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Visit our Hugging Face or ModelScope organization (click links above), search checkpoints with names starting with Qwen3-Coder-, and you will find all you need! Enjoy!
Today, we're announcing Qwen3-Coder, our most agentic code model to date. Qwen3-Coder is available in multiple sizes, but we're excited to introduce its most powerful variant first: Qwen3-Coder-480B-A35B-Instruct — a 480B-parameter Mixture-of-Experts model with 35B active parameters, offering exceptional performance in both coding and agentic tasks. Qwen3-Coder-480B-A35B-Instruct sets new state-of-the-art results among open models on Agentic Coding, Agentic Browser-Use, and Agentic Tool-Use, comparable to Claude Sonnet.
💻 Significant Performance: among open models on Agentic Coding, Agentic Browser-Use, and other foundational coding tasks, achieving results comparable to Claude Sonnet;
📚 Long-context Capabilities: with native support for 256K tokens, extendable up to 1M tokens using Yarn, optimized for repository-scale understanding;
🛠 Agentic Coding: supporting for most platfrom such as Qwen Code, CLINE, featuring a specially designed function call format;
- ✨ Supporting long context understanding and generation with the context length of 256K tokens;
- ✨ Supporting 358 coding languages;
['ABAP', 'ActionScript', 'Ada', 'Agda', 'Alloy', 'ApacheConf', 'AppleScript', 'Arc', 'Arduino', 'AsciiDoc', 'AspectJ', 'Assembly', 'Augeas', 'AutoHotkey', 'AutoIt', 'Awk', 'Batchfile', 'Befunge', 'Bison', 'BitBake', 'BlitzBasic', 'BlitzMax', 'Bluespec', 'Boo', 'Brainfuck', 'Brightscript', 'Bro', 'C', 'C#', 'C++', 'C2hs Haskell', 'CLIPS', 'CMake', 'COBOL', 'CSS', 'CSV', "Cap'n Proto", 'CartoCSS', 'Ceylon', 'Chapel', 'ChucK', 'Cirru', 'Clarion', 'Clean', 'Click', 'Clojure', 'CoffeeScript', 'ColdFusion', 'ColdFusion CFC', 'Common Lisp', 'Component Pascal', 'Coq', 'Creole', 'Crystal', 'Csound', 'Cucumber', 'Cuda', 'Cycript', 'Cython', 'D', 'DIGITAL Command Language', 'DM', 'DNS Zone', 'Darcs Patch', 'Dart', 'Diff', 'Dockerfile', 'Dogescript', 'Dylan', 'E', 'ECL', 'Eagle', 'Ecere Projects', 'Eiffel', 'Elixir', 'Elm', 'Emacs Lisp', 'EmberScript', 'Erlang', 'F#', 'FLUX', 'FORTRAN', 'Factor', 'Fancy', 'Fantom', 'Forth', 'FreeMarker', 'G-code', 'GAMS', 'GAP', 'GAS', 'GDScript', 'GLSL', 'Genshi', 'Gentoo Ebuild', 'Gentoo Eclass', 'Gettext Catalog', 'Glyph', 'Gnuplot', 'Go', 'Golo', 'Gosu', 'Grace', 'Gradle', 'Grammatical Framework', 'GraphQL', 'Graphviz (DOT)', 'Groff', 'Groovy', 'Groovy Server Pages', 'HCL', 'HLSL', 'HTML', 'HTML+Django', 'HTML+EEX', 'HTML+ERB', 'HTML+PHP', 'HTTP', 'Haml', 'Handlebars', 'Harbour', 