How much does the Qwen3 VL 235B A22B Instruct API cost?

On FastMetal, Qwen3 VL 235B A22B Instruct is billed per token: $0.211 per 1M input tokens and $0.9284 per 1M output tokens on a US-dollar account, before tax. Usage is drawn from a prepaid balance; there is no subscription or monthly fee.

Can I call Qwen3 VL 235B A22B Instruct with the OpenAI SDK?

Yes. Point base_url at https://api.fastmetal.ai/v1 and pass "qwen3-vl-235b-a22b-instruct" as the model; existing OpenAI-style code works unchanged, including streaming, tool calls and structured output.

What is the context window of Qwen3 VL 235B A22B Instruct?

262,144 tokens, shared between the prompt and the response.

How does Qwen3 VL 235B A22B Instruct rank?

#118 on the public arena's Japanese board (ELO 1,370). Ranks move as the leaderboard is updated.

What do I need to try Qwen3 VL 235B A22B Instruct?

Create an account and add credit; the model is then available both in the browser chat and over the API. There is no contract or minimum spend.

Back to Models

Qwen

qwen3-vl-235b-a22b-instruct

Qwen3 VL 235B A22B Instruct

Qwen3-VL-235B-A22B Instruct is an open-weight multimodal model that unifies strong text generation with visual understanding across images and video. The Instruct model targets general vision-language use (VQA, document parsing, chart/table extraction, multilingual OCR). The series emphasizes robust perception (recognition of diverse real-world and synthetic categories), spatial understanding (2D/3D grounding), and long-form visual comprehension, with competitive results on public multimodal benchmarks for both perception and reasoning. Beyond analysis, Qwen3-VL supports agentic interaction and tool use: it can follow complex instructions over multi-image, multi-turn dialogues; align text to video timelines for precise temporal queries; and operate GUI elements for automation tasks. The models also enable visual coding workflows—turning sketches or mockups into code and assisting with UI debugging—while maintaining strong text-only performance comparable to the flagship Qwen3 language models. This makes Qwen3-VL suitable for production scenarios spanning document AI, multilingual OCR, software/UI assistance, spatial/embodied tasks, and research on vision-language agents.

9/23/2025

262,144 tokens

#84 Vision (Overall)

Input: $0.211/M

Output: $0.9284/M

Try in Chat Get API Key

Specifications

Modalities

Input

text

image

Output

text

Supported Parameters

frequency_penalty

logit_bias

max_tokens

min_p

presence_penalty

repetition_penalty

response_format

seed

stop

structured_outputs

temperature

tool_choice

tools

top_k

top_p

Data policy

Prompt retention: Unknown — we could not confirm
Training: Not used for training

"Unknown" does not mean "safe". It means we could not confirm it.

Whether a provider trains on prompts is a declared value from our terms with them. Retention is determined from the upstream listing for every host this model can reach. Neither is guessed.

Measured performance

Time to first response (p50)771ms

Throughput (p50)14 tokens/s

Measured through FastMetal's own gateway over the last 30 days (660 first-response samples).

Throughput is sampled once a day: the median of 27 observations over the last 30 days.

Time to first response is measured to the first streamed chunk; throughput counts all output tokens, including reasoning tokens. These are observations, not a performance guarantee.

Code Examples

curl https://api.fastmetal.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "qwen3-vl-235b-a22b-instruct",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

How Qwen3 VL 235B A22B Instruct actually answers

Real responses to our standard prompts, recorded on FastMetal.

Count the number of 'r's in 'strawberry'

Count the number of 'r's in 'strawberry'. Explain your reasoning step by step.

Let’s go step by step to count the number of **'r'** letters in the word **'strawberry'**. --- ### Step 1: Write down the word clearly **s t r a w b e r r y** --- ### Step 2: Go letter by letter and look for 'r' We’ll go from left to right: 1. **s** → not 'r' 2. **t** → not 'r' 3. **r** → ✅ **1st 'r'** 4. **a** → not 'r' 5. **w** → not 'r' 6. **b** → not 'r' 7. **e** → not 'r' 8. **r** → ✅ **2nd 'r'** 9. **r** → ✅ **3rd 'r'** 10. **y** → not 'r' --- ### Step 3: Total up the 'r's We found **'r'** at positions 3, 8, and 9. That’s **3** occurrences. --- ### ✅ Final Answer: **3** There are **three 'r's** in the word **'strawberry'**.

