Qwen: Qwen2.5 VL 32B Instruct✓ Catalog verified
Qwen2.5-VL-32B is a multimodal vision-language model fine-tuned through reinforcement learning for enhanced mathematical reasoning, structured outputs, and visual problem-solving capabilities. It excels at visual analysis tasks, including object recognition, textual interpretation within images, and precise event localization in extended videos. Qwen2.5-VL-32B demonstrates state-of-the-art performance across multimodal benchmarks such as MMMU, MathVista, and VideoMME, while maintaining strong reasoning and clarity in text-based tasks like MMLU, mathematical problem-solving, and code generation.
Overview
Qwen2.5-VL-32B is a multimodal vision-language model fine-tuned through reinforcement learning for enhanced mathematical reasoning, structured outputs, and visual problem-solving capabilities. It excels at visual analysis tasks, including object recognition, textual interpretation within images, and precise event localization in extended videos. Qwen2.5-VL-32B demonstrates state-of-the-art performance across multimodal benchmarks such as MMMU, MathVista, and VideoMME, while maintaining strong reasoning and clarity in text-based tasks like MMLU, mathematical problem-solving, and code generation.
Access: available through the official Qwen API. Context window: 128K.
Specifications
| API identifier | qwen/qwen2.5-vl-32b-instruct |
| Provider | Qwen |
| Model type | Multimodal LLM |
| Context window | 128K catalog |
| Input modalities | text · image |
| Output modalities | text |
| Released | Mar 24, 2025 |
| Tokenizer | Qwen |
| Moderated | No |
| Architecture modality | text+image->text |
Pricing
Live pricing components for qwen/qwen2.5-vl-32b-instruct as published in the catalog.
| Pricing component | Raw unit price | Normalized | Unit |
|---|---|---|---|
| Prompt tokens | $2e-7 | $0.20 / 1M | Per input token |
| Completion tokens | $6e-7 | $0.60 / 1M | Per output token |
| Request fee | N/A | N/A | Per request |
| Image fee | N/A | N/A | Per image unit |
| Web search fee | N/A | N/A | Per search request |
Cost calculator
Estimate monthly spend from your own token volumes.
Estimated Total Cost
$0.22Calculated from current list pricing in the ModelsAtlas catalog.Capabilities
| Capability | Status | What it means |
|---|---|---|
| Visual Understanding | ✓ Supported | Image and document analysis support |
| Audio Processing | Not advertised | Speech and voice aligned flows |
| Tool Calling | Not advertised | Supports tools / function calling |
| Self-Hosting | ✓ Supported | Deploy outside managed APIs |
Capability signals
Directional signals derived from model metadata and capability tags — not official benchmark submissions.
Quick start
Call Qwen: Qwen2.5 VL 32B Instruct through an OpenAI-compatible client.
from openai import OpenAI
client = OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key="YOUR_API_KEY"
)
response = client.chat.completions.create(
model="qwen/qwen2.5-vl-32b-instruct",
messages=[{"role": "user", "content": "Explain quantum physics."}]
)
print(response.choices[0].message.content)Alternatives
Closest models by context window from a different provider.
| Model | Provider | Context | Input /1M | Actions |
|---|---|---|---|---|
| AllenAI: Olmo 2 32B Instruct | Allenai | 128K | $0.05 | View → |
| Amazon: Nova Micro 1.0 | Amazon | 128K | $0.04 | View → |
Sources & attribution
Pricing and metadata are maintained in the ModelsAtlas catalog.
Frequently asked questions
How much does Qwen: Qwen2.5 VL 32B Instruct cost?
$0.20 per 1M input tokens and $0.60 per 1M output tokens on the official Qwen API.
What is the context window of Qwen: Qwen2.5 VL 32B Instruct?
Qwen: Qwen2.5 VL 32B Instruct supports up to 128K tokens of context.
Does Qwen: Qwen2.5 VL 32B Instruct support tool / function calling?
Qwen: Qwen2.5 VL 32B Instruct does not advertise first-class tool/function calling. Check vendor docs for the latest capabilities.
How do I access Qwen: Qwen2.5 VL 32B Instruct?
Use the official Qwen API with the model id `qwen/qwen2.5-vl-32b-instruct`. See the Quick start section above for code examples.