Both models are Mixture of Experts (MoE) architectures. Qwen3.6 35B A3B outperforms Gemma4 26B A4B on coding/agent tasks significantly, while Gemma4 has advantages in multimodal capabilities and smaller file size.
Model Specifications
| Specification | Gemma4 26B A4B | Qwen3.6 35B A3B |
|---|---|---|
| Architecture | MoE (128 experts) | MoE (8 experts) |
| Total Parameters | ~26B | 35B |
| Active Parameters | 3.8-4B | 3B |
| File Size (Q6) | 23.3GB | 31.8GB |
| Context Length | 256K | 256K-1M (KV compression) |
| Multimodal | Yes (text, image, video) | Yes |
| License | Apache 2.0 | Apache 2.0 |
Benchmark Results
| Benchmark | Qwen3.6-35B | Gemma4-26B | Delta |
|---|---|---|---|
| SWE-Bench Verified | 73.4 | 17.4 | +56.0 |
| Terminal-Bench 2.0 | 51.5 | 42.9 (31B) | +8.6 |
| MCP Tool Use | 37.0 | 18.1 | +2x |
| AIME 2026 | 88.3% | N/A | - |
| LiveCodeBench | 80.0% (31B) | N/A | - |
| Arena ELO | 1452 (31B) | #6 rank | - |
| Source: @namcios, @AIHeadlineJP |
Real-World User Testing
Coding/Vibe Coding Tests
@hosiken's game logic test:
- Gemma4 26B A4B: Fixed bugs in ~4 iterations, produced working code
- Qwen3.6 35B: Hallucinated identifiers, broke after error fixing
@stevibe's vibe coding challenge: - Same stack: Unsloth Q6_K_XL + llama.cpp
- Both models tested side-by-side
- Results: "Gemma 4 fixed the bugs in ~4 iterations; Qwen hallucinated identifiers"
@taziku_co's comparison: - 31.8GB Qwen3.6 vs 23.3GB Gemma4
- Same vibe coding test
- Note: "Benchmarks are less important than real-world tests for production adoption"
Speed Performance
| Hardware | Qwen3.6-35B | Gemma4-26B |
|---|---|---|
| M3 Ultra (90K ctx) | 21.7 tok/s | - |
| M3 Max (DFlash) | 47→70 tok/s | - |
| Mac Mini 128GB | 100 tok/s | - |
| M4 Pro 48GB | 81.6 tok/s | 73.2 tok/s |
| RTX 4090 (Q4) | 5-10 tok/s | 5-10 tok/s |
| DGX Spark | 50+ tok/s | 80 tok/s |
| Sources: @Zimo41650079726, @superoo7, @ainopara |
Key Strengths
Qwen3.6 35B A3B
- Superior coding/agent performance (SWE-Bench +56 points)
- Better MCP tool integration (2x score)
- Lower active parameters (3B vs 3.8-4B)
- Native Ollama support for Claude Code/OpenCode
- Runs on 6GB VRAM with quantization
- 1M context with KV compression (10.7GB→6.9GB)
Gemma4 26B A4B
- Smaller file size (23.3GB vs 31.8GB)
- True multimodal (text + image + video)
- Better for chat/creative tasks
- Japanese language quality praised
- Easier to run on limited VRAM (16GB)
- Works on edge devices (smartphone)
Known Issues
Gemma4 26B A4B
- Tool-call format needs JSON sanitization in vLLM/Ollama/llama.cpp
- Multiturn generation issues reported
- Hallucination more frequent than dense models
- Context compression can cause "rewind" behavior
- Some users report it thinks current date is 2023/2024
Qwen3.6 35B A3B
- Hallucinates identifiers in complex coding tasks
- High RAM usage (needs 32GB+ for smooth use)
- Gets hot on MacBooks
r/LocalLLaMA:
- "Qwen3.6 crushes Gemma 4 on my tests" (2.1k upvotes)
- "Local model finally reaches Claude-like coding quality" (1.4k upvotes)
Real user quotes: - @word_and_number: "Qwen3.5 couldn't reauth my Google accounts, but Gemma4 26B did. Are Google OAuth docs in the Gemma training set?"
- @yamamori_yamori: "Qwen3.6 isn't as good as people say, Gemini 4 31B is actually pretty good too"
- @VibeBloxDev: "Qwen3.6 response is good but Qwen3.5 27B answer quality seems better"
Verdict
For Coding/Agent Tasks: Qwen3.6 35B A3B wins
Significantly better SWE-Bench (+56 pts), MCP tool use (2x), and agent workflows.
For Multimodal/Edge Use: Gemma4 26B A4B wins
True multimodal support, smaller file, works on phones/edge devices.
For Limited VRAM (16GB): Gemma4 26B A4B wins
23.3GB file vs 31.8GB, easier to fit with quantization.
Recommendations
- Coding agents: Use Qwen3.6-35B-A3B with OpenCode/Claude Code
- Multimedia projects: Use Gemma4-26B-A4B for image/video understanding
- Limited hardware: Gemma4-26B-A4B (Q4 fits in ~16GB VRAM)
- Maximum context: Qwen3.6 supports 1M tokens with KV compression