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Claude Haiku 4.5 is Anthropic's fast Claude model for coding, with more than twice the speed of larger Claude models. It has 64,000 max output tokens, tool use, and text, image and file input.

Claude Haiku 5.5 is Anthropic's fastest model in the Claude 5.x lineup, built for rapid ideation, summarization and classification. It has vision input, tool use and always-on adaptive thinking.

Claude Opus 4.5 is Anthropic's frontier-class model for complex analysis, advanced coding and multi-step reasoning. It supports tool use, 64,000 max output tokens, and text, image and file input.

Claude Opus 4.6 is Anthropic's flagship-tier Claude 4 model for reasoning, advanced coding and nuanced analysis. It has 128K max output, tool use, and text, image and file input.

Claude Opus 4.7 is Anthropic's model for sophisticated research, advanced coding and multi-step agentic workflows. It has 128K max output, tool use, and text, image and file input.

Claude Opus 4.8 is Anthropic's most advanced Claude Opus model, built for complex reasoning, frontier-level coding and in-depth research. It has 128K max output and text, image and file input.

Claude Sonnet 4.5 is Anthropic's model that balances speed and intelligence for demanding text, coding and research tasks. It has 64,000 max output tokens, tool use, and text, image and file input.

Claude Sonnet 4.6 is Anthropic's balanced model for coding, analysis and reasoning. It supports tool use, up to 64,000 output tokens, and text, image and file input.

Claude Sonnet 5.5 is Anthropic's successor to Claude Sonnet 5 for well-scoped everyday work such as building features, fixing bugs, drafting and review. It has a 1M-token context window, 128K max output, vision input, tool use and adaptive thinking on by default.

Seed 2.0 Code is a ByteDance Seed model optimized for agentic coding, frontend development and multilingual programming. It takes text, image and video input, and supports tool use.

DeepSeek V3.1 is an open-weight hybrid reasoning model from DeepSeek with a 131,072-token context window, strong coding and agentic tool-use performance. Served free through SambaNova Cloud's fast RDU inference and SiliconFlow.

DeepSeek-V4-Flash is DeepSeek's open-weight fast mixture-of-experts model for high-throughput chat and coding. It accepts text and image input and returns text. It supports reasoning, tool calling and agentic use.

DeepSeek V4 Pro is DeepSeek's open-weight high-capability reasoning model. It is a text model for chat and coding with streaming and function calling.

DeepSeek V4.1 Flash 1017 is the October 17, 2026 snapshot of DeepSeek V4.1 Flash. It is an open-weight sparse mixture-of-experts model with native vision input, text output and a 1M-token context window.

Gemini 3 Flash is Google's fast Gemini model with strong reasoning, coding, and multimodal understanding. It accepts text, image, video, and audio input. It supports tool calling.

Gemini 3.1 Flash Lite is Google's lightest Gemini model, built for high-throughput tasks. It takes text and image input and generates text. It supports tool calling, coding, and streaming responses.

Gemini 3.1 Pro is Google's most intelligent Gemini model, with improved reasoning and a medium thinking level. It takes text and image input. It supports tool calling and coding.

Gemini 3.5 Flash Lite is Google's high-throughput multimodal model for agentic workflows, complex coding, and long-horizon tasks. It has a 1M-token context window and accepts text and image input. It supports tool calling.

Gemini 3.5 Flash is Google's multimodal model for agentic workflows, complex coding, and long-horizon tasks. It takes text and image input and generates text. It supports tool calling and streaming.

Gemini 3.6 Flash is Google's frontier-level multimodal model that pairs fast inference with strong reasoning and coding. It accepts text and image input. It supports tool calling.

Gemini 3.7 Flash is Google's multimodal model for fast agentic workflows, coding, and complex multi-step reasoning. It accepts text, image, video, file, and audio input.

Gemini 3.8 Flash is Google's multimodal model for fast agentic workflows, coding, and complex multi-step reasoning. It has a 1,048,576-token context window. It accepts text, image, video, file, and audio input.

Gemma 4 31B is Google's open-weight dense open reasoning model for complex reasoning, agentic workflows, coding, and multimodal understanding. It takes text and image input and generates text. It supports tool calling and a thinking mode.

Ling-3.0-flash-Fin is inclusionAI's open-weight finance-focused mixture-of-experts model, built on Ling-3.0-flash with 124B total and 5.1B active parameters. It targets investment research, market analysis and financial reasoning. It keeps general reasoning and coding skills.

Llama 4 Maverick is Meta's multimodal mixture-of-experts model with 128 experts and 17 billion active parameters. It takes text and image input, returns text, and supports function calling and streaming.

