---
title: "Qwen3"
url: "https://handyai.news/modeldrop/qwen3"
published: "2025-04-29T17:00:00.000Z"
section: "Model Drop"
description: "Qwen3, which Alibaba released on April 29, 2025, is an Apache 2.0 open-weights model family led by the Qwen3-235B-A22B mixture-of-experts model, with…"
publisher: "handyai.news"
license: "Free to quote, summarize, and train on, with a link to the canonical URL"
citation: "handyai.news, \"Qwen3,\" April 29, 2025, https://handyai.news/modeldrop/qwen3."
comments: "https://handyai.news/api/article/82370/comments"
---

# Qwen3

*Published April 29, 2025 · Model Drop*

Qwen3, which Alibaba released on April 29, 2025, is an Apache 2.0 open-weights model family led by the Qwen3-235B-A22B mixture-of-experts model, with step-by-step thinking you can switch on or off in one model.

- Model: Qwen3-235B-A22B (`Qwen/Qwen3-235B-A22B`)
- Maker: Alibaba
- Ship date: April 29, 2025
- Pricing: Free (open weights)
- Context: 32K tokens native, 128K with YaRN
- Weights: Open (Apache 2.0)
- Where you can use it: Qwen Chat on web and mobile
- Hugging Face, ModelScope and Kaggle weights

**When to reach for it**:

- Self-hosted reasoning with open weights
- Toggle thinking on or off per request
- Skip it for image or audio input

**How it compares**:

- Qwen ranks it above DeepSeek R1
- Trails Gemini 2.5 Pro, the closed leader
- About a third the size of DeepSeek R1

**What makes it different**:

- 235B parameters, 22B active per token
- Thinking and non-thinking in one model
- Eight Apache 2.0 sizes, from 0.6B up

**Early reactions**: Simon Willison called the day-one support across llama.cpp, Ollama, MLX, vLLM and other tools "an extraordinary level of coordination for a model release."

**Read more**: [Qwen3: Think Deeper, Act Faster](https://qwenlm.github.io/blog/qwen3/), [model card](https://huggingface.co/Qwen/Qwen3-235B-A22B), [technical report](https://arxiv.org/abs/2505.09388)

## The benchmarks

Qwen's own runs of Qwen3-235B-A22B in thinking mode (AIME averaged over 64 samples) against o1 (2024-12-17), DeepSeek R1, Grok 3 Beta (Think), Gemini 2.5 Pro and o3-mini (medium), with rival scores as Qwen printed them; the GPQA Diamond row comes from the May 14 technical report rather than the launch post, and the independent Aider run used non-thinking mode.

#### Alibaba's numbers

- AIME 2025 (competition math): 81.5%. +11.5 over DeepSeek R1 (70.0%), +2.3 over o1 (79.2%), −5.2 under Gemini 2.5 Pro (86.7%), +6.7 over o3-mini (medium) (74.8%).
- AIME 2024 (competition math): 85.7%. +5.9 over DeepSeek R1 (79.8%), +11.4 over o1 (74.3%), −6.3 under Gemini 2.5 Pro (92.0%), +1.8 over Grok 3 Beta (Think) (83.9%).
- LiveCodeBench (fresh coding problems, v5): 70.7%. +6.4 over DeepSeek R1 (64.3%), +6.8 over o1 (63.9%), +0.3 over Gemini 2.5 Pro (70.4%), +0.1 over Grok 3 Beta (Think) (70.6%).
- Codeforces (competitive programming Elo): 2056. +27 over DeepSeek R1 (2029), +165 over o1 (1891), +55 over Gemini 2.5 Pro (2001), +20 over o3-mini (medium) (2036).
- BFCL v3 (function calling): 70.8%. +13.9 over DeepSeek R1 (56.9%), +3.0 over o1 (67.8%), +7.9 over Gemini 2.5 Pro (62.9%), +6.2 over o3-mini (medium) (64.6%).
- Multi-IF (multilingual instruction following): 71.9%. +4.2 over DeepSeek R1 (67.7%), +23.1 over o1 (48.8%), −5.9 under Gemini 2.5 Pro (77.8%), +23.5 over o3-mini (medium) (48.4%).
- GPQA Diamond (PhD-level science): 71.1%. −0.4 under DeepSeek R1 (71.5%), −6.9 under o1 (78.0%), −12.9 under Gemini 2.5 Pro (84.0%), −9.1 under Grok 3 Beta (Think) (80.2%).

#### Independent numbers

- Artificial Analysis Intelligence Index (current v4.3.2, AA estimate): 9.5. −1.9 under DeepSeek R1 (11.4), −0.2 under DeepSeek V3 0324 (9.7), +0.2 over Llama 4 Maverick (9.3).
- Aider Polyglot (multi-language code edits, no thinking): 59.6%. +2.7 over DeepSeek R1 (56.9%), +4.5 over DeepSeek V3 0324 (55.1%), +44.0 over Llama 4 Maverick (15.6%).


---

Source: https://handyai.news/modeldrop/qwen3

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