# Qwen 3 VL 235B a22b Instruct API

Alibaba's Qwen 3 VL 235B a22b Instruct, through an OpenAI-compatible API. Providers quote it on an open exchange, and each request goes to the offer with the lowest estimated charge that meets your rules.

- Providers compete for each request. The exchange ranks the offers that meet your rules by estimated charge. It fixes your maximum charge before the first token, and you pay for the tokens that the model uses.
- The OpenAI and Anthropic APIs. Use the OpenAI or Anthropic SDK that you have. Set the base URL and your key, and send qwen3-vl-235b-a22b-instruct as the model.
- Your data rules. Send x-liquid-retention: none and only offers whose provider does not keep your content compete for the request.

## Facts

- Model name: qwen3-vl-235b-a22b-instruct
- Maker: Alibaba
- Context window: 256K tokens
- Input price: $0.250 per million tokens
- Output price: $1.100 per million tokens
- Providers: 1 independent provider
- Regions: us

## Providers

- [Pera](https://liquidinference.ai/providers/f651491e-f85b-4f03-b80c-32876aeb80ce.md): us, $0.250 input and $1.100 output per million tokens, $0.250 cache read per million tokens, 256K tokens of context

## Scores

- HLE: 6.6, #186 of 273
- GPQA: 71.2, #149 of 250
- AA-LCR: 32.7, #191 of 237
- Terminal-Bench Hard: 6.8, #131 of 166
- LiveCodeBench: 59.4, #44 of 130
- AIME 2025: 70.7, #35 of 100
- MMLU-Pro: 82.3, #33 of 133
- τ²-Bench: 35.1, #123 of 169
- IFBench: 42.7, #107 of 178
- SimpleQA Verified: 40.4, #54 of 126

Scores: Artificial Analysis (https://artificialanalysis.ai/)

Scores: Epoch AI, 'Capabilities & Benchmarking' (https://epoch.ai/benchmarks)

## Request

```sh
curl https://router.inference.ai.exchange/v1/chat/completions \
  -H "Authorization: Bearer $LIQUID_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3-vl-235b-a22b-instruct",
    "messages": [{"role":"user","content":"Say hello."}]
  }'
```

## Questions

### What is Qwen 3 VL 235B a22b Instruct?

Served by intent-router: each job goes to the cheapest healthy upstream for this model.

### How much does Qwen 3 VL 235B a22b Instruct cost?

Offers now ask $0.250 per million input tokens and $1.100 per million output tokens. A job pays for the tokens it uses. The exchange picks the cheapest eligible offer.

### What is the context length of Qwen 3 VL 235B a22b Instruct?

The largest offer takes 256,000 input tokens. One job can generate up to 32,768 tokens. A smaller offer can take less. The exchange skips an offer that cannot take your message.

### What can Qwen 3 VL 235B a22b Instruct do?

Its providers list Vision, JSON object, JSON schema, Frequency penalty, Presence penalty, Seed, Stop sequences, Top-p sampling, Text, Tools.

### What inputs and outputs does Qwen 3 VL 235B a22b Instruct support?

Text · Images → Text.

### Which providers serve Qwen 3 VL 235B a22b Instruct?

1 independent provider quote it now, with 1 offer. The table above ranks them by the cost of a typical job.

### How do I call Qwen 3 VL 235B a22b Instruct?

Send "qwen3-vl-235b-a22b-instruct" as the model in an OpenAI or Anthropic request. The API section links to setup steps for each client.

### When was Qwen 3 VL 235B a22b Instruct listed?

The exchange first listed it on 10/8/2026. This is not its release date.

## Related pages

- [This page in a browser](https://liquidinference.ai/models/qwen3-vl-235b-a22b-instruct)
- [Qwen 3 14B](https://liquidinference.ai/models/qwen3-14b.md)
- [Qwen 3.8 27B](https://liquidinference.ai/models/qwen3.8-27b.md)
- [Qwen 3 5 4B](https://liquidinference.ai/models/qwen3-5-4b.md)
- [Qwen 3.5 9B](https://liquidinference.ai/models/qwen3.5-9b.md)
- [Qwen 2.5 7B Instruct](https://liquidinference.ai/models/qwen2.5-7b-instruct.md)
- [Qwen 3.6 35B a3b](https://liquidinference.ai/models/qwen3.6-35b-a3b.md)
- [Set up a client](https://liquidinference.ai/connect/qwen3-vl-235b-a22b-instruct)
- [All models](https://liquidinference.ai/models)
- [Providers](https://liquidinference.ai/providers.md)
- [Discover the best model for your budget](https://liquidinference.ai/discover)
- [Quickstart](https://liquidinference.ai/docs/quickstart.md)
- [llms.txt: the index of every page, as Markdown](https://liquidinference.ai/llms.txt)
- [llms-full.txt: every page in one file](https://liquidinference.ai/llms-full.txt)