Long Context Processing
Handles up to 262,144 tokens natively in a single context window, with extended context support available via advanced attention mechanisms.
Qwen
Qwen3 235B
Qwen3 235B is an instruction-tuned large language model developed by Alibaba's Qwen team, built on a Mixture-of-Experts (MoE) architecture with 235 billion total parameters. During inference, only 22 billion parameters are activated at a time, which reduces computational cost relative to the model's full parameter count. The model supports a native context window of 262,144 tokens and is released under the Apache 2.0 license, permitting commercial use. This release, versioned as Qwen3-235B-A22B-Instruct-2507, is the non-thinking instruct variant, meaning it produces direct responses without exposing an internal chain-of-thought. It is designed for instruction following, agentic workflows, tool use, multilingual tasks, complex question answering, and coding. The model scores 51.8% on LiveCodeBench v6, 70.3% on AIME25, and 77.5% on GPQA, reflecting its range across coding, mathematical reasoning, and knowledge-intensive tasks.
Apr 28, 2025
262,144 context
262,144 tokens output
Long Context Processing
Instruction Following
Code Generation
Mathematical Reasoning
Knowledge Retrieval
Agentic Tool Use
Model Overview
High-signal model metadata in a structured two-column overview table.
Provider
The entity that provides this model.
Qwen
Model ID
The routed model identifier exposed by upstream providers.
qwen/qwen3-235b-a22b
Input Context Window
The number of tokens supported by the input context window.
262,144
tokens
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
262,144 tokens
tokens
Open Source
Whether the model's code is available for public use.
No
Release Date
When the model was first released.
Apr 28, 2025
1 year ago
Knowledge Cut-off Date
When the model's knowledge was last updated.
July 2025
API Providers
The providers that offer this model. This is not an exhaustive list.
Alibaba
Modalities
Types of data this model can process.
Text
Code
What is Qwen3 235B
A fuller summary of positioning, capabilities, and source-specific details for Qwen3 235B.
Qwen3 235B is an instruction-tuned large language model developed by Alibaba's Qwen team, built on a Mixture-of-Experts (MoE) architecture with 235 billion total parameters. During inference, only 22 billion parameters are activated at a time, which reduces computational cost relative to the model's full parameter count. The model supports a native context window of 262,144 tokens and is released under the Apache 2.0 license, permitting commercial use.
This release, versioned as Qwen3-235B-A22B-Instruct-2507, is the non-thinking instruct variant, meaning it produces direct responses without exposing an internal chain-of-thought. It is designed for instruction following, agentic workflows, tool use, multilingual tasks, complex question answering, and coding. The model scores 51.8% on LiveCodeBench v6, 70.3% on AIME25, and 77.5% on GPQA, reflecting its range across coding, mathematical reasoning, and knowledge-intensive tasks.
Capabilities
What Qwen3 235B supports
Instruction Following
Optimized for direct, helpful responses as the non-thinking instruct variant, without exposing internal chain-of-thought output.
Code Generation
Scores 51.8% on LiveCodeBench v6, covering real-world programming tasks across multiple languages.
Mathematical Reasoning
Achieves 70.3% on AIME25 and 41.8% on ARC-AGI, handling multi-step mathematical and logical problem solving.
Knowledge Retrieval
Scores 77.5% on GPQA and 54.3% on SimpleQA, reflecting broad factual knowledge across science and general domains.
Agentic Tool Use
Supports agentic workflows and tool-use scenarios, making it suitable for multi-step task execution and API-integrated pipelines.
Multilingual Text Generation
Generates and understands text across multiple languages, consistent with the broader Qwen3 model family's multilingual training.
MoE Efficient Inference
Uses a Mixture-of-Experts architecture that activates only 22B of 235B parameters per forward pass, reducing per-token compute.
Pricing for Qwen3 235B
Primary API pricing shown in the same “quick compare” spirit as the reference page.
Input tokens
$0.15
Per million tokens
Output tokens
$1.82
Per million tokens
Price Comparison
Additional usage-cost dimensions synced into the project for this model.
maxTemperature
1
maxResponseSize
262,144 tokens
API Access & Providers
Places where this model is available, based on the synced detail-page metadata.
Alibaba
Provider Endpoints
Endpoint-level provider data currently available for this model.
