Google: Gemma 3 4B

Qwen2.5-VL-7B-Instruct is a 7-billion parameter vision-language model from Alibaba’s QwenLM team, released on January 26, 2025 under the Apache 2.0 license. It is the instruction-tuned variant of the 7B scale in the Qwen2.5-VL family, designed to process multimodal inputs such as text, images, charts, documents, and video. The model enables structured outputs—including JSON for structured content and bounding boxes for visual localization. Weights are publicly available on Hugging Face and GitHub, making it suitable for both research and applied multimodal use.

PaliGemma 2 is a vision-language model released in December 2024 by Google DeepMind. It pairs the SigLIP-So400m vision encoder with the Gemma 2 language model family, extending the original PaliGemma architecture with stronger language capabilities and a wider set of transfer benchmarks. The model is designed primarily as a fine-tuning base rather than a chat-optimized assistant. Google releases pretrained "PT" checkpoints intended for task-specific adaptation rather than direct out-of-the-box use.PaliGemma 2 accepts an image paired with a text prompt and generates natural language output, supporting image captioning, visual question answering, optical character recognition, document understanding, object detection and segmentation (with appropriate fine-tuning), and a range of specialized vision-language tasks. The model is released at three parameter sizes (3B, 10B, and 28B), built on the Gemma 2 2B, 9B, and 27B language backbones. Each size is available at three input resolutions: 224, 448, and 896 pixels. Alongside the base PT checkpoints, Google released PaliGemma 2 Mix variants that have been tuned on a mixture of downstream tasks to provide stronger out-of-the-box performance for common applications such as OCR and document parsing. PaliGemma 2 is distributed under the Gemma license, a custom license from Google that permits commercial use subject to the terms of the Gemma Prohibited Use Policy.

Gemini 3.5 Flash-Lite is a natively multimodal reasoning model developed by Google DeepMind, released on July 21, 2026 as part of the Gemini 3.5 model family. It is the fastest model in the 3.5 series, designed for both low-latency tasks and high-throughput production workloads such as agentic search, document processing, receipt translation, and large-scale data extraction. The model accepts text, images, audio, and video as inputs, with a context window of up to 1 million tokens, and produces text output. It supports configurable thinking levels, allowing developers to tune the balance between response quality, cost, and latency depending on workload requirements.On agentic and coding benchmarks, Gemini 3.5 Flash-Lite significantly outperforms its predecessor, Gemini 3.1 Flash-Lite, including on Terminal-Bench 2.1 (54% vs. 31%), GDM-MRCR v2 long-context (72.2% vs. 60.1%), and real-world task execution as measured by GDPval-AA v2 (1140 vs. 642). It also surpasses Gemini 3 Flash on SWE-Bench Pro (54.2% vs. 49.6%) and OSWorld-Verified (74.0% vs. 65.1%). According to the Artificial Analysis Index, the model generates output at approximately 350 tokens per second. It is built on the Gemini 3.5 Flash foundation and is evaluated across reasoning, coding, multimodal understanding, multilingual performance, and long-context tasks. The model is developed under Google's Frontier Safety Framework.

GPT-5 Nano, released by OpenAI on August 7, 2025, is the smallest and most cost-efficient model in the GPT-5 family. Like its larger counterparts, it is multimodal—accepting text and images, supporting tool use, structured outputs, and reasoning—but it is optimized for speed, low latency, and affordability. It features input and output token limits of roughly 272K and 128K tokens respectively, enabling large-context processing even at its compact scale. Its knowledge cutoff is around May 2024, slightly earlier than the full GPT-5 model.GPT-5 Nano is well-suited for high-volume or cost-sensitive deployments such as mobile apps, embedded AI systems, or rapid-response APIs. While it offers less depth on complex reasoning and coding tasks compared to GPT-5 Mini or Pro, it retains core multimodal and agentic capabilities, making it an attractive option where efficiency and scale matter more than maximum performance.

Moondream 2 is a small open-source vision-language model from Moondream, the company founded by Vikhyat Korrapati. It was first released in early 2024 and updated through mid-2025. At approximately 1.9 billion parameters, it is designed to run efficiently on consumer hardware such as laptops and edge devices while supporting a practical range of multimodal tasks. Moondream 2 combines a vision encoder based on SigLIP with a compact language backbone, trained for image understanding tasks rather than as a general chat model.The model accepts an image paired with a natural language prompt and produces text responses, supporting visual question answering, image captioning, and image-conditioned dialogue. Later Moondream 2 releases added object localization through a point API that returns coordinates for queried objects, along with improvements to OCR, counting, and document understanding. Moondream 2 is distributed under the Apache 2.0 license and is available through Hugging Face and the maintainer's distribution. Because the model is updated frequently, production deployments should pin to a specific revision rather than tracking the latest release. A successor model, Moondream 3 (Preview), was released in September 2025 with a 9B mixture-of-experts architecture and 2B active parameters, offering substantially stronger visual reasoning than Moondream 2 while retaining the efficiency-focused design. A referring expression segmentation extension to Moondream 3 was released in March 2026.