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Claude Fable 5.1 is Anthropic's successor to Claude Fable 5 for long-running agentic coding, knowledge work and research. It has a 1M-token context window, 128K max output, tool use, and text, image and file input.

Claude Fable 5.2 is Anthropic's frontier model for the most demanding technical work, with gains over Fable 5.1 in coding, reasoning and vision. It supports text and image input, a 1M-token context window, 128K max output and always-on adaptive thinking.

Claude Fable 5 is Anthropic's Mythos-class model for autonomous knowledge work and coding. It has a 1M-token context window, 128K max output, tool use, and text, image and file input.

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 Opus 5.5 is Anthropic's successor to Claude Opus 5 for long-running agentic coding and knowledge work. It has a 1M-token context window, vision input, tool use and always-on adaptive thinking, suited to multi-step changes in large codebases.

Claude Opus 5 is Anthropic's flagship model for demanding reasoning, coding and long-horizon agentic work. It handles end-to-end software tasks, code review, bug finding and chart analysis.

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.

Claude Sonnet 5 is Anthropic's agentic model with strong coding, tool use and reasoning. It has a 1M-token context window, 64,000 max output tokens, and takes text, image and file input.

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-R1 is DeepSeek's open-weight reasoning model, a 671B mixture-of-experts with 37B active parameters and a 131,072-token context. It is trained with large-scale reinforcement learning and released under the MIT license. It targets math, code and reasoning.

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 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.

DeepSeek V4.1 Flash is the open-weight, cost-efficient sparse mixture-of-experts tier of the V4.1 family. It has 552B total parameters with 8B active on input and 16B on output, 1M context, and native vision in with text out. This is the original September 2026 checkpoint.

Ultramarine 1 Flash is a multimodal model from Paper Instruments for knowledge work in enterprise operations, finance, law and consulting. It is built on GLM 5.3 Flash (320B total, 18B active MoE), with a 1M-token context window and image input. Function calling is not supported.

Gemini 2.5 Flash-Lite is Google's lightest Gemini 2.5 Flash variant, built for high-throughput inference. It has a 1M-token context window and takes text, image, video, and audio input. It supports tool calling and coding.

Gemini 2.5 Flash is Google's fast Gemini model with strong reasoning, designed for high-throughput tasks. It takes text and image input and generates text. It supports tool calling and coding.

Gemini 2.5 Pro is Google's advanced Gemini model for deep reasoning over long context. It accepts text, image, video, and audio input and generates text. It fits complex research, coding, and analytical workflows.

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.

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.

Llama 3.1 8B Instruct is Meta's compact instruction-tuned model, optimized for fast inference and edge deployments. It supports chat, reasoning, coding, function calling, and streaming, with text in and text out.

Muse Glimmer 30B is Meta's dense multimodal instruct model with about 30B parameters, distilled from Muse Spark. It takes text and image input and returns text, with reasoning and function calling. Weights are open under Apache 2.0.

Muse Spark 1.3 is Meta's multimodal reasoning model for complex agentic tasks. It accepts text, images, video, audio, and PDF documents, returns text, and has a 1M-token context window. Audio understanding is not fully supported, so use Muse Spark 1.2 for audio.

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.

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 Medium 3.5 128B is an open-weight 128B-parameter Mixture-of-Experts model balancing capability and efficiency. It is strong at reasoning, coding and multilingual tasks, and supports function calling with text input and output.

Kimi K2.5 is Moonshot AI's open-weight mixture-of-experts model with native vision, tool calling and structured outputs. It has 1T total and 32B active parameters, a 256K context window and a Modified MIT license.

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 K2.7 Code is Moonshot AI's open-weight coding-focused model for end-to-end programming tasks over long contexts. It is a native multimodal mixture-of-experts model with 32B active of roughly 1T total parameters and always-on thinking.

Kimi K3.1 Pro Highspeed is Moonshot AI's high-speed variant of Kimi K3.1 Pro, running at about 2-3x the output speed. It has a 1M-token context window, native visual understanding and always-on thinking, and suits coding agents and latency-sensitive work.

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.

Nemotron-3 Nano 30B is a compact 30B-parameter text model from NVIDIA's Nemotron-3 family. It is tuned for fast, efficient text generation and understanding, with chat and streaming.

