Alibaba (Qwen) · language model
Qwen3 VL 235B A22B Instruct: pricing, context and benchmarks
Qwen3 VL 235B A22B Instruct is Alibaba (Qwen)'s model released on September 23, 2025. It costs $0.21 per million input tokens and $1.9 per million output tokens, with a 262.1K-token context window. No independent score has been published for it so far.
| Price | 16/30 | |
|---|---|---|
| Context | 9/15 | |
| Features | 12/15 |
What you need to know about Qwen3 VL 235B A22B Instruct
Alibaba (Qwen), based in China, put Qwen3 VL 235B A22B Instruct online on September 23, 2025. It accepts text and images 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 32,768 tokens in one go.
It has been available for about 13 months, long enough for its prices and scores to have settled.
Qwen3 VL 235B A22B Instruct pricing
On OpenRouter, Qwen3 VL 235B A22B Instruct costs $0.21 per million input tokens and $1.9 per million output tokens. Counting three tokens read for every token written, its average price is $0.633 per million, more expensive than 46 % of paid models (1.2 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 $0.0014. At 1,000 tasks a day, budget $41 a month. The price calculator redoes this with your own volumes.
Tokens already sent and cached are billed at $0.1 per million.
Performance and benchmarks
Epoch AI does not yet publish a capabilities index for Qwen3 VL 235B A22B Instruct. 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).
- Its knowledge stops at March 2025.
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: 62/100, 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.
Alternatives
- Seed-2.0-Lite$0.69
- Seed 1.6$0.69
- Mistral Large 3 2512$0.75
- Inkling Small$0.64
- GPT-5 Mini$0.69
- Nemotron 3 Super$0.83
Comparisons and tools
Learn more
Frequently asked questions
How much does Qwen3 VL 235B A22B Instruct cost?
$0.21 per million input tokens and $1.9 per million output tokens on OpenRouter (checked October 25, 2026). A task with 2,000 tokens read and 500 written costs $0.0014.
How big is Qwen3 VL 235B A22B Instruct's context window?
262,144 tokens, roughly 197,000 words, with a maximum of 32,768 output tokens.
Can Qwen3 VL 235B A22B Instruct run locally?
Yes, its weights are published.
Is Qwen3 VL 235B A22B Instruct 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 VL 235B A22B Instruct?
The closest models on price and performance are Seed-2.0-Lite, Seed 1.6, and Mistral Large 3 2512.
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