Qwen just released their first 3.8 model.

The main addition in 3.8 is prompt-steered reasoning effort. You can tell the model how deeply to think by setting reasoning_effort to xhigh, medium, or low.

However, the official template still has some serious problems:

• You cannot disable thinking. If you pass enable_thinking=false, it 3.8 crashes with a hard exception.
• Chat history gets poisoned. In multi-turn chats, the official template injects blank <think></think> tags before real thoughts.
• Tool calling crashes. If your client passes arguments as JSON strings (the standard OpenAI API format), the official template crashes.
• Agent stalls. The official template often drops mid-dialogue system messages and wedges multi-step tool loops.

I maintain a single, drop-in fixed Jinja template that works across all Qwen 3.5, 3.6, and 3.8 models:

https://huggingface.co/froggeric/Qwen-Fixed-Chat-Templates

What this template does:

• Full 3.8 reasoning effort support: Steer reasoning depth with reasoning_effort (xhigh, high, low, medium).
• Restores the thinking toggle: Turn off reasoning whenever you want fast answers, either via kwargs or by typing <|think_off|> in your prompt.
• 100% KV Cache hits: Keeps past thoughts intact by default so your prefix cache stays warm across turns.
• llama.cpp support: Native support for the new --reasoning-preserve flag.
• Universal tool parsing: Handles both Python dicts and JSON strings. Works on llama.cpp, vLLM, LM Studio, and MLX.

Recommended llama-server launch command:

llama-server -m your_model.gguf --jinja --chat-template-file chat_template.jinja --reasoning-format deepseek 

(The --reasoning-format deepseek flag separates thinking into the OpenAI reasoning_content field so OpenCode, Claude Code, and other harnesses do not stall on raw tokens).

Note on hardware:

I cannot run a 2.4 trillion parameter model on my local rig. The template passes all 28 automated tests and tokenizer parity checks, but I would appreciate feedback from anyone testing it with Qwen 3.8.