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Chinese language e-commerce large Alibaba’s “Qwen Crew” has performed 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 resembling 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 centered on helping with software program growth. It’s designed to deal with complicated, multi-step coding workflows and may create full-fledged, practical 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 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 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 growth processes and steps — that not less than one, LLM researcher Sebastian Raschka, wrote on X that: “This is perhaps one of the best coding mannequin but. Basic-purpose is cool, however in order for you one of the best at coding, specialization wins. No free lunch.”
Builders and enterprises interested by downloading it could 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 by way of varied third-party cloud inference suppliers, may use it straight by way 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
Based on the documentation launched by Qwen Crew on-line, Qwen3-Coder is a Combination-of-Specialists (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 (One more RoPE extrapolatioN — a way used to increase a language mannequin’s context size past its authentic coaching restrict by modifying the Rotary Positional Embeddings (RoPE) used throughout consideration computation. This capability allows the mannequin to know and manipulate complete repositories or prolonged paperwork in a single move.
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 help 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 resembling agentic browser use, multi-language programming, and gear use. Visible benchmarks present progressive enchancment throughout coaching iterations in classes like code era, SQL programming, code enhancing, 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 may be put in through npm or from supply.
Qwen3-Coder additionally integrates with developer platforms resembling:
- 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 regionally or join through OpenAI-compatible APIs utilizing endpoints hosted on Alibaba Cloud.
Put up-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 numerous, 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 size essential 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 gives an open, extremely succesful different to closed-source proprietary fashions. With sturdy ends in coding execution and long-context reasoning, it’s particularly related for:
- Codebase-level understanding: Preferrred for AI programs that should comprehend giant 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
- Instrument integration and orchestration: By way of its native tool-calling APIs and performance interface, the mannequin may be embedded in inside tooling and CI/CD programs. 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 desires 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 straight
Help for lengthy contexts and modular deployment choices throughout varied dev environments makes Qwen3-Coder a candidate for production-grade AI pipelines in each giant 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 because of qwen3_moe incompatibility)
APIs and SDK examples are supplied utilizing OpenAI-compatible Python purchasers.
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 absolutely one of the best one at present.”
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 centered 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 supplied scaffolds however even embedded a message inside 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 person base looking for efficiency, adaptability, and deeper integration with present growth stacks.
Trying 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 extra mannequin sizes are in growth.
These will intention to supply comparable capabilities with decrease deployment prices, broadening accessibility.
Future work additionally consists of exploring self-improvement, because the group investigates whether or not agentic fashions can iteratively refine their very own efficiency by way of real-world use.
