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Chinese language e-commerce large Alibaba’s “Qwen Crew” has carried out it once more.
Mere days after releasing without spending a dime and with open supply licensing what’s now the highest performing non-reasoning giant language mannequin (LLM) on the earth — full cease, even in comparison with proprietary AI fashions from well-funded U.S. labs equivalent to Google and OpenAI — within the type of the lengthily named Qwen3-235B-A22B-2507, this group of AI researchers has come out with one more blockbuster mannequin.
That’s Qwen3-Coder-480B-A35B-Instruct, a brand new open-source LLM targeted on aiding with software program growth. It’s designed to deal with complicated, multi-step coding workflows and may create full-fledged, practical functions in seconds or minutes.
The mannequin is positioned to compete with proprietary choices like Claude Sonnet-4 in agentic coding duties and units new benchmark scores amongst open fashions.
It’s obtainable on Hugging Face, GitHub, Qwen Chat, through Alibaba’s Qwen API, and a rising listing of third-party vibe coding and AI instrument platforms.
Open sourcing licensing means low price and excessive optionality for enterprises
However not like Claude and different proprietary fashions, Qwen3-Coder, which we’ll name it for brief, is on the market now underneath an open supply Apache 2.0 license, that means it’s free for any enterprise to take with out cost, obtain, modify, deploy and use of their industrial functions for workers or finish prospects with out paying Alibaba or anybody else a dime.
It’s additionally so extremely performant on third-party benchmarks and anecdotal utilization amongst AI energy customers for “vibe coding” — coding utilizing pure language and with out formal growth processes and steps — that no less than one, LLM researcher Sebastian Raschka, wrote on X that: “This may be the most effective coding mannequin but. Normal-purpose is cool, however if you’d like the most effective at coding, specialization wins. No free lunch.”
Builders and enterprises focused on downloading it may possibly discover the code on the AI code sharing repository Hugging Face.
Enterprises who don’t want to, or don’t have the capability to host the mannequin on their very own or via numerous third-party cloud inference suppliers, may also use it instantly via the Alibaba Cloud Qwen API, the place the per-million token prices begin at $1/$5 per million tokens (mTok) for enter/output of as much as 32,000 tokens, then $1.8/$9 for as much as 128,000, $3/$15 for as much as 256,000 and $6/$60 for the complete million.
Mannequin structure and capabilities
Based on the documentation launched by Qwen Crew on-line, Qwen3-Coder is a Combination-of-Consultants (MoE) mannequin with 480 billion whole parameters, 35 billion energetic per question, and eight energetic consultants out of 160.
It helps 256K token context lengths natively, with extrapolation as much as 1 million tokens utilizing YaRN (Yet one more RoPE extrapolatioN — a method used to increase a language mannequin’s context size past its unique coaching restrict by modifying the Rotary Positional Embeddings (RoPE) used throughout consideration computation. This capability allows the mannequin to know and manipulate whole repositories or prolonged paperwork in a single go.
Designed as a causal language mannequin, it options 62 layers, 96 consideration heads for queries, and eight for key-value pairs. It’s optimized for token-efficient, instruction-following duties and omits assist for
Excessive efficiency
Qwen3-Coder has achieved main efficiency amongst open fashions on a number of agentic analysis suites:
- SWE-bench Verified: 67.0% (customary), 69.6% (500-turn)
- GPT-4.1: 54.6%
- Gemini 2.5 Professional Preview: 49.0%
- Claude Sonnet-4: 70.4%
The mannequin additionally scores competitively throughout duties equivalent to agentic browser use, multi-language programming, and power use. Visible benchmarks present progressive enchancment throughout coaching iterations in classes like code era, SQL programming, code modifying, and instruction following.
Alongside the mannequin, Qwen has open-sourced Qwen Code, a CLI instrument forked from Gemini Code. This interface helps operate calling and structured prompting, making it simpler to combine Qwen3-Coder into coding workflows. Qwen Code helps Node.js environments and will be put in through npm or from supply.
Qwen3-Coder additionally integrates with developer platforms equivalent to:
- Claude Code (through DashScope proxy or router customization)
- Cline (as an OpenAI-compatible backend)
- Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers
Builders can run Qwen3-Coder domestically or join through OpenAI-compatible APIs utilizing endpoints hosted on Alibaba Cloud.