Alibaba
Model Performance
Benchmark scores synced from the current model source and normalized into the local catalog.
| Benchmark | Score |
|---|---|
|
AIME 2024
American math olympiad problems
|
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GPQA Diamond
PhD-level science questions (biology, physics, chemistry)
|
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HLE
Questions that challenge frontier models across many domains
|
|
|
LiveCodeBench
Real-world coding tasks from recent competitions
|
|
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MATH-500
Undergraduate and competition-level math problems
|
|
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MMLU-Pro
Expert knowledge across 14 academic disciplines
|
|
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SciCode
Scientific research coding and numerical methods
|
Resources & Documentation
Official model cards, release notes, docs, and other references synced from the source page.
Community discussion
What people think about Qwen3 235B
Qwen3 235B discussions are most active in r/LocalLLaMA, r/Qwen_AI, r/LocalLLM. Top Reddit threads cluster around benchmark and model-comparison threads, coding workflow discussions.
The strongest match in this snapshot has 1938 upvotes and 430 comments.
🚀 We’re excited to introduce Qwen3-235B-A22B-Thinking-2507 — our most advanced reasoning model yet!
Over the past 3 months, we’ve significantly scaled and enhanced the thinking capability of Qwen3, achieving:
✅ Improved performance in logical reasoning, math, science & coding
✅ Better general skills: instruction following, tool use, alignment
✅ 256K native context for deep, long-form understanding
🧠 Built exclusively for thinking mode, with no need to enable it manually. The model now natively supports extended reasoning chains for maximum depth and accuracy.
🚀 Qwen3-30B-A3B-2507 and Qwen3-235B-A22B-2507 now support ultra-long context-up to 1 million tokens!
🚀 Qwen3-30B-A3B-2507 and Qwen3-235B-A22B-2507 now support ultra-long context—up to 1 million tokens!
🔧 Powered by:
• Dual Chunk Attention (DCA) – A length extrapolation method that splits long sequences into manageable chunks while preserving global coherence.
• MInference – Sparse attention that cuts overhead by focusing on key token interactions
💡 These innovations boost both generation quality and inference speed, delivering up to 3× faster performance on near-1M token sequences.
✅ Fully compatible with vLLM and SGLang for efficient deployment.
📄 See the update model cards for how to enable this feature.
https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507
https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507
https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507
https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507
https://modelscope.cn/models/Qwen/Qwen3-235B-A22B-Instruct-2507
https://modelscope.cn/models/Qwen/Qwen3-235B-A22B-Thinking-2507
https://modelscope.cn/models/Qwen/Qwen3-30B-A3B-Instruct-2507
https://modelscope.cn/models/Qwen/Qwen3-30B-A3B-Thinking-2507
https://x.com/Alibaba_Qwen/status/1947344511988076547
New Qwen3-235B-A22B with thinking mode only –– no more hybrid reasoning.
Came across this benchmark PR on Aider
I did my own benchmarks with aider and had consistent results
This is just impressive...
PR: [https://github.com/Aider-AI/aider/pull/3908/commits/015384218f9c87d68660079b70c30e0b59ffacf3](https://github.com/Aider-AI/aider/pull/3908/commits/015384218f9c87d68660079b70c30e0b59ffacf3)
Comment: [https://github.com/Aider-AI/aider/pull/3908#issuecomment-2841120815](https://github.com/Aider-AI/aider/pull/3908#issuecomment-2841120815)
FAQ
Common questions about Qwen3 235B
What is the context window for Qwen3 235B?
Qwen3 235B supports a native context window of 262,144 tokens, which is approximately 200,000 words. Extended context beyond this is possible using advanced attention mechanisms.
How many parameters are actually used during inference?
Although the model has 235 billion total parameters, only 22 billion are activated at a time during inference due to its Mixture-of-Experts architecture.
What is the difference between this model and the Thinking variant?
This is the instruct (non-thinking) variant, which produces direct responses without exposing internal chain-of-thought reasoning. The Thinking variant (Qwen3-235B-A22B-Thinking-2507) is a separate model that outputs its reasoning process before answering.
What is the training data cutoff for this model?
Based on the metadata, the training date is listed as July 2025, which corresponds to the 2507 version suffix in the model name.
What license does Qwen3 235B use?
Qwen3 235B is released under the Apache 2.0 license, which permits commercial use, modification, and redistribution subject to the license terms.
What tasks is this model best suited for?
The model is designed for instruction following, agentic workflows, tool use, complex question answering, coding, multilingual tasks, and creative writing. It is not the recommended choice when visible chain-of-thought reasoning is required, as that is handled by the separate Thinking variant.
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