Qwen: Qwen3 Coder 480B A35B

Qwen3 Coder 480B A35B is a text generation model from the Qwen team, released on 23 July 2025. It is a Mixture-of-Experts (MoE) model built specifically for code generation, and the name reflects its architecture: 480 billion total parameters with roughly 35 billion active per forward pass. The provider describes it as an instruct model optimized for agentic coding tasks, including function calling, tool use, and long-context reasoning. Its knowledge cutoff is 30 June 2025.

The model accepts text input and returns text output, so it works with prompts, source files, diffs, logs, and documentation rather than images or audio. Its context window is 262,144 tokens, which is large enough to hold entire repositories, long conversation histories, or extensive tool transcripts in a single request. The maximum output length is 65,536 tokens, so it can produce substantial code files or multi-step plans in one response. Because it is tuned for coding, its strongest behavior appears on programming and tool-driven tasks rather than open-ended prose.

Those capabilities point to a fairly specific set of use cases. Agentic coding is the primary one: the model can decide when to call a function, emit structured tool arguments, and continue reasoning across many turns of a task. Long-context reasoning over a large codebase suits refactoring, migration work, and answering questions that require reading many files at once. Structured outputs and response formats make it usable for generating patches, configuration files, or machine-readable plans. Tool calling also makes it a reasonable fit for assistants that need to query APIs, run commands, or chain steps together.

You call it through api14's OpenAI-compatible API using the model id "qwen/qwen3-coder", so existing OpenAI SDKs and clients work with only the base URL and key changed. The request parameters it supports are frequency_penalty, logit_bias, logprobs, max_tokens, min_p, presence_penalty, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_logprobs, and top_p. Tool use and structured outputs are both available, which is what makes the agentic workflows above practical to implement.

One limit worth noting is modality: input and output are text only, so tasks that depend on images or audio need a different model. The 65,536 token output cap also means very large generated artifacts may need to be produced in several passes. For general conversation or non-coding writing, a model tuned for those tasks may fit better, while Qwen3 Coder is aimed at code and tool-driven work.