Qwen3 Next 80B A3B Thinking
VerifiedOpen-weight LLM optimized for extended reasoning over long texts.
About Qwen3 Next 80B A3B Thinking
Its design centers on efficient handling of lengthy input sequences enabled by the large context capacity. Released as open weights, the model supports customization and local deployment by the community. The inclusion of 'Thinking' in its name indicates a focus on structured, step-by-step processing.
Key strengths lie in maintaining coherence across extensive documents and conversations. It processes text inputs exclusively without support for other modalities. This makes it suitable for scenarios where context preservation is critical.
Users typically apply it to research analysis, technical writing, and multi-turn dialogue systems. Developers integrate the model into tools requiring detailed textual comprehension and generation.
Capabilities
How Qwen3 Next 80B A3B Thinking compares
Qwen3 Next 80B A3B Thinking (striped bar) vs other language models on intelligence, speed and price.
Price
USD per 1M output tokens · Lower is better · Qwen3 Next 80B A3B Thinking ranks #44 of 98
Sources: Artificial Analysis (intelligence, speed) · OpenRouter (price).
Best for
Long Document Reasoning
The model excels at processing and reasoning over extensive inputs up to 262144 tokens, such as analyzing full research papers or lengthy legal documents with sustained coherence.
Mathematical Problem Solving
It performs well on complex math tasks by applying step-by-step chain-of-thought to break down equations, proofs, and quantitative problems accurately.
Code Generation and Analysis
The model handles code-related work including writing, debugging, and reviewing large codebases while following detailed technical instructions.
Strengths & limitations
Strengths
- +Strong reasoning via thinking-focused design
- +Effective 256k context utilization
- +Competitive coding and math performance
- +Efficient MoE-style inference
Limitations
- –Text-only modality
- –High compute requirements for full model
- –May over-reason on simple tasks
Cost calculator
Estimate what Qwen3 Next 80B A3B Thinking would cost for your usage.
Based on Qwen3 Next 80B A3B Thinking's $0.10/1M input · $0.78/1M output. Estimate only — actual cost varies by provider and caching.
Download & self-host Qwen3 Next 80B A3B Thinking
This is an open-weight model. Download the weights from Hugging Face or load it directly with Transformers.
# Install the Hugging Face CLI
pip install -U "huggingface_hub[cli]"
# Download the model weights
hf download Qwen/Qwen3-Next-80B-A3B-Thinking
# Or load it directly in Python
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-Next-80B-A3B-Thinking")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-Next-80B-A3B-Thinking", device_map="auto")Inference providers
Hosted APIs that serve Qwen3 Next 80B A3B Thinking (via Hugging Face Inference Providers).
Quick start
OpenRouter's API is OpenAI-compatible — most SDKs work by just swapping the base URL. Only the model slug changes between models.
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://openrouter.ai/api/v1",
apiKey: process.env.OPENROUTER_API_KEY,
});
const completion = await client.chat.completions.create({
model: "qwen/qwen3-next-80b-a3b-thinking",
messages: [{ role: "user", content: "Hello!" }],
});
console.log(completion.choices[0].message.content);Model slug: qwen/qwen3-next-80b-a3b-thinking
Editor's verdict
Qwen3 Next 80B A3B Thinking is Alibaba Qwen's open-weight language models with a 262K-token context window.
At $0.78 per 1M output tokens, it is very cost-efficient for its class.
As an open-weight model you can self-host it (81B parameters) or call it through a hosted API.
Best suited to strong reasoning via thinking-focused design and effective 256k context utilization.
Frequently asked questions
The model provides a context window of 262144 tokens.
User reviews
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