Qwen3 235B A22B Instruct 2507
VerifiedOpen-weight instruct LLM built for long-context multilingual tasks.
About Qwen3 235B A22B Instruct 2507
The model follows the Qwen3 architecture and ships as an instruction-tuned variant. Its open-weight release allows full access for local deployment, customization, and further training. The 262k context supports processing of lengthy documents without truncation.
Strengths center on coherent multi-turn dialogue, code assistance, and cross-lingual understanding. Because weights are publicly available, users can run the model on their own hardware or cloud instances with standard inference frameworks.
Typical usage includes building chat systems, document analysis pipelines, and agent workflows that benefit from long context and open licensing. Researchers also employ it as a base for domain-specific adaptation.
Capabilities
How Qwen3 235B A22B Instruct 2507 compares
Qwen3 235B A22B Instruct 2507 (striped bar) vs other language models on intelligence, speed and price.
Price
USD per 1M output tokens · Lower is better · Qwen3 235B A22B Instruct 2507 ranks #3 of 98
Sources: Artificial Analysis (intelligence, speed) · OpenRouter (price).
Best for
Long-Context Document Analysis
The model processes and reasons over very large inputs thanks to its 262144-token context window, making it suitable for summarizing or analyzing lengthy reports and transcripts.
Complex Code Generation Tasks
It generates and refines code across languages while following detailed instructions, supporting development workflows that require both precision and scale.
Multilingual Mathematical Problem Solving
The model tackles advanced math and logical challenges in multiple languages, delivering step-by-step solutions for research or educational use cases.
Strengths & limitations
Strengths
- +Strong reasoning and coding capabilities
- +Efficient MoE design at large scale
- +Robust multilingual performance
- +High-quality instruction adherence
Limitations
- –Text-only modality with no vision support
- –High inference cost due to model size
- –May require retrieval for current events
Cost calculator
Estimate what Qwen3 235B A22B Instruct 2507 would cost for your usage.
Based on Qwen3 235B A22B Instruct 2507's $0.09/1M input · $0.10/1M output. Estimate only — actual cost varies by provider and caching.
Download & self-host Qwen3 235B A22B Instruct 2507
This is an open-weight model. Download the weights from Hugging Face or load it directly with Transformers.
# Install the Hugging Face CLI
pip install -U "huggingface_hub[cli]"
# Download the model weights
hf download Qwen/Qwen3-235B-A22B-Instruct-2507
# Or load it directly in Python
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-235B-A22B-Instruct-2507")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-235B-A22B-Instruct-2507", device_map="auto")Inference providers
Hosted APIs that serve Qwen3 235B A22B Instruct 2507 (via Hugging Face Inference Providers).
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-2507",
messages: [{ role: "user", content: "Hello!" }],
});
console.log(completion.choices[0].message.content);Model slug: qwen/qwen3-235b-a22b-2507
Editor's verdict
Qwen3 235B A22B Instruct 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 (235B parameters) or call it through a hosted API.
Best suited to strong reasoning and coding capabilities and efficient moe design at large scale.
Frequently asked questions
The model provides a context window of 262144 tokens.
User reviews
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