qwen/qwen2.5-vl-72b-instruct
Release date: 2025-02-01
32,000 context · $0.80/M input tokens · $1.00/M output tokens
Qwen2.5-VL is proficient in recognizing common objects such as flowers, birds, fish, and insects. It is also highly capable of analyzing texts, charts, icons, graphics, and layouts within images.
Pricing
Pay-per-use
No upfront costs, pay only for what you use
Try the model
API Usage
Use the following code examples to integrate with our API:
import OpenAI from 'openai';
if (!process.env.WAVESPEED_API_KEY) throw new Error('Set WAVESPEED_API_KEY');
const client = new OpenAI({
apiKey: process.env.WAVESPEED_API_KEY,
baseURL: 'https://llm.wavespeed.ai/v1',
timeout: 120_000,
maxRetries: 0,
});
try {
const response = await client.chat.completions.create({
model: 'qwen/qwen2.5-vl-72b-instruct',
messages: [{ role: 'user', content: 'Hello!' }],
});
const body = response as typeof response & { error?: { message?: string } };
if (body.error) throw new Error(body.error.message || 'LLM generation failed');
console.log(response.choices[0]?.message?.content ?? '');
} catch (error) {
console.error('LLM request failed:', error);
process.exitCode = 1;
}import OpenAI from 'openai';
if (!process.env.WAVESPEED_API_KEY) throw new Error('Set WAVESPEED_API_KEY');
const client = new OpenAI({
apiKey: process.env.WAVESPEED_API_KEY,
baseURL: 'https://llm.wavespeed.ai/v1',
timeout: 120_000,
maxRetries: 0,
});
try {
const response = await client.chat.completions.create({
model: 'qwen/qwen2.5-vl-72b-instruct',
messages: [{ role: 'user', content: 'Hello!' }],
});
const body = response as typeof response & { error?: { message?: string } };
if (body.error) throw new Error(body.error.message || 'LLM generation failed');
console.log(response.choices[0]?.message?.content ?? '');
} catch (error) {
console.error('LLM request failed:', error);
process.exitCode = 1;
}Model Introduction
Qwen: Qwen2.5 VL 72B Instruct
Qwen2.5-VL is proficient in recognizing common objects such as flowers, birds, fish, and insects. It is also highly capable of analyzing texts, charts, icons, graphics, and layouts within images.
This model is available through the WaveSpeed AI OpenAI-compatible API for chat completions and compatible application workflows.
Why It Looks Great
- text+image->text architecture for Text, Image to Text workloads
- 128000 context window for long prompts, document analysis, and multi-turn workflows
- Vision input support for image understanding and multimodal tasks
- Structured output support for JSON responses and schema-constrained generation
Key Features
- Context Window: 128000 tokens
- Max Input: 12800 tokens
- Max Output: 115200 tokens
- Vision: Supported
- Function Calling: Not listed
- Structured Outputs: Supported
- Image Generation: Not listed
- Audio Input: Not listed
- Supported Parameters: frequency_penalty, logit_bias, logprobs, max_tokens, presence_penalty, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, top_k, top_logprobs, top_p
Specifications
| Specification | Value |
|---|---|
| Provider | alibaba |
| Model Type | Chat Completions model |
| Architecture | text+image->text |
| Context Window | 128000 tokens |
| Max Input | 12800 tokens |
| Max Output | 115200 tokens |
| Input | Text, Image |
| Output | Text |
| Vision | Supported |
| Function Calling | Not listed |
| Structured Outputs | Supported |
| Release Date | February 1, 2025 |
How to Use
- Write your prompt - describe the task, provide context, and specify the desired output format.
- Submit - the model processes your request and returns the response.
API Integration
Base URL: https://llm.wavespeed.ai/v1 API Endpoint: chat/completions Model ID: qwen/qwen2.5-vl-72b-instruct
API Usage
Python SDK
from openai import OpenAI
client = OpenAI(
api_key="YOUR_API_KEY",
base_url="https://llm.wavespeed.ai/v1"
)
response = client.chat.completions.create(
model="qwen/qwen2.5-vl-72b-instruct",
messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)cURL
curl https://llm.wavespeed.ai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_API_KEY" \
-d '{
"model": "qwen/qwen2.5-vl-72b-instruct",
"messages": [{"role": "user", "content": "Hello!"}]
}'Notes
- Model: qwen/qwen2.5-vl-72b-instruct
- Provider: alibaba
Info
Supported Functionality
API Access Guide
Qwen2.5 Vl 72b Instruct API
qwen/qwen2.5-vl-72b-instruct
Qwen2.5-VL is proficient in recognizing common objects such as flowers, birds, fish, and insects. It is also highly capable of analyzing texts, charts, icons, graphics, and layouts within images.
Input
$0.8 /M
Output
$1 /M
Context
32K
Vision
Supported
Try Qwen2.5 Vl 72b Instruct on WaveSpeedAI
Access Qwen2.5 Vl 72b Instruct through our unified API — OpenAI-compatible, no cold starts, transparent pricing.
Frequently Asked Questions about Qwen2.5 Vl 72b Instruct
How much does Qwen2.5 Vl 72b Instruct cost via the API?+
Pricing on WaveSpeedAI: $0.80 per million input tokens and $1.00 per million output tokens. Prompt caching and batch processing are billed separately and reduce effective cost on long, repetitive workloads.
What is the context window of Qwen2.5 Vl 72b Instruct?+
Qwen2.5 Vl 72b Instruct supports up to 32K tokens of context with up to — tokens of output per request.
Is Qwen2.5 Vl 72b Instruct OpenAI-compatible?+
WaveSpeedAI exposes Qwen2.5 Vl 72b Instruct at https://llm.wavespeed.ai/v1 through the OpenAI-compatible Chat Completions interface. Most OpenAI SDK clients work by changing the base URL and API key; optional fields depend on the selected model.
How do I get started with Qwen2.5 Vl 72b Instruct?+
Sign in to WaveSpeedAI, create an API key in Access Keys, then send a request to https://llm.wavespeed.ai/v1/chat/completions with the model id shown above. Check the current model catalog for availability, capabilities, and pricing.