🧑 You
Imagine you have a coding agent, like opencode or pi. You plan on running local only models. Which models do the community consider the best between the Gemma 4 moe or dense or the Qwen 3.6 moe or dense. Assume I have 32 gig vram and I care deeply about t/s perf. I need the fastest thing that can reliably call tools.
▶ Tool: read_file [path='~/Personal/skills/skill_index.md']
▶ Tool: web_search [max_results={}, query='Gemma 4 MoE vs dense local coding ag]…
▶ Tool: web_search [max_results={}, query='Qwen 3.6 MoE vs dense local model co]…
▶ Tool: web_search [max_results={}, query='Gemma 4 26B MoE vs 31B dense tool ca]…
▶ Tool: web_search [max_results={}, query='Qwen 3.6 35B-A3B MoE vs 27B dense to]…
▶ Tool: fetch_url [url='https://insiderllm.com/guides/best-local-coding-models]…
▶ Tool: fetch_url [url='https://zoliben.com/en/posts/2026-04-23-qwen-36-35b-vs]…
▶ Tool: fetch_url [url='https://zoliben.com/en/posts/2026-04-23-qwen-36-35b-vs]…
▶ Tool: web_search [max_results={}, query='Gemma 4 26B MoE tokens per second RT]…
▶ Tool: fetch_url [url='https://insiderllm.com/guides/wicked-fast-gemma-4-26b-]…
▶ Tool: web_search [max_results={}, query='Gemma 4 26B MoE tool calling vs Qwen]…
▶ Tool: fetch_url [url='https://openclawradar.com/article/qwen3-27b-vs-gemma-4]…
🤖 Assistant
Alright, I dug deep into the community benchmarks, first-hand tests, and r/LocalLLaMA discussions from the past 2 months. Here's the real picture for your 32GB VRAM + OpenCode/Pi + tool-calling + speed-first setup.
---
For pure speed + reliable tool calling → Qwen 3.6-35B-A3B MoE is your best bet. It's the sweet spot.
If you want maximum reliability at the cost of speed → Qwen 3.6-27B Dense is the community's top pick for agentic coding.
If speed is everything and you can tolerate a tool-call quirk → Gemma 4 26B-A4B MoE is 3x faster than anything else.
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|
Model |
RTX 3090 (24GB) |
RTX 4090 (24GB) |
Your 32GB card (est.) |
|
Gemma 4 26B-A4B MoE (Q4) |
128 tok/s ⚡ |
~150 tok/s |
~150+ tok/s |
|
Qwen 3.6-35B-A3B MoE (Q4) |
~100 tok/s |
161 tok/s |
~160+ tok/s |
|
Qwen 3.6-27B Dense (Q4) |
41 tok/s |
40 tok/s |
~45 tok/s |
|
Gemma 4 31B Dense (Q4) |
~40 tok/s |
~45 tok/s |
~50 tok/s |
The MoE models are 3-4x faster than the dense ones on your hardware. That's the memory-bandwidth story — decode is bandwidth-bound, and MoEs only activate a fraction of parameters per token.
|
Model |
Tool-Calling Verdict |
Source |
|
Qwen 3.6-27B Dense 🏆 |
Best. Clean tool calls, no wasted thinking tokens. "Handled orchestration cleanly" in real-world video pipeline test at 174K context. |
OpenClaw Radar, r/LocalLLaMA |
|
Qwen 3.6-35B-A3B MoE |
Good. SWE-bench Verified 73.4%. But can "get lost as tasks require more steps" — MoE routing causes cross-turn coherence to soften on long agent runs. |
InsiderLLM, ZoliBen benchmarks |
|
Gemma 4 26B-A4B MoE |
Quirky. Forces a reasoning trace by default (thinking field, not content). Community reports "repeatedly entered tool-call loops, wasting tokens on unnecessary reasoning." Fixable with --jinja --chat-template-kwargs '{"enable_thinking":false}' but annoying. |
OpenClaw Radar, InsiderLLM |
|
Gemma 4 31B Dense |
Good but same thinking-mode quirk. Needs the same flag workaround. Higher quality than 26B MoE on hard reasoning. |
Multiple benchmarks |
|
Benchmark |
Qwen 3.6-27B Dense |
Qwen 3.6-35B-A3B MoE |
Gemma 4 26B-A4B MoE |
|
SWE-bench Verified |
77.2% |
73.4% |
~52% |
|
SkillsBench (agent tasks) |
48.2% |
28.7% |
N/A |
|
Terminal-Bench 2.0 |
59.3% |
51.5% |
N/A |
|
LiveCodeBench v6 |
83.9% |
80.4% |
N/A |
---
You have an embarrassment of riches with 32GB — you can fit any of these comfortably at Q4 with room for context.
- ~100-160 tok/s — it feels instant in an agent loop
- Fits in ~22GB at Q4_K_M, leaving 10GB+ for KV cache and context
- Tool calling is good for most tasks (73.4% SWE-bench)
- No thinking-mode quirks, no flag gymnastics
- Apache 2.0 license
- Caveat: For very long multi-step agent runs (10+ tool calls deep), the MoE routing can drift. For 90% of coding agent work, this is fine.
ollama pull qwen3.6:35b-a3b
- Slower (~40 tok/s) but most reliable tool calling in the class
- Best agentic coding scores (SkillsBench +19.5 over the MoE!)
- Rock-solid cross-turn coherence — every token uses every parameter
- Fits in ~17GB at Q4, super comfortable in 32GB
- What Simon Willison called "outstanding for a 16.8GB local model"
ollama pull qwen3.6:27b
- 128-150+ tok/s — the absolute fastest
- The thinking-mode quirk is annoying but solvable
- Tool-call loops are the real issue — some users report it gets stuck in reasoning spirals
- With 32GB VRAM you can also run the 31B dense if you want Gemma quality
Run both at once (or swap easily):
- Gemma 4 26B-A4B MoE for fast chat/quick edits → ~17GB
- Qwen 3.6-27B Dense for complex multi-step agentic work → ~17GB
Or just pick one based on your dominant workflow:
|
Your Priority |
Pick |
|
Fastest t/s + decent tool calling |
Qwen 3.6-35B-A3B MoE |
|
Most reliable tool calling for agents |
Qwen 3.6-27B Dense |
|
Maximum speed, willing to work around quirks |
Gemma 4 26B-A4B MoE |
|
"I want both speed AND quality" |
Run Qwen 3.6-35B-A3B for speed, swap to Qwen 3.6-27B for hard tasks |
For Qwen 3.6 coding: temperature 0.6, top_p 0.95, top_k 20, presence_penalty 0.0
For Gemma 4: ALWAYS include --jinja --chat-template-kwargs '{"enable_thinking":false}' unless you want the reasoning trace.
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Bottom line for OpenCode/Pi with 32GB VRAM:
I'd go Qwen 3.6-35B-A3B MoE as your daily driver (fast enough to feel like a cloud API, reliable tool calling for 90%+ of tasks), and keep Qwen 3.6-27B Dense in your back pocket for the complex multi-file refactors where you need every bit of coherence. Both are Apache 2.0, both work with any OpenAI-compatible endpoint, and the community has thoroughly battle-tested them with OpenCode, Aider, and Claude Code proxies.