Back to Articles

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 its 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

• Qwen-3 Chat Template
• Qwen-2.5 Chat Template
• Qwen-QwQ Chat Template

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 its 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.