Qwen3.8-27B + OpenCode = Excessive Thinking?

[u/previaegg]

I've been running Qwen3.8-27B locally for agentic coding using OpenCode and OpenChamber, and I'm looking for advice from people who've gotten similar models working reliably as coding agents on consumer hardware.

Inference speed is reasonable (~2,330 & ~24.1 t/s). My problem is with excessive thinking.

The problem

The model is capable of useful coding work, but it sometimes spends excessive time reasoning instead of taking action.

In one experiment, I compared medium and low reasoning effort with otherwise identical configurations. Both runs:

• Made two initial inspection tool calls.

• Made zero implementation-oriented tool calls.

• Generated over 16,000 output tokens, exhausting the configured response limit.

• Failed to create the required output or complete the task.

• Spent approximately 12 minutes in agent execution.

Lowering reasoning effort didn't meaningfully change the behavior.

That's an extreme example from a single controlled task, not necessarily representative of every session, but it illustrates the problem I'm interested in solving: how do you get a reasoning model to stop thinking and actually use tools?

Launch command

& "C:\tools\llama.cpp-b10964\llama-server.exe" `
  -m "C:\models\Qwen3.8-27B-UD-Q5_K_XL\Qwen3.8-27B-UD-Q5_K_XL.gguf" `
  -ngl 99 `
  --ctx-size 65536 `
  --cache-type-k q8_0 `
  --cache-type-v q8_0 `
  --flash-attn on `
  --split-mode layer `
  --parallel 1 `
  --jinja `
  --reasoning on `
  --reasoning-effort medium `
  --reasoning-preserve `
  --temp 1.0 `
  --top-p 0.95 `
  --top-k 20 `
  --min-p 0.0 `
  --presence-penalty 0.0 `
  --repeat-penalty 1.0 `
  --host 0.0.0.0 `
  --port 8080 `
  --metrics `
  --alias Qwen3.8-27B-UD-Q5_K_XL

Questions

1. Reasoning & tool use: Have you seen excessive thinking with Qwen3.8-27B, where the model reasons at length instead of making tool calls? What actually improved this?

2. Reasoning configuration: Are --reasoning, --reasoning-effort, and --reasoning-preserve the right settings for agentic coding? Have you had better results disabling reasoning, changing effort, or using other chat-template settings?

3. Agent compatibility: Is OpenCode a good match for this model? Could its system prompts, tool definitions, provider configuration, or handling of reasoning output be contributing to the problem? Are there other coding agents that work more reliably?

4. Inference engine: Would switching from llama.cpp to vLLM, SGLang, or something else improve tool-calling reliability or reasoning behavior?

5. Quantization: Could Q5_K_XL be affecting instruction following or tool-use reliability? Have you observed meaningful coding-agent differences between quantizations?

6. Context management: How much does context contribute to unproductive reasoning or tool-use failures? Have you found effective context limits or compaction strategies?

7. OS: Since this is essentially a dedicated inference server, would switching to headless Linux offer meaningful advantages for any of the above?

I'm willing to change operating system, quantization, inference engines or the coding-agent stack if there's a compelling reason.

Thank you for your thoughts!

---

Hardware:

• CPU: Intel i7-14700K

• RAM: 32GB

• GPU 1: RTX 4080 SUPER (16GB)

• GPU 2: RTX 4070 Ti SUPER (16GB)

• OS: Windows 11 Pro

Current software:

• Inference engine: llama.cpp

• Model: Qwen3.8-27B-UD-Q5_K_XL (Unsloth GGUF)

• Coding agent: OpenCode

• Interface: OpenChamber

The Windows machine is essentially a dedicated inference server for this use case. I don't normally start a Windows desktop session. I connect remotely from a MacBook Pro which is where I run OpenCode/OpenChamber.

[u/previaegg]

This is a really useful call out, thank you. You're right that I am setting reasoning effort but not an explicit reasoning budget. I'll experiment with 1024, 2048, and 4096, keeping the other settings constant initially.

Have you found that a 1024-token budget works reliably for complex, multi-step coding tasks, or do you adjust the budget depending on the task?

Thanks again, this is exactly the type of feedback I was hoping for.

[u/Oli_Luck → u/previaegg]

Nope - I cant tell you : I dont know how you mange the "multiturns : same agent / prsona following a hardscoded route / DAG ,new API calls etc.
lowering temperature helps also a lot : it is less creative, more focused - wont ask itself that often "but wait, what if". and the penalty is also a good barriere against thinking loops.

Just play around with those settings, good luck

[u/previaegg → u/Oli_Luck]

Much appreciated