Qwen3 235B A22B Thinking 2507
VerifiedOpen-weight LLM built for deep reasoning and long-context analysis.
About Qwen3 235B A22B Thinking 2507
The architecture centers on a large-scale transformer design with extensive context capacity. At 235 billion total parameters it accommodates detailed multi-turn interactions and lengthy inputs without truncation. Full weight availability enables fine-tuning and local deployment by researchers.
Its Thinking configuration prioritizes step-by-step logical deduction and structured problem solving. This focus yields reliable performance on tasks that require careful intermediate reasoning rather than quick pattern matching. The open-weight release further supports transparency and customization.
Developers commonly apply it to advanced coding, mathematical derivations, and in-depth research summarization. Enterprise teams use it for analyzing long technical documents and generating detailed reports. Its scale and context length make it suitable for both academic experiments and production pipelines.
Capabilities
How Qwen3 235B A22B Thinking 2507 compares
Qwen3 235B A22B Thinking 2507 (striped bar) vs other language models on intelligence, speed and price.
Price
USD per 1M output tokens · Lower is better · Qwen3 235B A22B Thinking 2507 ranks #2 of 98
Sources: Artificial Analysis (intelligence, speed) · OpenRouter (price).
Best for
Long-Document Analysis
The model processes and reasons over documents up to 262144 tokens, enabling accurate summarization and insight extraction from extensive reports or research papers.
Complex Mathematical Problem Solving
It delivers step-by-step logical solutions to advanced math problems, supporting precise calculations and proofs in academic or technical settings.
Code Generation and Debugging
Developers use it to generate, debug, and refine code across languages while following detailed instructions for software projects.
Strengths & limitations
Strengths
- +Efficient MoE design with large context window
- +Strong reasoning and coding performance
- +Robust multilingual support
Limitations
- –Text-only modality
- –High inference compute demands
- –Standard LLM hallucination risks
Cost calculator
Estimate what Qwen3 235B A22B Thinking 2507 would cost for your usage.
Based on Qwen3 235B A22B Thinking 2507's $0.10/1M input · $0.10/1M output. Estimate only — actual cost varies by provider and caching.
Quick start
OpenRouter's API is OpenAI-compatible — most SDKs work by just swapping the base URL. Only the model slug changes between models.
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://openrouter.ai/api/v1",
apiKey: process.env.OPENROUTER_API_KEY,
});
const completion = await client.chat.completions.create({
model: "qwen/qwen3-235b-a22b-thinking-2507",
messages: [{ role: "user", content: "Hello!" }],
});
console.log(completion.choices[0].message.content);Model slug: qwen/qwen3-235b-a22b-thinking-2507
Editor's verdict
Qwen3 235B A22B Thinking 2507 is Alibaba Qwen's open-weight language models with a 262K-token context window.
At $0.10 per 1M output tokens, it is very cost-efficient for its class.
As an open-weight model you can self-host it or call it through a hosted API.
Best suited to efficient moe design with large context window and strong reasoning and coding performance.
Frequently asked questions
The model supports a context window of 262144 tokens.
User reviews
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