Put up-training methods: code RL and long-horizon planning
Along with pretraining on 7.5 trillion tokens (70% code), Qwen3-Coder advantages from superior post-training methods:
- Code RL (Reinforcement Studying): Emphasizes high-quality, execution-driven studying on various, verifiable code duties
- Lengthy-Horizon Agent RL: Trains the mannequin to plan, use instruments, and adapt over multi-turn interactions
This section simulates real-world software program engineering challenges. To allow it, Qwen constructed a 20,000-environment system on Alibaba Cloud, providing the dimensions needed for evaluating and coaching fashions on complicated workflows like these present in SWE-bench.
Enterprise implications: AI for engineering and DevOps workflows
For enterprises, Qwen3-Coder provides an open, extremely succesful different to closed-source proprietary fashions. With sturdy leads to coding execution and long-context reasoning, it’s particularly related for:
- Codebase-level understanding: Superb for AI techniques that should comprehend giant repositories, technical documentation, or architectural patterns
- Automated pull request workflows: Its capability to plan and adapt throughout turns makes it appropriate for auto-generating or reviewing pull requests
- Software integration and orchestration: By way of its native tool-calling APIs and performance interface, the mannequin will be embedded in inside tooling and CI/CD techniques. This makes it particularly viable for agentic workflows and merchandise, i.e., these the place the person triggers one or a number of duties that it needs the AI mannequin to go off and do autonomously, by itself, checking in solely when completed or when questions come up.
- Knowledge residency and price management: As an open mannequin, enterprises can deploy Qwen3-Coder on their very own infrastructure—whether or not cloud-native or on-prem—avoiding vendor lock-in and managing compute utilization extra instantly
Help for lengthy contexts and modular deployment choices throughout numerous dev environments makes Qwen3-Coder a candidate for production-grade AI pipelines in each giant tech firms and smaller engineering groups.
Developer entry and greatest practices
To make use of Qwen3-Coder optimally, Qwen recommends:
- Sampling settings: temperature=0.7, top_p=0.8, top_k=20, repetition_penalty=1.05
- Output size: As much as 65,536 tokens
- Transformers model: 4.51.0 or later (older variations might throw errors as a result of qwen3_moe incompatibility)
APIs and SDK examples are offered utilizing OpenAI-compatible Python shoppers.
Builders can outline customized instruments and let Qwen3-Coder dynamically invoke them throughout dialog or code era duties.
Heat early reception from AI energy customers
Preliminary responses to Qwen3-Coder-480B-A35B-Instruct have been notably constructive amongst AI researchers, engineers, and builders who’ve examined the mannequin in real-world coding workflows.
Along with Raschka’s lofty reward above, Wolfram Ravenwolf, an AI engineer and evaluator at EllamindAI, shared his expertise integrating the mannequin with Claude Code on X, stating, “That is certainly the most effective one at the moment.”
After testing a number of integration proxies, Ravenwolf mentioned he in the end constructed his personal utilizing LiteLLM to make sure optimum efficiency, demonstrating the mannequin’s attraction to hands-on practitioners targeted on toolchain customization.
Educator and AI tinkerer Kevin Nelson additionally weighed in on X after utilizing the mannequin for simulation duties.
“Qwen 3 Coder is on one other degree,” he posted, noting that the mannequin not solely executed on offered scaffolds however even embedded a message inside the output of the simulation — an sudden however welcome signal of the mannequin’s consciousness of job context.
Even Twitter co-founder and Sq. (now referred to as “Block”) founder Jack Dorsey posted an X message in reward of the mannequin, writing: “Goose + qwen3-coder = wow,” in reference to his Block’s open supply AI agent framework Goose, which VentureBeat lined again in January 2025.
These responses counsel Qwen3-Coder is resonating with a technically savvy person base searching for efficiency, adaptability, and deeper integration with current growth stacks.
Wanting forward: extra sizes, extra use instances
Whereas this launch focuses on probably the most highly effective variant, Qwen3-Coder-480B-A35B-Instruct, the Qwen group signifies that further mannequin sizes are in growth.
These will intention to supply related capabilities with decrease deployment prices, broadening accessibility.
Future work additionally contains exploring self-improvement, because the group investigates whether or not agentic fashions can iteratively refine their very own efficiency via real-world use.
