Qwen: Qwen3 235B A22B Instruct 2507
qwen/qwen3-235b-a22b-2507
Access Qwen3 235B A22B Instruct 2507 from Qwen using Puter.js AI API.
Get Started// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms", {
model: "qwen/qwen3-235b-a22b-2507"
}).then(response => {
document.body.innerHTML = response.message.content;
});
<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
puter.ai.chat("Explain quantum computing in simple terms", {
model: "qwen/qwen3-235b-a22b-2507"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
# pip install openai
from openai import OpenAI
client = OpenAI(
base_url="https://api.puter.com/puterai/openai/v1/",
api_key="YOUR_PUTER_AUTH_TOKEN",
)
response = client.chat.completions.create(
model="qwen/qwen3-235b-a22b-2507",
messages=[
{"role": "user", "content": "Explain quantum computing in simple terms"}
],
)
print(response.choices[0].message.content)
curl https://api.puter.com/puterai/openai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_PUTER_AUTH_TOKEN" \
-d '{
"model": "qwen/qwen3-235b-a22b-2507",
"messages": [
{"role": "user", "content": "Explain quantum computing in simple terms"}
]
}'
Model Card
Qwen3 235B A22B (2507) is the July 2025 updated version with significant improvements in instruction following, reasoning, coding, tool usage, and 256K long-context understanding.
Context Window 262K
tokens
Max Output N/A
tokens
Input Cost $0.07
per million tokens
Output Cost $0.1
per million tokens
Release Date Jul 28, 2025
Output Speed 70
tokens / sec
Latency 1.23s
time to first token
Model Playground
Try Qwen3 235B A22B Instruct 2507 instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
Benchmarks
How Qwen3 235B A22B Instruct 2507 performs on standard evaluations.
| Benchmark | Score |
|---|---|
| GPQA Diamond Graduate-level science Q&A | 75.3% |
| Humanity's Last Exam Cross-domain reasoning | 10.6% |
| LiveCodeBench Recent coding problems | 52.4% |
| SciCode Scientific programming | 36.0% |
| MATH-500 Competition math | 98.0% |
| AIME 2024 Advanced math exam | 71.7% |
| AIME 2025 Advanced math exam | 71.7% |
| IFBench Instruction following | 46.1% |
| LCR Long-context reasoning | 31.2% |
| Terminal-Bench Hard Agentic terminal tasks | 15.2% |
| τ²-Bench Tool use / agents | 33.3% |
Scores sourced from Artificial Analysis.
Find other Qwen models →
Qwen3.6 Plus
Qwen 3.6 Plus is Alibaba's flagship large language model, built on a hybrid architecture combining linear attention with sparse mixture-of-experts routing for high throughput and scalability. It's optimized for agentic coding and complex multi-step workflows. On Terminal-Bench 2.0, it scores 61.6, surpassing Claude 4.5 Opus (59.3), while its 78.8 on SWE-bench Verified places it close behind. It also leads on MCPMark (48.2%) for tool-calling reliability. A native multimodal model, it handles text, images, and documents within a 1M-token context window with up to 65K output tokens. Notable features include always-on chain-of-thought reasoning, native function calling, and a preserve_thinking parameter that retains reasoning across multi-turn agent loops. A strong fit for developers building AI coding agents, terminal automation, and tool-using pipelines.
ChatQwen3.5-9B
Qwen 3.5 9B is a 9-billion parameter open-source multimodal model by Alibaba's Qwen Team, featuring a 262K native context window (extendable to ~1M tokens), support for text, image, and video input, and coverage of 201 languages. It uses a hybrid Gated DeltaNet architecture and outperforms much larger models like Qwen3-30B and OpenAI's gpt-oss-120B on key benchmarks including reasoning, vision, and document understanding.
ChatQwen3.5-122B-A10B
Qwen 3.5 122B (10B Active) is Alibaba's largest medium-sized MoE model, activating only 10B of its 122B total parameters per inference pass. It excels at agentic tasks like tool use and multi-step reasoning, leading the Qwen 3.5 lineup on benchmarks such as BFCL-V4 and BrowseComp. It supports 262K native context (extendable to 1M), native multimodal input, and 201 languages under Apache 2.0.
Frequently Asked Questions
You can access Qwen3 235B A22B Instruct 2507 by Qwen through Puter.js AI API. Include the library in your web app or Node.js project and start making calls with just a few lines of JavaScript — no backend and no configuration required. You can also use it with Python or cURL via Puter's OpenAI-compatible API.
Yes, it is free if you're using it through Puter.js. With the User-Pays Model, you can add Qwen3 235B A22B Instruct 2507 to your app at no cost — your users pay for their own AI usage directly, making it completely free for you as a developer.
| Price per 1M tokens | |
|---|---|
| Input | $0.07 |
| Output | $0.1 |
Qwen3 235B A22B Instruct 2507 was created by Qwen and released on Jul 28, 2025.
Qwen3 235B A22B Instruct 2507 supports a context window of 262K tokens. For reference, that is roughly equivalent to 524 pages of text.
Yes — the Qwen3 235B A22B Instruct 2507 API works with any JavaScript framework, Node.js, or plain HTML through Puter.js. Just include the library and start building. See the documentation for more details.
Get started with Puter.js
Add Qwen3 235B A22B Instruct 2507 to your app without worrying about API keys or setup.
Read the Docs View Tutorials