AI Models

The latest and greatest from Anthropic.

The latest model from Anthropic (but you can make it think).

The last gen model from Anthropic with advanced reasoning capabilities.

Smart model for complex problems.

DeepSeek-V3.1 is a large hybrid reasoning model (671B parameters, 37B active).

DeepSeek-V3.1 is a large hybrid reasoning model (671B parameters, 37B active).

The open source reasoning model that shook the whole industry.

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](/deepseek/deepseek-chat-v3) model and performs really well on a variety of tasks.

DeepSeek R1 Distill Llama 70B is a distilled large language model based on [Llama-3.3-70B-Instruct](/meta-llama/llama-3.3-70b-instruct), using outputs from [DeepSeek R1](/deepseek/deepseek-r1). The model combines advanced distillation techniques to achieve high performance across multiple benchmarks, including: - AIME 2024 pass@1: 70.0 - MATH-500 pass@1: 94.5 - CodeForces Rating: 1633 The model leverages fine-tuning from DeepSeek R1's outputs, enabling competitive performance comparable to larger frontier models.

Gemini 2.5 Flash Image Preview (Nano Banana) is a state of the art image generation model with contextual understanding.

Gemini 2.5 Flash-Lite is a member of the Gemini 2.5 series of models, a suite of highly-capable, natively multimodal models with reasoning capabilities.

Google's state of the art fast model, known for speed and accuracy, now with support for "thinking".

Google's most advanced model, excelling at complex reasoning and problem-solving.

Similar to 2.0 Flash, but even faster.

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.

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 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.

Industry-leading speed in an open source model.

Kimi K2 0905 is the September update of Kimi K2 0711.

Kimi K2 Instruct is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion total parameters with 32 billion active per forward pass. It is optimized for agentic capabilities, including advanced tool use, reasoning, and code synthesis. Kimi K2 excels across a broad range of benchmarks, particularly in coding (LiveCodeBench, SWE-bench), reasoning (ZebraLogic, GPQA), and tool-use (Tau2, AceBench) tasks. It supports long-context inference up to 128K tokens and is designed with a novel training stack that includes the MuonClip optimizer for stable large-scale MoE training.

A lighter-weight GPT-5 variant optimized for speed while retaining strong reasoning and tool use.

An ultra-fast GPT-5 variant tuned for low-latency tasks with reasoning and tool use.

OpenAI's latest flagship model with advanced reasoning capabilities.

A large open-weight model from OpenAI.

A medium-sized open-weight model from OpenAI suitable for general-purpose tasks.

Qwen 3-Coder is a cutting-edge model from Alibaba that rivals o3, Deepseek, and even Kimi K2.

Qwen3-235B-A22B-Thinking-2507 is a high-performance, open-weight Mixture-of-Experts (MoE) language model optimized for complex reasoning tasks. It activates 22B of its 235B parameters per forward pass and natively supports up to 262,144 tokens of context. This "thinking-only" variant enhances structured logical reasoning, mathematics, science, and long-form generation, showing strong benchmark performance across AIME, SuperGPQA, LiveCodeBench, and MMLU-Redux. It enforces a special reasoning mode (</think>) and is designed for high-token outputs (up to 81,920 tokens) in challenging domains. The model is instruction-tuned and excels at step-by-step reasoning, tool use, agentic workflows, and multilingual tasks. This release represents the most capable open-source variant in the Qwen3-235B series, surpassing many closed models in structured reasoning use cases.

Qwen3-235B-A22B-Instruct-2507 is a multilingual, instruction-tuned mixture-of-experts language model based on the Qwen3-235B architecture, with 22B active parameters per forward pass. It is optimized for general-purpose text generation, including instruction following, logical reasoning, math, code, and tool usage. The model supports a native 262K context length and does not implement "thinking mode" (<think> blocks). Compared to its base variant, this version delivers significant gains in knowledge coverage, long-context reasoning, coding benchmarks, and alignment with open-ended tasks. It is particularly strong on multilingual understanding, math reasoning (e.g., AIME, HMMT), and alignment evaluations like Arena-Hard and WritingBench.

Qwen3-32B is a dense 32.8B parameter causal language model from the Qwen3 series, optimized for both complex reasoning and efficient dialogue. It supports seamless switching between a "thinking" mode for tasks like math, coding, and logical inference, and a "non-thinking" mode for faster, general-purpose conversation. The model demonstrates strong performance in instruction-following, agent tool use, creative writing, and multilingual tasks across 100+ languages and dialects. It natively handles 32K token contexts and can extend to 131K tokens using YaRN-based scaling.

Similar to the Llama distilled model, but distilled on Qwen 32B instead.

xAI's latest multimodal model with SOTA cost-efficiency and a 2M token context window.

xAI's latest multimodal model with SOTA cost-efficiency and a 2M token context window.

xAI's flagship model that excels at data extraction, coding, and text summarization.

A lightweight model that thinks before responding.

GLM-4.5V is an open-weight MoE model that competes with o3 and Claude 4 while being smaller and stronger than DeepSeek-R1 and Kimi K2.

GLM-4.5 is an open-weight MoE model that competes with o3 and Claude 4 while being smaller and stronger than DeepSeek-R1 and Kimi K2.

GLM-4.5 is an open-weight MoE model that competes with o3 and Claude 4 while being smaller and stronger than DeepSeek-R1 and Kimi K2.

GLM-4.5 Air is the lightweight variant of GLM-4.5, an open-weight MoE model that competes with o3 and Claude 4 while being smaller and stronger than DeepSeek-R1 and Kimi K2.