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Like gpt-4o, but faster. This model sacrifices some of the original GPT-4o's precision for significantly reduced latency. It accepts both text and image inputs.

OpenAI's flagship non-reasoning model. Works with text and images. Relatively smart. Good at most things.

DeepSeek's groundbreaking direct prediction model. Laid the groundwork for R1 (their reasoning model). Super underrated, comparable performance to Claude 3.5 Sonnet. Just... slow.

DeepSeek V3, a 685B-parameter, mixture-of-experts model, is the latest iteration of the flagship chat model family from the DeepSeek team. It succeeds the DeepSeek V3 model and performs really well on a variety of tasks.

The open source reasoning model that shook the whole industry. Very smart. Shows all of its thinking. Not the fastest.

It's like normal R1, but WAY faster and slightly dumber. Basically, DeepSeek stuffed Llama full of R1 knowledge. Since Llama is smaller and faster, the result is a really good compromise.

A small, fast, super smart reasoning model. OpenAI clearly didn't want DeepSeek to be getting all the attention. Good at science, math, and coding, even if it's not as good at CSS.

Google's flagship model, known for speed and accuracy (and also web search!). Not quite as smart as Claude 3.5 Sonnet, but WAY faster and cheaper. Also has an insanely large context window (it can handle a lot of data).

Similar to 2.0 Flash, but even faster. Not as smart, but still good at most things.

Google's most advanced model, excelling at complex reasoning and problem-solving. Particularly strong at tackling difficult code challenges, mathematical proofs, and STEM problems. With its massive context window, it can deeply analyze large codebases, datasets and technical documents to provide comprehensive solutions.

The best model for writing. Biggest model ever? Also very slow and expensive.

A surprisingly smart reasoning model that punches way above its weight. Despite being much smaller than other models, it's just as good at complex tasks, in fact it beats GPT-4o and Claude 3.5 Sonnet on math and coding benchmarks. And since it runs on Groq, it's FAST.

This is a cloaked model provided to the community to gather feedback. It's geared toward real world use cases, including programming. NOTE: All prompts and completions for this model are logged by OpenRouter as well as the upstream provider.

Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input (text and image) and multilingual output (text and code) across 12 supported languages. Designed for assistant-style interaction and visual reasoning, Scout uses 16 experts per forward pass and features a context length of up to 10 million tokens, with a training corpus of ~40 trillion tokens. Built for high efficiency and local or commercial deployment, Llama 4 Scout incorporates early fusion for seamless modality integration. It is instruction-tuned for use in multilingual chat, captioning, and image understanding tasks. Released under the Llama 4 Community License, it was last trained on data up to August 2024 and launched publicly on April 5, 2025.

Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model from Meta, built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active parameters per forward pass (400B total). It supports multilingual text and image input, and produces multilingual text and code output across 12 supported languages. Optimized for vision-language tasks, Maverick is instruction-tuned for assistant-like behavior, image reasoning, and general-purpose multimodal interaction. Maverick features early fusion for native multimodality and a 1 million token context window. It was trained on a curated mixture of public, licensed, and Meta-platform data, covering ~22 trillion tokens, with a knowledge cutoff in August 2024. Released on April 5, 2025 under the Llama 4 Community License, Maverick is suited for research and commercial applications requiring advanced multimodal understanding and high model throughput.

xAI's flagship model that excels at data extraction, coding, and text summarization. Possesses deep domain knowledge in finance, healthcare, law, and science.

A lightweight model that thinks before responding. Great for simple or logic-based tasks that do not require deep domain knowledge.