'Haskell', 'Haxe', 'Hy', 'IDL', 'IGOR Pro', 'INI', 'IRC log', 'Idris', 'Inform 7', 'Inno Setup', 'Io', 'Ioke', 'Isabelle', 'J', 'JFlex', 'JSON', 'JSON5', 'JSONLD', 'JSONiq', 'JSX', 'Jade', 'Jasmin', 'Java', 'Java Server Pages', 'JavaScript', 'Julia', 'Jupyter Notebook', 'KRL', 'KiCad', 'Kit', 'Kotlin', 'LFE', 'LLVM', 'LOLCODE', 'LSL', 'LabVIEW', 'Lasso', 'Latte', 'Lean', 'Less', 'Lex', 'LilyPond', 'Linker Script', 'Liquid', 'Literate Agda', 'Literate CoffeeScript', 'Literate Haskell', 'LiveScript', 'Logos', 'Logtalk', 'LookML', 'Lua', 'M', 'M4', 'MAXScript', 'MTML', 'MUF', 'Makefile', 'Mako', 'Maple', 'Markdown', 'Mask', 'Mathematica', 'Matlab', 'Max', 'MediaWiki', 'Metal', 'MiniD', 'Mirah', 'Modelica', 'Module Management System', 'Monkey', 'MoonScript', 'Myghty', 'NSIS', 'NetLinx', 'NetLogo', 'Nginx', 'Nimrod', 'Ninja', 'Nit', 'Nix', 'Nu', 'NumPy', 'OCaml', 'ObjDump', 'Objective-C++', 'Objective-J', 'Octave', 'Omgrofl', 'Opa', 'Opal', 'OpenCL', 'OpenEdge ABL', 'OpenSCAD', 'Org', 'Ox', 'Oxygene', 'Oz', 'PAWN', 'PHP', 'POV-Ray SDL', 'Pan', 'Papyrus', 'Parrot', 'Parrot Assembly', 'Parrot Internal Representation', 'Pascal', 'Perl', 'Perl6', 'Pickle', 'PigLatin', 'Pike', 'Pod', 'PogoScript', 'Pony', 'PostScript', 'PowerShell', 'Processing', 'Prolog', 'Propeller Spin', 'Protocol Buffer', 'Public Key', 'Pure Data', 'PureBasic', 'PureScript', 'Python', 'Python traceback', 'QML', 'QMake', 'R', 'RAML', 'RDoc', 'REALbasic', 'RHTML', 'RMarkdown', 'Racket', 'Ragel in Ruby Host', 'Raw token data', 'Rebol', 'Red', 'Redcode', "Ren'Py", 'RenderScript', 'RobotFramework', 'Rouge', 'Ruby', 'Rust', 'SAS', 'SCSS', 'SMT', 'SPARQL', 'SQF', 'SQL', 'STON', 'SVG', 'Sage', 'SaltStack', 'Sass', 'Scala', 'Scaml', 'Scheme', 'Scilab', 'Self', 'Shell', 'ShellSession', 'Shen', 'Slash', 'Slim', 'Smali', 'Smalltalk', 'Smarty', 'Solidity', 'SourcePawn', 'Squirrel', 'Stan', 'Standard ML', 'Stata', 'Stylus', 'SuperCollider', 'Swift', 'SystemVerilog', 'TOML', 'TXL', 'Tcl', 'Tcsh', 'TeX', 'Tea', 'Text', 'Textile', 'Thrift', 'Turing', 'Turtle', 'Twig', 'TypeScript', 'Unified Parallel C', 'Unity3D Asset', 'Uno', 'UnrealScript', 'UrWeb', 'VCL', 'VHDL', 'Vala', 'Verilog', 'VimL', 'Visual Basic', 'Volt', 'Vue', 'Web Ontology Language', 'WebAssembly', 'WebIDL', 'X10', 'XC', 'XML', 'XPages', 'XProc', 'XQuery', 'XS', 'XSLT', 'Xojo', 'Xtend', 'YAML', 'YANG', 'Yacc', 'Zephir', 'Zig', 'Zimpl', 'desktop', 'eC', 'edn', 'fish', 'mupad', 'nesC', 'ooc', 'reStructuredText', 'wisp', 'xBase']
- ✨ Retain strengths in math and general capabilities from base model.
Important
Qwen3-coder function calling relies on our new tool parser qwen3coder_tool_parser.py here.