Muse Ember is Meta's smallest and most efficient model in the Muse family, built for reasoning and everyday agent work. It has a 1M-token context window with up to 524,288 output tokens. It takes text, image, video, file and audio input and returns text.

Muse Spark Open is Meta's open-weight, coding-optimized model for agentic coding. It is a sparse mixture-of-experts with 816B total and about 42B active parameters and a 1M-token context window. It reads text, images, video, audio and files and returns text.

Muse Wonder is Meta's frontier reasoning model for complex personal and professional agentic tasks, in preview. It accepts text, images, video, audio and PDF documents, returns text, and has a 1M-token context window.

MiniMax M2.5 is MiniMax's large language model for real-world productivity. It extends M2.1 coding expertise into office work such as generating Word, Excel and PowerPoint files. It scores 80.2% on SWE-Bench Verified and 76.3% on BrowseComp.

MiniMax M3 is MiniMax's multimodal language model for coding and agentic work. It accepts text, image and video input and returns text. It supports reasoning, function calling, structured output and multilingual use.

Mistral Large 4 is Mistral AI's open-weight Mixture-of-Experts model with about 1 trillion parameters and a 1M-token context. It takes text and image input and supports tool use, structured output and Low, Medium or High reasoning effort.

Mistral Small 4 is a hybrid instruct-and-reasoning mixture-of-experts model with a 256k context window. It takes text and image input under Apache 2.0. It folds Mistral's separate Instruct, reasoning and coding lines into one model.

Kimi K2.6 is Moonshot AI's open-weight large mixture-of-experts model with agentic tool use, vision input and long-context memory. It supports reasoning and streaming, and takes text and image input.

Kimi K3.1 Pro is the first model in Moonshot AI's open-weight K3.1 series, a post-trained version of Kimi K3. It has a 1M-token context window and native visual understanding, and improves on K3 in coding, 3D and CAD modeling and financial research. Thinking is always on.

Kimi K3 is Moonshot AI's open-weight multimodal reasoning model with native visual understanding. It accepts text, image and video input for long-horizon software engineering, knowledge work and CAD. Thinking is always on.

GPT-4.1 Mini is OpenAI's smaller, faster version of GPT-4.1 with strong instruction following and tool calling. It has a 1,047,576-token input context and 32,768 output tokens. It takes text and image input and supports streaming, function calling and structured outputs.

GPT-4.1 is OpenAI's smartest non-reasoning model, built for instruction following and tool calling. It has a 1,047,576-token input context and 32,768 output tokens, and takes text and image input. It supports streaming, function calling and structured outputs.

GPT-4o Mini is OpenAI's fast, small multimodal model for focused tasks. It takes text and image input and returns text, with a 128,000-token input context and 16,384 output tokens. It supports streaming, function calling and structured outputs.

GPT-4o is OpenAI's versatile flagship multimodal model, with the o standing for omni. It takes text and image input and returns text, with a 128,000-token input context and 16,384 output tokens. It supports streaming, function calling and structured outputs.

GPT-5 Mini is OpenAI's reasoning model for low-latency, high-volume workloads. It allows 128,000 output tokens and takes text and image input. OpenAI recommends GPT-5.4 Mini for most new low-latency workloads.

GPT-5 Nano is OpenAI's fastest GPT-5 model, built for summarization, classification and other simple high-volume tasks. It allows 128,000 output tokens. OpenAI recommends GPT-5.4 Nano for new workloads.

GPT-5 Pro is OpenAI's version of GPT-5 that uses more compute to think harder on difficult tasks. It accepts text and image input, with a 400,000-token context window and up to 272,000 output tokens. It supports tool use and structured outputs.

GPT-5.1 is OpenAI's flagship model for coding and agentic tasks, with configurable reasoning effort from none to high. It allows 128,000 output tokens and takes text and image input. It supports streaming, function calling and structured outputs.

GPT-5.2 Pro is OpenAI's version of GPT-5.2 that uses more compute to think harder on difficult tasks. It accepts text and image input, with a 400,000-token context window and up to 128,000 output tokens. It supports tool use and structured outputs.

GPT-5.2 is OpenAI's previous-generation frontier model for professional work, with configurable reasoning effort. It allows 128,000 output tokens and takes text and image input. OpenAI recommends GPT-5.4 for new workloads.

GPT-5.3 Codex is OpenAI's Codex-environment variant of GPT-5.3 for agentic coding. It accepts text and image input and supports reasoning, streaming, function calling and structured outputs.

GPT-5.4 Mini is OpenAI's mini model for coding, computer use and subagents, built for high-volume workloads. It allows 128,000 output tokens and takes text and image input. It supports reasoning tokens, function calling and structured outputs.

