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 16K
tokens
Input Cost $0.09
per million tokens
Output Cost $0.55
per million tokens
Release Date Jul 21, 2025
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 | 11.1% |
| LiveCodeBench Recent coding problems | 52.4% |
| 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 | 33.9% |
| 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 Flash
Qwen3.6 Flash is the speed-optimized tier of Alibaba's Qwen3.6 model family, designed for high-throughput, low-latency inference pipelines. It sits alongside Qwen3.6 Max Preview, Plus, and 35B-A3B in the product lineup, targeting use cases where fast response times matter more than peak benchmark scores. Like other Qwen3.6 models, it builds on a hybrid architecture combining linear attention with sparse mixture-of-experts routing. It is best suited for high-volume production workloads such as classification, extraction, summarization, and lightweight agent tasks where latency and cost efficiency are the primary constraints.
ChatQwen3.5 Plus 2026-04-20
Qwen3.5 Plus is a proprietary hosted model from Alibaba, built on the Qwen3.5-397B-A17B Mixture-of-Experts architecture with 397 billion total parameters and 17 billion active per token. Its headline feature is a 1-million-token native context window — among the largest available via API — making it well suited for processing entire codebases, long documents, or extended multi-turn conversations in a single request. It supports both a deep-thinking mode and an "Auto" mode that adaptively invokes tools like web search and code interpreters. This April 20, 2026 snapshot reflects ongoing improvements to the model since its original February 2026 launch. The Qwen3.5 series demonstrated strong multimodal performance across reasoning, coding, and vision tasks. A solid general-purpose option for developers needing large-context capabilities without migrating to the newer Qwen3.6 line.
ChatQwen3.6 27B
Qwen3.6 27B is a dense 27-billion-parameter multimodal model from Alibaba's Qwen team, purpose-built for agentic coding and repository-level reasoning. It scores 77.2% on SWE-bench Verified and 59.3% on Terminal-Bench 2.0, outperforming the previous-generation Qwen3.5-397B-A17B across all major coding benchmarks despite being far smaller. It natively supports text, image, and video inputs with a 262K-token context window, extendable to 1M tokens. A standout feature is Thinking Preservation, which retains reasoning traces across conversation turns — reducing redundant computation in multi-step agent loops. The model uses a hybrid attention architecture combining Gated DeltaNet with traditional self-attention. Ideal for developers building coding agents, multi-turn tool-use workflows, or frontend generation pipelines.
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 | $0.09 |
| Output | $0.55 |
Qwen3 235B A22B Instruct 2507 was created by Qwen and released on Jul 21, 2025.
Qwen3 235B A22B Instruct 2507 supports a context window of 262K tokens. For reference, that is roughly equivalent to 524 pages of text.
Qwen3 235B A22B Instruct 2507 can generate up to 16K tokens in a single response.
Qwen3 235B A22B Instruct 2507 scores 12.1 on the Artificial Analysis Intelligence Index, outperforming 52% of tracked models. On math, it scores 71.7 (outperforms 65% of models).
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