Alibaba (Qwen) · language model
Qwen3.5-122B-A10B: pricing, context and benchmarks
Qwen3.5-122B-A10B is Alibaba (Qwen)'s model released on February 25, 2026. It costs $0.4 per million input tokens and $3.2 per million output tokens, with a 262.1K-token context window. No independent score has been published for it so far.
| Price | 12/30 | |
|---|---|---|
| Context | 9/15 | |
| Features | 15/15 |
What you need to know about Qwen3.5-122B-A10B
Alibaba (Qwen), based in China, put Qwen3.5-122B-A10B online on February 25, 2026. It accepts text, images, and video 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 65,536 tokens in one go.
It has been available for about 8 months, long enough for its prices and scores to have settled.
Qwen3.5-122B-A10B pricing
On OpenRouter, Qwen3.5-122B-A10B costs $0.4 per million input tokens and $3.2 per million output tokens. Counting three tokens read for every token written, its average price is $1.1 per million, more expensive than 60 % of paid models (1.4 times 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 $0.0024. At 1,000 tasks a day, budget $72 a month. The price calculator redoes this with your own volumes.
Performance and benchmarks
Epoch AI does not yet publish a capabilities index for Qwen3.5-122B-A10B. 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 weights are open and downloadable from Hugging Face: you can run it on your own machines without sending your data to a third party.
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: 60/100, 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.
Avoid if…
- your tasks are mostly simple: a non-reasoning model will cost less.
Alternatives
- Kimi K2 Thinking$1.08
- Kimi K2 0905$1.08
- Kimi K2.5$1
- Nemotron 3 Ultra$1.05
- Seed 2.1 Turbo$1
- Seed-2.0-Code$1.13
Comparisons and tools
Learn more
Frequently asked questions
How much does Qwen3.5-122B-A10B cost?
$0.4 per million input tokens and $3.2 per million output tokens on OpenRouter (checked October 25, 2026). A task with 2,000 tokens read and 500 written costs $0.0024.
How big is Qwen3.5-122B-A10B's context window?
262,144 tokens, roughly 197,000 words, with a maximum of 65,536 output tokens.
Can Qwen3.5-122B-A10B run locally?
Yes, its weights are published.
Is Qwen3.5-122B-A10B 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 Qwen3.5-122B-A10B?
The closest models on price and performance are Kimi K2 Thinking, Kimi K2 0905, and Kimi K2.5.
Related articles
- Mistral AI: models, pricing and agentsOctober 25, 2026
- LLM benchmarks: how to read model rankingsOctober 24, 2026
- Running a local LLM: hardware, software, and limitationsOctober 24, 2026
- 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