Nemotron-3 Nano Omni 30B-A3B (Reasoning) is NVIDIA's multimodal mixture-of-experts model for video, audio, image and text understanding. It targets enterprise Q&A, summarization, transcription and OCR, and supports reasoning and function calling.

Nemotron-3 Super 120B is NVIDIA's 120B-parameter reasoning model from the Nemotron-3 family. It supports tool calling and coding, and takes text input and returns text.

Nemotron 3 Ultra 550B is NVIDIA's 550B-parameter frontier model with 55B active, on a LatentMoE hybrid of Mamba-2, MoE and Attention. It has configurable reasoning through enable_thinking. It suits agentic workflows, long-context analysis and tool use.

Nemotron 3.5 Content Safety is NVIDIA's open-weight 4B-parameter multimodal guardrail model, fine-tuned from Google Gemma-3-4B. It moderates inputs to and responses from LLMs and VLMs. It returns a safety classification, category labels and optional reasoning.

Nemotron 3.5 Lightning 30B A3B is NVIDIA's sparse mixture-of-experts chat model, optimized for low-latency agentic workloads. It supports function calling and coding. It allows up to 131,072 output tokens.

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 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-5.5 Pro is OpenAI's Pro version of GPT-5.5, built for agentic workflows with built-in tools, improved reasoning and stateful context management. It accepts text and image input and supports coding, streaming and function calling.

GPT-5.5 is OpenAI's flagship model for coding, reasoning and multimodal work, with configurable reasoning effort from none to xhigh. It accepts text and image input. It supports streaming and function calling.

GPT-5.6 Luna is OpenAI's reasoning-focused model in the GPT-5.6 family, built for cost-sensitive workloads. It accepts text and image input. It supports streaming and function calling.

GPT-5.6 Sol is OpenAI's reasoning-focused model in the GPT-5.6 family, with configurable reasoning effort and image understanding. It accepts text, image and file input. It supports streaming and function calling.

GPT-5.6 Terra is OpenAI's reasoning-focused model in the GPT-5.6 family that balances intelligence and cost. It accepts text and image input. It supports streaming and function calling.

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-6 Astra is OpenAI's reasoning and chat model. It accepts text and image input and supports streaming and function calling.

GPT-6 Luna is OpenAI's efficient GPT-6 tier for focused, high-throughput tasks. It has configurable reasoning, tool use, image input. It supports streaming and function calling.

GPT-6 Sol is OpenAI's model for complex coding and agentic workflows. It has configurable reasoning, tool use, image input. It is the high-end GPT-6 tier below GPT-6 Astra.

GPT-6.1 Sol is OpenAI's model for complex coding and agentic work, with performance close to GPT-6 Astra. It has configurable reasoning, tool use, and image input.

GPT-6.1 Terra is OpenAI's fast, efficient model for visual reasoning, precise computer use and clear, natural writing. It has configurable reasoning, tool use, image input and a 1.05M-token context window.

o1 is OpenAI's reinforcement-learning reasoning model for math, science, coding and multi-step analysis. It produces a long internal chain of thought before answering. It has a 200,000-token input context and 100,000 output tokens, with text and image input.

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.

o4-mini is OpenAI's o-series reasoning model for coding and visual tasks, succeeded by GPT-5 Mini for new workloads. It has a 200,000-token input context and 100,000 output tokens, with text and image input. It supports function calling and structured outputs.

Particle-1 is Particle AI's token-efficient agentic coding model, post-trained on DeepSeek V4.1 Flash. It targets terminal workflows and codebase changes, with a 1M-token context window, image input, tool calling and reasoning.

Qwen Plus is Alibaba Cloud's balanced general-purpose Qwen text model on Model Studio. It covers text understanding, generation and reasoning at a moderate price, with tiered pricing by input length.

Qwen3 235B A22B (Instruct 2507) is Qwen's open-weight, non-thinking Mixture-of-Experts model with 235B total and 22B active parameters. It is licensed Apache-2.0 and covers 119 languages and dialects, coding and tool use.

Qwen3 Coder 480B A35B is Qwen's open-weight coding model with 480B total and 35B active parameters. It targets agentic coding, tool calling and repository-scale work, with a native 262K context window under Apache-2.0.

