Over the past five months since the release of Qwen2-VL, developers have built new models based on it, contributing valuable feedback. Now, Qwen2.5-VL introduces enhanced capabilities, including precise analysis of images, text, and charts, as well as object localization with structured JSON outputs. It understands long videos, identifies key events, and functions as an agent, interacting with tools on computers and phones. The model's architecture features dynamic video processing and an optimized ViT encoder for improved speed and accuracy.
GPT-4.1 Nano, launched by OpenAI on April 14, 2025, is the company's fastest and most affordable model to date. Designed for low-latency tasks such as classification, autocomplete, and fast inference scenarios, it combines compact architecture with robust capabilities. Despite its size, it supports an impressive 1 million token context window and delivers strong benchmark results, achieving 80.1% on MMLU and 50.3% on GPQA. With a knowledge cutoff of June 2024, GPT-4.1 Nano offers exceptional value at just $0.10 per million input tokens and $0.40 per million output tokens, with a 75% discount applied to cached inputs, making it ideal for high-volume, cost-sensitive deployments.
| Qwen2.5-VL-32B | GPT-4.1 Nano | |
|---|---|---|
Web Site
| ||
Provider
| ||
Chat
| ||
Release Date
| ||
Modalities
| text images video | text images |
API Providers
| - | OpenAI API |
Knowledge Cut-off Date
| Unknown | - |
Open Source
| Yes (Source) | No |
Pricing Input
| $0 | $0.10 per million tokens |
Pricing Output
| $0 | $0.40 per million tokens |
MMLU
| 78.4% Source | 80.1% Source |
MMLU-Pro
| 49.5% | - |
MMMU
| 70% | 55.4% Source |
HellaSwag
| Not available | - |
HumanEval
| Not available | - |
MATH
| 82.2% | - |
GPQA
| 46.0% Diamond | 50.3% Diamond Source |
IFEval
| Not available | 74.5% Source |
SimpleQA
| - | - |
AIME 2024 | - | 29.4% Source |
AIME 2025 | - | - |
Aider Polyglot
| - | - |
LiveCodeBench v5
| - | - |
Global MMLU (Lite)
| - | 66.9% Source |
MathVista
| - | 56.2% Image Reasoning Source |
Mobile Application | - |
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