11.4s response261 output tokens

Debug This Error

I'm getting the following error in my Node.js application: TypeError: Cannot read properties of undefined (reading 'map') at UserList (/app/components/UserList.js:12:25) at renderWithHooks (/app/node_modules/rea…

The error `TypeError: Cannot read properties of undefined (reading 'map')` occurs because the `users` prop is `undefined` when your component tries to call `.map()` on it. This typically happens when: 1. The parent component hasn't passed the `users` prop 2. The `users` prop is passed as `undefined` (not an array) 3. The data hasn't loaded yet (common in async scenarios) ## Solutions: ### Solution 1: Add a null/undefined check (Recommended for most cases) ```javascript function UserList({ users }) { // Check if users is undefined or null if (!users) { return <div>Loading...</div>; // or return null, or show a loading spinner } return ( <ul> {users.map(user => <li…

27.5s response646 output tokens

Code Review

Please review the following Python function and suggest improvements for readability, performance, and best practices: def get_data(url, retries=3): import requests import time for i in range(retries):…

Your function has several areas for improvement in terms of **readability**, **performance**, **error handling**, and **best practices**. Below is a revised version with explanations: --- ### ✅ **Improved Version:** ```python import requests import time def get_data(url: str, retries: int = 3) -> list: """ Fetches active items from a JSON API endpoint. Args: url (str): The API endpoint URL. retries (int): Number of retry attempts on failure. Returns: list: List of active items with 'name' and 'id' fields, or empty list on failure. """ for attempt in range(retries): try: response = requests.get(url, timeout=10)…

36.3s response864 output tokens

Compare these answers side by side with other models →

Frequently asked questions

How much does the Qwen3 VL 235B A22B Instruct API cost?: On FastMetal, Qwen3 VL 235B A22B Instruct is billed per token: $0.211 per 1M input tokens and $0.9284 per 1M output tokens on a US-dollar account, before tax. Usage is drawn from a prepaid balance; there is no subscription or monthly fee.
Can I call Qwen3 VL 235B A22B Instruct with the OpenAI SDK?: Yes. Point base_url at https://api.fastmetal.ai/v1 and pass "qwen3-vl-235b-a22b-instruct" as the model; existing OpenAI-style code works unchanged, including streaming, tool calls and structured output.
What is the context window of Qwen3 VL 235B A22B Instruct?: 262,144 tokens, shared between the prompt and the response.
How does Qwen3 VL 235B A22B Instruct rank?: #118 on the public arena's Japanese board (ELO 1,370). Ranks move as the leaderboard is updated.
What do I need to try Qwen3 VL 235B A22B Instruct?: Create an account and add credit; the model is then available both in the browser chat and over the API. There is no contract or minimum spend.

Try Qwen3 VL 235B A22B Instruct right now

Qwen3 VL 235B A22B Instruct is available on FastMetal through one API key. Start in the browser, or call it from the OpenAI SDK.

Create a free account

Leaderboard

Text

russianELO: 1,404

#146

CodingELO: 1,463

#136

MathELO: 1,409

#152

Creative WritingELO: 1,356

#175

Instruction FollowingELO: 1,409

#134

Hard PromptsELO: 1,438

#137

Multi-TurnELO: 1,425

#129

OverallELO: 1,413

#147

JapaneseELO: 1,370

#118

ChineseELO: 1,453

#135

KoreanELO: 1,375

#111

EnglishELO: 1,426

#142

frenchELO: 1,446

#116

germanELO: 1,412

#120

spanishELO: 1,418

#121

Vision

OverallELO: 1,215

#84

Rankings: LMArena leaderboard dataset (lmarena-ai/leaderboard-dataset), CC BY 4.0, snapshot 2026-10-22; category names mapped and ratings rounded by FastMetal.

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