Ultra-lightweight open MoE coding model requiring only 3B active parameters per token, perfect for local IDE autocomplete and inline edits.
Technical Specifications
- Provider
- Alibaba
- Category
- open-source
- Release Date
- 2025-04
- Batch API Discount
- Standard rate
- Consumer Subscription
- Free / Self-Hosted
- Reasoning Effort Control
- Fixed
- Structured Outputs
- Yes
- Output Modalities
- text
- Official Documentation
- Visit Official Docs ↗
Data Privacy, Licensing & Deployment Governance
Official Provider Release Intelligence, API Quotas & Agentic Specs
- Official API Model ID (SDK String)
- qwen3-coder-30b-a3b-instruct
- Supported SDK & API Protocol
- OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
- Lifecycle & Deprecation Status
- Active GA (Guaranteed support through 2027+)
- Production Rate Limits (RPM & TPM)
- Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
- Long-Context Pricing Tier (>128K / >200K)
- Free across full context window (only KV-cache VRAM scales)
- Prompt Cache Write & TTL Policy
- Automatic prefix KV-caching in vLLM / SGLang (0 cost)
- Computer Use & Browser GUI Agent
- API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
- MCP & Parallel Tool Calling
- Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
- Web Search Grounding & Code Sandbox
- Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
- Fine-Tuning & Distillation Support
- Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
- Multimodal Tokenization Billing
- Text BPE (~4 chars/token)
- Weights Precision & Quantization
- BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0)
- Minimum GPU VRAM Requirement
- 19 GB VRAM (4-bit Quantized) · 65 GB VRAM (Native BF16)
- Supported Inference Engines
- vLLM, SGLang, Ollama, llama.cpp, TensorRT-LLM, HuggingFace TGI
- System Card & Safety Framework
- Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Qwen3-Coder 30B-A3B Instruct Cost, Context, Hardware & API Integration
Open-weights or free-tier model: $0 API license cost when self-hosted.
Same Monthly Workload Cost Across Popular Reference Models
Live Prompt Paste Tokenizer & Cost Counter
Paste real system instructions, JSON schemas, or code to measure BPE token counts across tokenizers.
Hybrid Prompt Routing Cascade Simulator
Route routine queries to a fast lightweight model ($0.15/1M) and escalate only complex reasoning turns to Qwen3-Coder 30B-A3B Instruct:
Benchmark Scores & 6-Axis Capability Radar
- MMLU-Pro
- 74%
- HumanEval
- 89.5%
- LiveCodeBench
- 63%
- Chatbot Arena Elo
- 1285pts
Strengths
- Astonishing inference throughput exceeding 140 tokens/sec on consumer GPUs
- High code generation precision despite negligible memory footprint
- Generous 262K context window despite its compact 3B active footprint
Limitations & Trade-offs
- Less proficient at deep abstract architectural planning across microservices
- Minor syntax edge cases in rare programming languages
Editorial Assessment
The ideal lightweight code assistant for developers seeking local, zero-latency privacy.
Best for: IDE plugins, fast inline copilot completions, unit test generation, and CI bug triage
Compare Qwen3-Coder 30B-A3B Instruct
Frequently Asked Questions about Qwen3-Coder 30B-A3B Instruct
How much does the Qwen3-Coder 30B-A3B Instruct API cost per 1M tokens?+
Qwen3-Coder 30B-A3B Instruct by Alibaba is priced at $0 per 1M input tokens and $0 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0 per 1M tokens, and asynchronous Batch API requests receive a 0% discount.
Should I use a $20/month subscription or the Qwen3-Coder 30B-A3B Instruct pay-as-you-go API?+
A flat $20/month consumer subscription is predictable for daily interactive chat and manual coding. However, if you are building software, running automated workflows, or sending low-to-moderate volume with prompt caching enabled, the pay-as-you-go API is typically far more cost-effective and offers higher rate limits without manual UI caps.
How many words and pages fit in Qwen3-Coder 30B-A3B Instruct's context window?+
Qwen3-Coder 30B-A3B Instruct features a 262K tokens context window, which equals approximately 196,608 English words or ~524 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 8,192 tokens.
Can I run Qwen3-Coder 30B-A3B Instruct locally on my own GPU (Open Weights)?+
Yes, Qwen3-Coder 30B-A3B Instruct (30B total (3B active MoE)) releases open weights that can be self-hosted locally or on private cloud GPUs using vLLM, Ollama, SGLang, or llama.cpp.
Does Alibaba train on data sent to the Qwen3-Coder 30B-A3B Instruct API, and is commercial use allowed?+
100% Isolated when self-hosted on private GPUs or VPC. License tier: Permissive Open-Weights (Apache 2.0 / MIT Commercial). Full commercial ownership of generated outputs. Supported deployment channels include: Self-Hosted GPU (vLLM / Ollama), Private VPC, or Managed Serverless API.
How does Qwen3-Coder 30B-A3B Instruct perform on coding and reasoning benchmarks?+
Qwen3-Coder 30B-A3B Instruct achieves a composite Intelligence Index of 76/100, scoring N/A% on SWE-bench Verified, N/A% on GPQA Diamond, 74% on MMLU-Pro, and 1285 pts on Chatbot Arena Elo.
What is Qwen3-Coder 30B-A3B Instruct best used for?+
Qwen3-Coder 30B-A3B Instruct is best suited for: IDE plugins, fast inline copilot completions, unit test generation, and CI bug triage. Assessment: The ideal lightweight code assistant for developers seeking local, zero-latency privacy.
Alibaba Family Lineage, Routing Tiers & Upgrade Path
Step down to a lighter sibling for high-volume routing or step up to Alibaba's flagship reasoning tier:
Qwen3.8 Max Preview
Qwen3.7 Plus
Qwen3.7 Max
Qwen3.6 Max Preview
Qwen3.6 Plus
Qwen3.6 Flash
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to Qwen3-Coder 30B-A3B Instruct:
The basic unit of text that a language model processes, typically representing about 3 to 4 English characters or roughly 0.75 words. For example, the sentence "Artificial intelligence is fast" breaks down into about 5 tokens.
A standardized evaluation suite used to objectively measure and rank an AI model's capabilities across specific tasks — such as SWE-bench Verified for software engineering, AIME for competitive math, or MMLU for general knowledge.
The maximum number of tokens a model can consider at once — both the input it reads and the output it writes. A 1,000,000-token context window can hold roughly 750,000 English words in a single request.
The ability of a language model to recognize when an external capability is needed and output structured JSON arguments to invoke external APIs, databases, or terminal commands. The runtime executes the tool and passes results back to the model.
An open standard created by Anthropic that allows AI applications and agents to connect seamlessly to local filesystems, development environments, and third-party APIs via standardized server interfaces.
An architecture where a large model is split into many specialist "expert" sub-networks and only a few activate per token. This keeps inference cheap while allowing a huge total parameter count — for example DeepSeek-V3 has 671B total parameters but activates only 37B per token.
Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
Sources: Qwen Coder Open Model Hub↗