Qwen3 Coder Flash is the fast coding model of Alibaba's Qwen3-Coder family on Model Studio. It trades some depth for speed in code completion and agentic coding tools.

Qwen3.5-27B is Alibaba's largest open-weight dense Qwen3.5 model. It has a thinking mode, tool calling, multi-token prediction and 201 languages, with text, image and video input.

Qwen3.5-9B is an open-weight model from Alibaba's Qwen3.5 series with a hybrid Gated Delta Networks and sparse MoE architecture. It has a thinking mode, tool calling and 201 languages.

Qwen3.6 Plus is Alibaba's plus-tier multimodal model of the Qwen3.6 generation. It takes text and image input, has a 1 million token context window, and supports reasoning, coding and tool use.

Qwen3.7 Max is Alibaba's flagship max-tier model of the Qwen3.7 generation. It takes text and image input, has a 1 million token context window, and supports reasoning, coding and tool use.

Qwen3.7 Plus is Alibaba's advanced multimodal model with text and image input. It has a 1 million token context window and strong reasoning capabilities.

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.

Qwen3.8-27B is Alibaba's open-weight dense Qwen3.8 model for agentic coding and complex reasoning, with a native thinking mode. It targets multi-step agent work and large coding tasks.

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.

Qwen3.8 Max is Alibaba's flagship 2.4-trillion parameter MoE model of the Qwen3.8 generation. It takes text, image and video input, has a 1 million token context window, and supports reasoning, coding and tool use.

Fugu Ultra is Sakana AI's flagship orchestration model. It coordinates a deep pool of expert agents and frontier models behind one OpenAI-compatible API, then synthesizes the result. It supports Chat Completions, Responses, and web_search.

Step 5 Preview is StepFun's open-weight flagship base model for real-world tasks, with a focus on programming and professional expertise. It is multimodal, taking text, image and video in and text out, with a 1M-token context and adjustable reasoning effort.

Step 3.7 Flash is StepFun's open-weight 1T parameter multimodal Mixture-of-Experts model for long-horizon coding, agentic tool use, and image and video understanding. It takes text and image input and supports structured output.

Hy4 Preview is Tencent Hunyuan's open-weight 770B mixture-of-experts model with 49B active parameters, built for agents and coding. It targets office automation and complex tool use.

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 4.3 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.

Grok Build 0.1 is xAI's coding and automation model. It takes text and image input and supports function calling, structured outputs, and reasoning.

MiMo V2.5 Pro is Xiaomi's open-weight flagship model for agentic work, complex software engineering, and long-horizon tasks. It accepts text, image and video input. It completes professional tasks involving thousands of tool calls.

MiMo V2.5 is Xiaomi's open-weight native omnimodal model with Pro-level agentic performance. It accepts text, image and video input, improves on MiMo V2 Omni in multimodal perception, and suits agent frameworks.

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.

MiMo-V2.6-Pro UltraSpeed is the fast edition of Xiaomi's flagship MiMo-V2.6-Pro, built from the same 1T checkpoint with roughly 10x faster output. It accepts text, image, video and audio input and has a 1M-token context window.

MiMo V2.6 Pro is Xiaomi's trillion-parameter, natively omni-modal flagship reasoning model. It targets complex projects, long-horizon agentic tasks, coding, research, and professional knowledge work, with up to 128K output tokens.

GLM-4.5 is Z-AI's open-weight foundational model for agent-oriented applications. It is a mixture-of-experts model with 355B total and 32B active parameters, trained on 15 trillion tokens. It has a 128K context window, hybrid thinking modes, and strong tool use and coding.

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 is Z-AI's open-weight multilingual text generation model with a 131,072 token context window. It supports chat, instruction following, multi-turn tool calling, and reasoning across 100+ languages.

GLM-5.1 is Z-AI's open-weight flagship foundation model for long-horizon autonomous tasks. It has a 128K output window and scores 58.4 on SWE-Bench Pro. It supports thinking mode, function calling, and MCP integration.

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-FlashX is Z.AI's high-speed inference variant for coding agents, real-time interactions, and long-running agentic workflows. It takes text, image, and video input and returns text.

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.

GLM-5 is Z-AI's open-weight general-purpose foundation model for reasoning, coding, and agentic workloads. It has a 128K context window and 32K output. It supports thinking mode, function calling, and structured outputs.