We updated both the special tokens and their corresponding token ids, in order to maintain consistency with Qwen3. Please make sure to use the new tokenizer.
| model name | type | length | Download |
|---|---|---|---|
| Qwen3-Coder-480B-A35B-Instruct | instruct | 256k | 🤗 Hugging Face • 🤖 ModelScope |
| Qwen3-Coder-480B-A35B-Instruct-FP8 | instruct | 256k | 🤗 Hugging Face • 🤖 ModelScope |
Detailed performance and introduction are shown in this 📑 blog.
Important
Qwen3-Coder-480B-A35B-Instruct are instruction models for chatting;
This model supports only non-thinking mode and does not generate <think></think> blocks in its output. Meanwhile, specifying enable_thinking=False is no longer required.**
You can just write several lines of code with transformers to chat with Qwen3-Coder-480B-A35B-Instruct. Essentially, we build the tokenizer and the model with from_pretrained method, and we use generate method to perform chatting with the help of chat template provided by the tokenizer. Below is an example of how to chat with Qwen3-Coder-480B-A35B-Instruct:
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen3-Coder-480B-A35B-Instruct"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "write a quick sort algorithm."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=65536
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]The apply_chat_template() function is used to convert the messages into a format that the model can understand.
The add_generation_prompt argument is used to add a generation prompt, which refers to <|im_start|>assistant\n to the input. Notably, we apply ChatML template for chat models following our previous practice.
The max_new_tokens argument is used to set the maximum length of the response. The tokenizer.batch_decode() function is used to decode the response. In terms of the input, the above messages is an example to show how to format your dialog history and system prompt.
You can use the other size of instruct model in the same way.
The code insertion task, also referred to as the "fill-in-the-middle" challenge, requires the insertion of code segments in a manner that bridges the gaps within a given code context. For an approach aligned with best practices, we recommend adhering to the formatting guidelines outlined in the paper "Efficient Training of Language Models to Fill in the Middle"[arxiv].
The prompt should be structured as follows:
prompt = '<|fim_prefix|>' + prefix_code + '<|fim_suffix|>' + suffix_code + '<|fim_middle|>'Following the approach mentioned, an example would be structured in this manner:
from transformers import AutoTokenizer, AutoModelForCausalLM
# load model
device = "cuda" # the device to load the model onto
TOKENIZER = AutoTokenizer.from_pretrained("Qwen/Qwen3-Coder-480B-A35B-Instruct")
MODEL = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-Coder-480B-A35B-Instruct", device_map="auto").eval()
input_text = """<|fim_prefix|>def quicksort(arr):
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
<|fim_suffix|>
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quicksort(left) + middle + quicksort(right)<|fim_middle|>"""
messages = [
{"role": "system", "content": "You are a code completion assistant."},
{"role": "user", "content": input_text}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = TOKENIZER([text], return_tensors="pt").to(model.device)
# Use `max_new_tokens` to control the maximum output length.
generated_ids = MODEL.generate(model_inputs.input_ids, max_new_tokens=512, do_sample=False)[0]
# The generated_ids include prompt_ids, we only need to decode the tokens after prompt_ids.
output_text = TOKENIZER.decode(generated_ids[len(model_inputs.input_ids[0]):], skip_special_tokens=True)
print(f"Prompt: {input_text}\n\nGenerated text: {output_text}")If you find our work helpful, feel free to give us a cite.
@misc{qwen3technicalreport,
title={Qwen3 Technical Report},
author={Qwen Team},
year={2025},
eprint={2505.09388},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.09388},
}
@article{hui2024qwen2,
title={Qwen2. 5-Coder Technical Report},
author={Hui, Binyuan and Yang, Jian and Cui, Zeyu and Yang, Jiaxi and Liu, Dayiheng and Zhang, Lei and Liu, Tianyu and Zhang, Jiajun and Yu, Bowen and Dang, Kai and others},
journal={arXiv preprint arXiv:2409.12186},
year={2024}
}If you are interested to leave a message to either our research team or product team, join our Discord or WeChat groups!