Qwen 3 Coder 480B Turbo
Alibaba's flagship open-weight code model — a 480B-parameter MoE with 35B active, built for agentic coding and 256K-context repo work.
Overview
What is Qwen 3 Coder 480B Turbo
Qwen 3 Coder 480B Turbo is an open-weight, code-optimized Mixture-of-Experts model from Alibaba's Qwen team. It runs 480B total parameters with 35B active per inference, supports 256K native context, and is designed for agentic coding, function calling, and repository-scale development tasks.
Running it privately on Venice
On Venice you interact with Qwen 3 Coder 480B Turbo under a private, zero-retention tier — your prompts are not stored or used for training. The FP8-quantized Turbo build is tuned for fast inference and carries Venice's 'Default for code' trait, with full support for tool use, web search, and structured JSON output. Because the weights are open under Apache 2.0, you retain sovereignty to audit or self-host the model outside Venice if needed.
Assessment
Strengths and limitations
- Open weights under Apache 2.0: auditable, forkable, and self-hostable outside Venice.
- Massive MoE scale (480B total / 35B active) delivers frontier coding performance without dense-model inference costs.
- Native 256K context window, extendable toward 1M via extrapolation, built for repo-scale understanding.
- Agentic-ready: native function calling, web search, structured JSON output, and log-probability support.
- Default code model on Venice, optimized for speed in an FP8-quantized Turbo build.
- Non-thinking instruct mode skips reasoning-trace blocks for fast, deterministic code generation.
- Extremely large model: self-hosting requires substantial GPU infrastructure even with MoE sparsity.
- FP8 quantization on Venice trades marginal precision for inference speed versus full-precision builds.
- Not uncensored: safety alignment may refuse certain prompts, unlike fully uncensored alternatives.
- Tool-calling format requires an updated tokenizer (transformers ≥ 4.51) for reliable parsing.
- Turbo variant prioritizes latency; maximum numerical precision may require non-quantized endpoints elsewhere.
Capabilities
What it supports
- Tool use / function calling
- Vision (image input)
- Reasoning
- Web search
- Code-optimized
- Structured output (JSON schema)
- Audio input
- Video input
- Multiple image inputs
- Log probabilities
Specifications
Datasheet
- Maker
- Alibaba Cloud (Qwen team)
- Released
- July 22, 2025
- Architecture
- Mixture-of-Experts (MoE), 160 experts / 8 active, GQA
- Parameters
- 480B total · 35B active
- Open weights
- Yes — Apache 2.0
- Context window
- 256K tokens
- Max output
- 65.536K tokens
- Capabilities
- Function calling, Web search, Code-optimized
- Privacy on Venice
- Private — zero retention
- Available on Venice since
- Jan 2026
API
Call it from your code
Venice exposes an OpenAI-compatible API. Point your base URL at Venice and pass the model id.
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen3-coder-480b-a35b-instruct-turbo",
"messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
}'Pricing
What it costs on Venice
Billed per token on Venice: $0.35 per 1M input tokens and $1.50 per 1M output tokens.
New Venice accounts include a free daily allowance and 500 welcome credits — no credit card required.
Alternatives
How it compares
| Model | Context window | Open weights | Price (Venice) | Best for |
|---|---|---|---|---|
| Qwen 3 Coder 480B Turbo | 256K tokens | Yes | $0.35 in · $1.50 out / 1M | Agentic coding & repo-scale work |
| DeepSeek V3.2 | 160K tokens | Yes | $0.33 in · $0.48 out / 1M | General reasoning & coding |
| Claude Sonnet 4.6 | 1M tokens | No | $3.60 in · $18 out / 1M | High-end closed coding |
| Kimi K2.6 | 256K tokens | Yes | $0.75 in · $3.50 out / 1M | Long-context reasoning |
The flagship open-weight coder on Venice — MoE scale with tool use and web search.
Use cases
What it is good for
- 01Repository-scale code review, refactoring, and architecture analysis across hundreds of files.
- 02Agentic coding with Qwen Code, CLINE, or custom toolchains via function calling and web search.
- 03Automated documentation generation grounded in live web data.
- 04Structured log parsing, config extraction, and API response shaping via JSON schema output.
- 05Long-context fill-in-the-middle tasks and multi-file edit planning in large codebases.
Prompting
Getting better results
Use transformers ≥ 4.51 to avoid 'qwen3_moe' tokenizer errors.
Feed entire files or repo slices into the 256K context rather than chunking — the model scales well to long inputs.
Define tool schemas explicitly for agentic workflows; it follows purpose-built function-call formats.
Request structured JSON output when you need machine-parseable results from code analysis.
FAQ
Frequently asked questions
It is an open-weight, code-optimized Mixture-of-Experts model from Alibaba's Qwen team. It has 480B total parameters with 35B active per inference, a 256K native context window, and is built for agentic coding, function calling, and large-scale software development.
On Venice it costs $0.35 per 1M input tokens and $1.50 per 1M output tokens. Cached input is billed at $0.04 per 1M tokens. You pay per token with no subscription required.
Yes. The model weights are released under the Apache 2.0 license on Hugging Face, so you can audit, modify, or self-host them. The Turbo FP8 build is served on Venice for fast inference.
Yes. It supports function calling, web search, structured JSON output, and log probabilities on Venice, making it suitable for agentic coding workflows and automated toolchains.
Qwen 3 Coder is open-weight, far cheaper per token, and purpose-built for coding with strong tool use. Claude Sonnet 4.6 offers a 1M context and polished closed-ecosystem integration at a premium price. Choose Qwen for openness and cost; choose Sonnet if you need maximum context and closed-model polish.
No. It carries standard safety alignment and may refuse certain requests. It is not marketed as an uncensored model, though its Apache 2.0 weights allow downstream fine-tuning if you need different alignment.
Yes. Venice runs it under a private, zero-retention tier — your prompts are not stored, profiled, or used for training. There is no personal account history tied to your generations.
It natively supports 256K tokens and can be extended up to 1M tokens via extrapolation methods such as YaRN, making it suitable for repository-scale code understanding.
Run Qwen 3 Coder 480B Turbo privately
No prompt logging. No data used for training.