# Qwen3 235B A22B ![Organization](https://img.shields.io/badge/Org-Alibaba-ff6a00) ![License](https://img.shields.io/badge/License-Apache%202.0-22c55e) ![Released](https://img.shields.io/badge/Released-Apr%202025-555) ![MoE](https://img.shields.io/badge/Type-MoE-6366f1) ![Params](https://img.shields.io/badge/Params-235B%2F22B%20active-blue) > **Quick answer:** Qwen3 235B A22B is Alibaba's April 2025 flagship MoE model, scoring 95.6% Arena Hard, 70.8% BFCL, 65.9% LiveCodeBench, 94.4% GSM8K, and 61.8% Aider. Apache 2.0 with 235B total parameters (22B active). Alibaba's strongest open-weight model. ## At a Glance **Where Qwen3 235B A22B leads** - 95.6% Arena Hard — near-frontier instruction quality; above DeepSeek-V3 (76.2%) - 65.9% LiveCodeBench — competitive coding with frontier closed models - 70.8% BFCL — strong function calling - 94.4% GSM8K — excellent math - Apache 2.0 — fully open, including fine-tuning and redistribution - MoE efficiency: 22B active params at inference vs 235B total **Where it lags** - 68.2% MMLU-Pro — moderate academic knowledge - 71.8% MATH — below strong reasoning models - 17.6% AetherCode — weak on this agent benchmark - Requires large GPU cluster for full deployment **Best for:** Open-source frontier instruction quality; tool-use pipelines; teams needing Apache 2.0 near-frontier models on shared infrastructure. ## What Qwen3 235B A22B Is Qwen3 235B A22B is Alibaba's April 2025 flagship in the Qwen3 generation — succeeding Qwen2.5. The "235B A22B" notation means 235 billion total parameters with 22 billion active at each token inference pass (MoE routing). This gives DeepSeek-level per-token compute cost while maintaining the capacity of a 235B model. The 95.6% Arena Hard score is exceptional for an open-weight model — competitive with proprietary models like Claude Opus-class and GPT-4-class systems from the same period. Combined with Apache 2.0, this is a strong option for teams needing frontier-class instruction quality without vendor lock-in. ## Specifications | Field | Value | |---|---| | Organization | Alibaba | | License | Apache 2.0 | | HuggingFace | Qwen/Qwen3-235B-A22B | | Release date | April 29, 2025 | | Parameters | 235B total / 22B active (MoE) | | Modality | Text only | | Context window | 128K tokens | ## Pricing Open weights under Apache 2.0 — self-host. Also available via Alibaba Cloud (DashScope) and major providers. ## Public Benchmark Scores | Benchmark | Score | Source | Date | |---|---|---|---| | [Arena Hard](/benchmarks/arena-hard-v2) | 95.6% | Benchgen evaluation | 2025-04 | | [GSM8K](/benchmarks/gsm8k) | 94.4% | Benchgen evaluation | 2025-04 | | [BFCL](/benchmarks/bfcl-v3) | 70.8% | Benchgen evaluation | 2025-04 | | [LiveCodeBench](/benchmarks/livecodebench) | 65.9% | Benchgen evaluation | 2025-04 | | [MATH](/benchmarks/math) | 71.8% | Benchgen evaluation | 2025-04 | | [MMLU-Pro](/benchmarks/mmlu-pro) | 68.2% | Benchgen evaluation | 2025-04 | | [Aider](/benchmarks/aider) | 61.8% | Benchgen evaluation | 2025-04 | ## Qwen3 235B A22B vs Alternatives | Model | Arena Hard | LiveCodeBench | License | Architecture | |---|---|---|---|---| | **Qwen3 235B A22B** | 95.6% | 65.9% | Apache 2.0 | MoE 235B/22B | | [DeepSeek-V3](/models/deepseek/deepseek-v3) | 76.2% | — | MIT | MoE | | [Qwen3 30B A3B](/models/alibaba/qwen3-30b-a3b) | 91% | 62.6% | Apache 2.0 | MoE 30B/3B | | [Llama 3.3 70B Instruct](/models/meta/llama-3-3-70b-instruct) | — | — | Llama 3.3 | Dense | Qwen3 235B A22B leads open-weight models on Arena Hard (95.6%). For lower compute: Qwen3 30B A3B (91% Arena Hard, 3B active params). For general open-weight: DeepSeek-V3. ## Related Models & Benchmarks - Compare: [Qwen3 30B A3B](/models/alibaba/qwen3-30b-a3b) · [DeepSeek-V3](/models/deepseek/deepseek-v3) · [Llama 3.3 70B Instruct](/models/meta/llama-3-3-70b-instruct) - Benchmarks: [Arena Hard](/benchmarks/arena-hard-v2) · [LiveCodeBench](/benchmarks/livecodebench) · [BFCL](/benchmarks/bfcl-v3) ## Frequently Asked Questions

What is Qwen3 235B A22B?

Alibaba's April 2025 flagship MoE model: 235B total parameters, 22B active at inference. 95.6% Arena Hard, 65.9% LiveCodeBench, 70.8% BFCL. Apache 2.0.

What does "235B A22B" mean in Qwen3?

235B = total parameter count. A22B = 22B parameters active per token inference pass. Mixture-of-Experts routing selects which expert layers to activate, making inference cost similar to a 22B dense model while retaining 235B model capacity.

--- *Specs from Alibaba's Qwen3 235B A22B release (April 2025) and Benchgen evaluations. Last updated 2026-07-24.*

Qwen3 235B A22B — Training jobs

Training jobs