All with fewer active parameters and faster inference.
Try Qwen 3.6
Experience the performance difference for yourself - 25+ points ahead on SWE-bench
Chat with Qwen 3.6 for free and see why it leads Gemma 4 by 25+ percentage points on SWE-bench, 38% on Terminal-Bench, and beats Claude 4.5 Opus on SkillsBench. Open-weight, locally deployable, and API accessible.