The 4 Things Qwen-3’s Chat Template Teaches Us
What a boring Jinja snippet tells us about the new Qwen-3 model.
The new Qwen-3 model by Qwen ships with a much more sophisticated chat template than it's predecessors Qwen-2.5 and QwQ. By taking a look at the differences in the Jinja template, we can find interesting insights into the new model.
Chat Templates
What is a Chat Template?
A chat template defines how conversations between users and models are structured and formatted. The template acts as a translator, converting a human-readable conversation:
[
{ role: "user", content: "Hi there!" },
{ role: "assistant", content: "Hi there, how can I help you today?" },
{ role: "user", content: "I'm looking for a new pair of shoes." },
]
into a model friendly format:
<|im_start|>user
Hi there!<|im_end|>
<|im_start|>assistant
Hi there, how can I help you today?<|im_end|>
<|im_start|>user
I'm looking for a new pair of shoes.<|im_end|>
<|im_start|>assistant
<think>
</think>
You can easily view the chat template for a given model on the Hugging Face model page.
Chat Template for Qwen/Qwen3-235B-A22B
Let's dive into the Qwen-3 chat template and see what we can learn!
1. Reasoning doesn't have to be forced
and you can make it optional via a simple prefill...
Qwen-3 is unique in it's ability to toggle reasoning via the enable_thinking flag. When set to false, the template inserts an empty <think></think> pair, telling the model to skip step‑by‑step thoughts. Earlier models baked the <think> tag into every generation, forcing chain‑of‑thought whether you wanted it or not.
{# Qwen-3 #}
{%- if enable_thinking is defined and enable_thinking is false %}
{{- '<think>\n\n</think>\n\n' }}
{%- endif %}
QwQ for example, forces reasoning in every conversation.
{# QwQ #}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n<think>\n' }}
{%- endif %}
If the enable_thinking is true, the model is able to decide whether to think or not.
You can test test out the template with the following code:
import { Template } from "@huggingface/jinja";
import { downloadFile } from "@huggingface/hub";
const HF_TOKEN = process.env.HF_TOKEN;
const file = await downloadFile({
repo: "Qwen/Qwen3-235B-A22B",
path: "tokenizer_config.json",
accessToken: HF_TOKEN,
});
const config = await file!.json();
const template = new Template(config.chat_template);
const result = template.render({
messages,
add_generation_prompt: true,
enable_thinking: false,
bos_token: config.bos_token,
eos_token: config.eos_token,
});
2. Context Management Should be Dynamic
Qwen-3 utilizes a rolling checkpoint system, intelligently preserving or pruning reasoning blocks to maintain relevant context. Older models discarded reasoning prematurely to save tokens.
Qwen-3 introduces a "rolling checkpoint" by traversing the message list in reverse to find the latest user turn that wasn’t a tool call. For any assistant replies after that index it keeps the full <think> blocks; everything earlier is stripped out.
Why this matters:
- Keeps the active plan visible during a multi‑step tool call.
- Supports nested tool workflows without losing context.
- Saves tokens by pruning thoughts the model no longer needs.
- Prevents "stale" reasoning from bleeding into new tasks.
Example
Here's an example of chain-of-thought preservation through tool calls with Qwen-3 and QwQ.
Check out @huggingface/jinja for testing out the chat templates
3. Tool Arguments Need Better Serialization
Before, every tool_call.arguments field was piped through | tojson, even if it was already a JSON‑encoded string—risking double‑escaping. Qwen‑3 checks the type first and only serializes when necessary.
{# Qwen3 #}
{%- if tool_call.arguments is string %}
{{- tool_call.arguments }}
{%- else %}
{{- tool_call.arguments | tojson }}
{%- endif %}
4. There's No Need for a Default System Prompt
Like many models, the Qwen‑2.5 series has a default system prompt.
You are Qwen, created by Alibaba Cloud. You are a helpful assistant.
This is pretty common as it helps models respond to user questions like "Who are you?".
Qwen-3 and QwQ ship without this default system prompt. Despite this, the model can still accurately identify its creator if you ask it.
Conclusion
Qwen-3 shows us that through the chat_template we can provide better flexibility, smarter context handling, and improved tool interaction. These improvements not only improve capabilities, but also make agentic workflows more reliable and efficent.
