Chinese language e-commerce big Alibaba’s “Qwen Team” has executed it once more.
Mere days after releasing without cost and with open supply licensing what’s now the highest performing non-reasoning massive language mannequin (LLM) on this planet — full cease, even in comparison with proprietary AI fashions from well-funded U.S. labs comparable 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 helping with software program improvement. It’s designed to deal with advanced, multi-step coding workflows and may create full-fledged, purposeful purposes 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 accessible 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 in contrast to Claude and different proprietary fashions, Qwen3-Coder, which we’ll name it for brief, is offered now beneath 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 business purposes 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 improvement processes and steps — that at the very least one, LLM researcher Sebastian Raschka, wrote on X that: “This might be the best coding model yet. General-purpose is cool, but if you want the best at coding, specialization wins. No free lunch.”
Builders and enterprises eager about downloading it might 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 by means of varied third-party cloud inference suppliers, can even use it instantly by means of 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
In keeping with the documentation launched by Qwen Group on-line, Qwen3-Coder is a Combination-of-Consultants (MoE) mannequin with 480 billion complete parameters, 35 billion lively per question, and eight lively specialists 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 permits the mannequin to know and manipulate complete 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 blocks by default, streamlining its outputs.
Excessive efficiency
Qwen3-Coder has achieved main efficiency amongst open fashions on a number of agentic analysis suites:
SWE-bench Verified: 67.0% (normal), 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 comparable to agentic browser use, multi-language programming, and gear use. Visible benchmarks present progressive enchancment throughout coaching iterations in classes like code technology, 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 perform calling and structured prompting, making it simpler to combine Qwen3-Coder into coding workflows. Qwen Code helps Node.js environments and may be put in through npm or from supply.
Qwen3-Coder additionally integrates with developer platforms comparable 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.
Publish-training strategies: code RL and long-horizon planning
Along with pretraining on 7.5 trillion tokens (70% code), Qwen3-Coder advantages from superior post-training strategies:
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 crucial for evaluating and coaching fashions on advanced workflows like these present in SWE-bench.
Enterprise implications: AI for engineering and DevOps workflows
For enterprises, Qwen3-Coder provides an open, extremely succesful various to closed-source proprietary fashions. With sturdy ends in coding execution and long-context reasoning, it’s particularly related for:
Codebase-level understanding: Superb for AI methods that should comprehend massive repositories, technical documentation, or architectural patterns
Automated pull request workflows: Its potential to plan and adapt throughout turns makes it appropriate for auto-generating or reviewing pull requests
Software integration and orchestration: Via its native tool-calling APIs and performance interface, the mannequin may be embedded in inside tooling and CI/CD methods. This makes it particularly viable for agentic workflows and merchandise, i.e., these the place the consumer 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.
Information 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 varied dev environments makes Qwen3-Coder a candidate for production-grade AI pipelines in each massive tech corporations and smaller engineering groups.
Developer entry and finest 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 could throw errors attributable to qwen3_moe incompatibility)
APIs and SDK examples are offered utilizing OpenAI-compatible Python purchasers.
Builders can outline customized instruments and let Qwen3-Coder dynamically invoke them throughout dialog or code technology 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, “This is surely the best one currently.”
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 enchantment 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 another level,” he posted, noting that the mannequin not solely executed on offered scaffolds however even embedded a message throughout the output of the simulation — an sudden however welcome signal of the mannequin’s consciousness of process context.
Even Twitter co-founder and Sq. (now known 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 consumer base in search of efficiency, adaptability, and deeper integration with present improvement stacks.
Trying forward: extra sizes, extra use circumstances
Whereas this launch focuses on probably the most highly effective variant, Qwen3-Coder-480B-A35B-Instruct, the Qwen workforce signifies that further mannequin sizes are in improvement.
These will purpose to supply comparable capabilities with decrease deployment prices, broadening accessibility.
Future work additionally contains exploring self-improvement, because the workforce investigates whether or not agentic fashions can iteratively refine their very own efficiency by means of real-world use.
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