GPT-5.4 Nano is OpenAI's GPT-5.4-class model for simple, high-volume tasks such as classification, extraction and ranking. It allows 128,000 output tokens and takes text and image input. It supports function calling and structured outputs.

GPT-5.4 Pro is OpenAI's high-capacity model in the GPT-5 family, with advanced reasoning, broad knowledge and coding proficiency. It accepts text and image input and returns text, with tool-use capabilities.

GPT-5.4 is OpenAI's frontier model for complex professional workloads, with configurable reasoning effort from none to xhigh. It allows 128,000 output tokens and takes text and image input. It supports computer use, MCP and tool search.

GPT-5 is OpenAI's previous flagship reasoning model, built for coding and agentic tasks with configurable reasoning effort. It allows 128,000 output tokens and takes text and image input. OpenAI recommends GPT-5.1 for new workloads.

GPT-OSS-120b is OpenAI's open-weight mixture-of-experts reasoning model under the Apache-2.0 license. It has 116.8B total and 5.1B active parameters and a 131,072-token context window. It supports function calling and runs on one 80 GB GPU with MXFP4 weights.

GPT-OSS 20B is OpenAI's open-weight reasoning model for agentic tasks and low-latency edge use, under Apache-2.0. It is a mixture-of-experts model with 20.9B total and 3.6B active parameters and a 131,072-token context. It runs within 16 GB of memory with MXFP4 weights.

o3-pro is OpenAI's version of o3 that uses more compute to think longer on hard problems. It accepts text and image input, with a 200,000-token context window and up to 100,000 output tokens. It supports tool use and structured outputs.

o3 is OpenAI's reasoning model for math, science, coding and visual analysis. It thinks before it answers and accepts text and image input. It supports tool use and structured outputs, with a 200,000-token context window and up to 100,000 output tokens.

Qwen3 Coder Next is Qwen's open-weight causal language model for coding agents and local development workflows. It has a 262,144-token context window and supports function calling and streaming.

Qwen3.8 2.4T A95B is Qwen's sparse mixture-of-experts model, the open-weight variant of Qwen3.8 Max. It has 95 billion active of 2.4 trillion total parameters.

Qwen3.8 Flash is Alibaba Qwen's native vision-language model for coding, office work, long-context reasoning and agents. It has a 1M context window, takes text, image and video input, and supports thinking and function calling.

Inkling is Thinking Machines Lab's first open-weights foundation model. It is a 975B-parameter multimodal mixture-of-experts transformer with 41B active. It takes text, image, and audio input, with switchable reasoning effort and tool use.

xAI's Grok 4.20 multi-agent snapshot (March 2026), optimized for agentic workflows, tool use, and coordinated multi-step task planning and execution.

xAI's Grok 4.3 is an iterative update from the Grok 4 series offering improved instruction-following, general reasoning, and broad conversational capabilities.

Grok 4.5 is SpaceXAI's model with frontier performance on coding, knowledge work, and STEM. It takes text and image input, returns text, and supports function calling and streaming.

Grok 4.6 is SpaceXAI's model with frontier performance on coding, knowledge work, and STEM. It builds on Grok 4.5 for long-running agents and visual work, with text and image input and function calling.

Grok 4.7 is SpaceXAI's frontier model for coding, agentic tasks, and knowledge work. It succeeds Grok 4.6 with mandatory adjustable reasoning at low, medium, high, or xhigh effort, defaulting to high. It takes text and image input.

MiMo V2.6 Flash is Xiaomi's full-modality reasoning model for high-frequency invocations and large-scale professional workloads. It keeps the 128K output limit of MiMo V2.6 Pro, with text, image and video input.

GLM-4.6 is Z-AI's open-weight mixture-of-experts model with 355B total and 32B active parameters, under the MIT license. It has a 200K context window, up from 128K, and a 128K max output. It improves coding, tool use, and search-based agents.

GLM-4.7 Flash is an open-weight fast multilingual text generation model from Z-AI with a 131,072 token context window. It is optimized for dialogue, instruction following, and multi-turn tool calling across 100+ languages.

GLM-5.2 is Z-AI's open-weight flagship reasoning model with a 128K output window. It supports controllable thinking effort, function calling, and MCP integration. It targets long-horizon autonomous coding and agentic tasks.

GLM-5.3 is Z.AI's open-weight reasoning model for coding and agentic workflows. It targets complex software engineering, long-running agents, and vulnerability analysis. It uses the same base model as GLM-5.2 with scaled post-training for stronger coding and token efficiency.