High-efficiency open reasoning MoE model that unleashes deep chain-of-thought exploration while utilizing only 3B active parameters per token.
Technical Specifications
- Provider
- Alibaba
- Category
- open-source
- Release Date
- 2025-09
- Batch API Discount
- Standard rate
- Consumer Subscription
- Free / Self-Hosted
- Reasoning Effort Control
- Configurable (Low / Med / High)
- 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-next-80b-a3b-thinking-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
- 50 GB VRAM (4-bit Quantized) · 172 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-Next 80B-A3B (Thinking) 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-Next 80B-A3B (Thinking):
Benchmark Scores & 6-Axis Capability Radar
- SWE-bench Verified
- 46%
- GPQA Diamond
- 68.5%
- MMLU-Pro
- 80%
- HumanEval
- 92.5%
- AIME
- 82%
- Chatbot Arena Elo
- 1345pts
Strengths
- Remarkable STEM and coding reasoning performance from just 3B active parameters
- High throughput compared to traditional dense 70B+ reasoners
- Open weights with permissive commercial license
Limitations & Trade-offs
- Extended reasoning traces increase generation latency and memory cache usage
- Can overthink trivial conversational queries
Editorial Assessment
A masterclass in sparse MoE reasoning efficiency, packing frontier logic into lightweight inference.
Best for: Local hardware reasoning, scientific problem-solving, and on-premises math tutoring
Compare Qwen3-Next 80B-A3B (Thinking)
Frequently Asked Questions about Qwen3-Next 80B-A3B (Thinking)
How much does the Qwen3-Next 80B-A3B (Thinking) API cost per 1M tokens?+
Qwen3-Next 80B-A3B (Thinking) 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-Next 80B-A3B (Thinking) 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-Next 80B-A3B (Thinking)'s context window?+
Qwen3-Next 80B-A3B (Thinking) features a 131K tokens context window, which equals approximately 98,304 English words or ~262 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 16,384 tokens.
Can I run Qwen3-Next 80B-A3B (Thinking) locally on my own GPU (Open Weights)?+
Yes, Qwen3-Next 80B-A3B (Thinking) (80B 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-Next 80B-A3B (Thinking) 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-Next 80B-A3B (Thinking) perform on coding and reasoning benchmarks?+
Qwen3-Next 80B-A3B (Thinking) achieves a composite Intelligence Index of 82/100, scoring 46% on SWE-bench Verified, 68.5% on GPQA Diamond, 80% on MMLU-Pro, and 1345 pts on Chatbot Arena Elo.
What is Qwen3-Next 80B-A3B (Thinking) best used for?+
Qwen3-Next 80B-A3B (Thinking) is best suited for: Local hardware reasoning, scientific problem-solving, and on-premises math tutoring. Assessment: A masterclass in sparse MoE reasoning efficiency, packing frontier logic into lightweight inference.
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-Next 80B-A3B (Thinking):
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.
An AI model trained via reinforcement learning to spend dynamic compute thinking through complex logic, math, and architectural problems before outputting a solution. Examples include OpenAI o1, o3, and DeepSeek R1.
A step-by-step reasoning technique where an AI model generates intermediate problem-solving thoughts before arriving at a final answer. Native reasoning models like OpenAI o3 and DeepSeek R1 use CoT to solve complex math and coding challenges.
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.
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.