UltraGPT
Every model, one list.
The full catalog UltraGPT draws from — context window, live pricing, capabilities and benchmarks for each model, kept in sync automatically. No guessing which model does what before you switch.
515
Models tracked
63
Providers
377
Reasoning models
333
See images (vision)
2,000K
Largest context window
28
Free to run
Explore and compare AI models
Search hundreds of AI models from leading providers and compare the specifications that matter for your work. Filter by provider and capabilities, then compare context window, reasoning support, input and output modalities, pricing and independent benchmark results where available.
Not sure where to start? Choose a use case above and narrow the catalog to models built for the job.
515 of 515 models
DeepSeek V4 Flash is an efficiency-optimized Mixture-of-Experts model from DeepSeek with 284B total parameters and 13B activated parameters, supporting a 1M-token context window. It is designed for fast inference and...
Design Arena — ranked #41 across 8 categories
DeepSeek V4 Flash Vision Exp is an experimental vision-enabled version of DeepSeek V4 Flash 0731 from DeepSeek, adding image understanding while matching the base model on text capabilities including agents,...
DeepSeek V4.1 Flash 1017 is the October 17, 2026 snapshot of DeepSeek V4.1 Flash, served by DeepSeek under the deepseek-flash API model name. V4.1 Flash is a sparse mixture-of-experts model...
Glimpse is a multimodal visual reasoning model from Elorian, built for visual understanding and complex visual reasoning in the physical world. It reasons directly over images and video rather than...
Beam 1.0 is Reflection's open-weight reasoning model for coding and long-horizon agentic workflows. It uses a sparse mixture-of-experts architecture with 501B total and 23B active parameters per token, and a...
Xing4.0-29B-A4B is an open-weight mixture-of-experts language model from China Telecom's AI team (TeleAI) and the successor to the TeleChat series. It has 29B total parameters with 4B active per token...
Muse Spark Open is a coding-optimized open-weight model. Trained specifically for agentic coding workflows, it delivers higher first-attempt accuracy, reliable tool calling, and deep codebase understanding across the repos developers...
Design Arena — ranked #24 across 1 categories
Moondream 3 Preview is a vision-language model from Moondream with 9B total parameters and 2B active (mixture-of-experts). It handles visual question answering, captioning, OCR, and visual reasoning over images, with...
Moondream 3.1 is a vision-language model from Moondream, the successor to Moondream 3 Preview, with 9B total parameters and 2B active (mixture-of-experts). It handles visual question answering, captioning, OCR, and...
NVIDIA Nemotron 3.5 Super VL is an open multimodal mixture-of-experts model from NVIDIA, with 12B active parameters out of 120B total. It takes text, images, documents, GUI screenshots, and video...
Design Arena — ranked #79 across 5 categories
NVIDIA Nemotron 3.5 Super VL is an open multimodal mixture-of-experts model from NVIDIA, with 12B active parameters out of 120B total. It takes text, images, documents, GUI screenshots, and video...
Design Arena — ranked #79 across 5 categories
Pareto is a multimodal composite model built for research, coding, and agentic workflows, while delivering frontier-level performance across a broad range of general-purpose tasks.
Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across...
Design Arena — ranked #6 across 6 categories
Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across...
Design Arena — ranked #6 across 6 categories
LFM2.5-24B-A2B is a mixture-of-experts reasoning model from Liquid AI with 24B total and 2.45B active parameters. It always reasons before answering and is built for agents that call tools. Liquid...
Pareto is a multimodal composite model built for research, coding, and agentic workflows, while delivering frontier-level performance across a broad range of general-purpose tasks.
This model always redirects to the latest model in the Pareto family.
Extract is a description-driven entity extraction model from ScaleDown. Instead of a fixed set of entity types, callers describe the fields they want in plain English through provider.options.scaledown.entities, and the...
Summarize is an abstractive summarization model from ScaleDown. It condenses long text and documents into a fluent, faithful rewrite rather than lifting sentences from the source, and is suited for...
Muse Wonder is a frontier reasoning model from Meta, designed for complex personal and professional agentic tasks. It accepts text, images, video, audio, and PDF documents and returns text, with...
Design Arena — ranked #1 across 6 categories
Claude Haiku 5.5 is Anthropic's small, fast model for high-volume, cost-sensitive work such as summarization, subagents, and browser use. It succeeds Claude Haiku 4.5 with stronger coding, computer use, and...
Design Arena — ranked #9 across 6 categories
Claude Haiku 5.5 is Anthropic's small, fast model for high-volume, cost-sensitive work such as summarization, subagents, and browser use. It succeeds Claude Haiku 4.5 with stronger coding, computer use, and...
Design Arena — ranked #9 across 6 categories
Nano Banana 2.1 (Gemini Nano Banana 2.1) is Google's image generation and editing model on the Flash tier, succeeding Nano Banana 2 and Nano Banana Pro. It improves product recontextualization,...
Mistral Large 4 is a frontier multimodal (text and image input) model from Mistral AI built for reasoning, coding, and agentic workloads. It offers a 1M-token context window with up...
Design Arena — ranked #17 across 8 categories
How to choose the right AI model
The best AI model depends on what you are trying to accomplish. A model that performs well on complex reasoning tasks may not be the fastest option for everyday questions, while a model with a very large context window may be more useful when working with long documents or codebases. Here are the main factors worth considering.
Start with the task
- For coding, look at coding performance, reasoning ability and tool support.
- For research and complex analysis, reasoning capability and a larger context window can be more important.
- For writing, brainstorming and everyday conversations, response quality and speed may matter more than maximum reasoning performance.
- For images, screenshots and visual documents, choose a model that supports vision input.
Consider context window
The context window determines how much information a model can work with at once. Larger context windows can be useful for long documents, large codebases, lengthy conversations and research involving multiple sources. A larger context window, however, does not automatically mean a model is better at every task.
Look at reasoning capabilities
Reasoning models spend additional computation working through difficult problems before producing an answer. They can be useful for programming, mathematics, planning, research and other multi-step tasks, while simpler tasks may benefit more from faster models.
Check supported modalities
AI models can support different types of input and output. Depending on the model, you may be able to work with text, images, files, audio or video. Check each model's capabilities before choosing it for a multimodal workflow.
Balance capability and speed
The most capable model is not always the most practical model. For quick questions, rewriting, classification or simple workflows, a faster model may provide a better experience. For difficult problems, a more capable reasoning model may be worth the additional processing time.
Compare models instead of relying on one
Different models have different strengths. Comparing several models on the same task is often the easiest way to discover which one works best for your workflow.