Alibaba (Qwen) · language model
Qwen3 Next 80B A3B Thinking: pricing, context and benchmarks
Qwen3 Next 80B A3B Thinking is Alibaba (Qwen)'s model released on September 11, 2025. It costs $0.15 per million input tokens and $1.2 per million output tokens, with a 262.1K-token context window. No independent score has been published for it so far.
What you need to know about Qwen3 Next 80B A3B Thinking
Alibaba (Qwen), based in China, put Qwen3 Next 80B A3B Thinking online on September 11, 2025. It accepts text as input and produces text. Its context window reaches 262,144 tokens, larger than 58 % of the models in the registry, and it can write up to 235,929 tokens in one go.
It has been available for about 13 months, long enough for its prices and scores to have settled.
Qwen3 Next 80B A3B Thinking pricing
On OpenRouter, Qwen3 Next 80B A3B Thinking costs $0.15 per million input tokens and $1.2 per million output tokens. Counting three tokens read for every token written, its average price is $0.413 per million, more expensive than 35 % of paid models (1.8 times cheaper than the median of $0.76).
A concrete example: a task that sends 2,000 tokens (about 1,500 words) and receives a 500-token answer costs $0.0009. At 1,000 tasks a day, budget $27 a month. The price calculator redoes this with your own volumes.
Performance and benchmarks
Epoch AI does not yet publish a capabilities index for Qwen3 Next 80B A3B Thinking. We therefore show no made-up performance rating: only price, context and features count towards its score.
Features, privacy and local use
- It supports tool calling, so it can drive an AI agent, call APIs or MCP servers.
- It can answer in an enforced JSON format (structured outputs).
- It is a reasoning model: it "thinks" before answering, which helps on hard tasks but uses more output tokens.
- Its knowledge stops at September 2025.
Its weights are open and downloadable from Hugging Face (apache-2.0 licence): you can run it on your own machines without sending your data to a third party. With 81,324,862,720 billion parameters, it needs about 45,443,808,317 GB of memory at 4-bit quantisation. Check with the VRAM calculator. It was downloaded 39,598 times on Hugging Face last month.
Other Alibaba (Qwen) models
| Model | Released | Avg. price | ECI |
|---|---|---|---|
| Qwen3.8 Max Prime | Sep 23, 2026 | $6 | — |
| Qwen3.8 Omni Flash | Sep 21, 2026 | $0.23 | — |
| Qwen3.8 Max (0902) | Sep 3, 2026 | $3 | 155.1 |
| Qwen3.8 Flash | Aug 26, 2026 | $0.23 | — |
| Qwen3.8 27B | Aug 14, 2026 | $0.78 | 151.7 |
| Qwen3.8 2.4T A95B | Aug 12, 2026 | $3 | — |
| Qwen3.7 Flash | Jul 27, 2026 | $0.06 | 144.1 |
| Qwen3.7 Plus | Jun 3, 2026 | $0.56 | 147 |
Verdict
Computed score: 73/100, from public data (performance, price, context, features).
Strengths
- Open weights: self-hostable, your data stays with you.
- Agent-ready: tool calling and structured JSON output.
Limitations
- No independent score published: performance cannot be verified.
- Text only: reads neither images nor files.
Avoid if…
- you want to host it on a single consumer computer (≈ 45,443,808,317 GB needed at 4-bit).
Alternatives
- Step 3.7 Flash$0.44
- Trinity Large Thinking$0.39
- GPT-5.4 Nano$0.46
- Hy3 preview$0.29
- Mistral Small 4$0.26
- Llama 4 Maverick$0.3
Comparisons and tools
Learn more
Frequently asked questions
How much does Qwen3 Next 80B A3B Thinking cost?
$0.15 per million input tokens and $1.2 per million output tokens on OpenRouter (checked October 25, 2026). A task with 2,000 tokens read and 500 written costs $0.0009.
How big is Qwen3 Next 80B A3B Thinking's context window?
262,144 tokens, roughly 197,000 words, with a maximum of 235,929 output tokens.
Can Qwen3 Next 80B A3B Thinking run locally?
Yes, its weights are published under the apache-2.0 licence. Expect about 45,443,808,317 GB of memory at 4-bit.
Is Qwen3 Next 80B A3B Thinking suitable for an AI agent?
Technically yes: it supports tool calling and structured outputs. Then check its cost for your volume.
What are the alternatives to Qwen3 Next 80B A3B Thinking?
The closest models on price and performance are Step 3.7 Flash, Trinity Large Thinking, and GPT-5.4 Nano.
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- LLM benchmarks: how to read model rankingsOctober 24, 2026
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- Open-source or closed models for your AI agents?October 5, 2026
Neutrality. No affiliate links. No vendor pays to be reviewed, rated or ranked. Reviews are directed by François Chollet, neutral and factual, and every data point is dated and sourced. How we score