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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 Fast is the fast-mode variant of Anthropic's Claude Opus 5, tuned for higher output speed. It has the same capabilities and a 1,000,000-token context window, with tool use and text, image and file input.

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.

Trinity Large Thinking is Arcee AI's open-weight reasoning model. It is a 398B sparse mixture-of-experts with about 13B active parameters per token, built for tool calling, multi-step planning and agent workflows. It writes its reasoning in think blocks before the answer.

Command A is Cohere's flagship model for agentic, multilingual, and enterprise use cases. It is optimized for tool use, retrieval-augmented generation, and complex reasoning, supports 23 languages, and offers a 256K-token context window. Released open-weight for research use.

Laya is Convai Innovations' System One evaluation model. It takes shared state and typed questions (noul, choice, score) and returns structured answers with probabilities in one forward pass. It does not generate chat text.

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-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.

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.

Glimpse is Elorian's visual reasoning model for image and video understanding. It tracks objects across motion and viewpoints and reasons about spatial relationships and physical cause and effect. It takes text, image, and video input and answers in text.

Ember-1 is a reasoning model from Fireworks Research built on Kimi K3. It is tuned to produce shorter reasoning traces, with tool use, image input, and a 1M-token context window.

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.

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.

Gemini 4 Argon is Google's frontier model for long-horizon software engineering, enterprise knowledge work, and cybersecurity defense. It accepts text, image, and video input.

Gemini 4 Carbon is Google's strongest model, built for long-horizon agentic work such as multi-step coding and debugging. It reads text, images, and video and has a 1,048,576-token context window. It also targets knowledge work, research, and cybersecurity.

Gemini 4 Hydrogen is the speed and efficiency model of Google's Gemini 4 family, built for everyday coding and agentic workflows. It reads text, images, video, audio, and PDFs, answers in text, and has a 1,048,576-token context window with adjustable thinking levels.

Gemini Decision 1 is Google's decision model for the Gemini Decisions API. It scores yes/no, choice, and rubric questions on one text, image, audio, video, or PDF input in a single pass, with a 262,144-token context window.

Gemini Embedding 2 is Google's first multimodal embedding model, mapping text, images, video, audio and PDFs into one 3,072-dimension space. This listing takes text input only, with an 8,192-token window.

Gemma 4 26B A4B is an open-weight model from Google's Gemma 4 family, built from Gemini 3 research to maximize intelligence per parameter. It takes text and image input and generates text. It supports reasoning, tool calling, and coding.

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.

Ampersand v0.1 is humans&'s collaborator model for group chats, trained from GLM 5.3. It tracks what each person wants, flags conflicts, shares context, and asks questions. It takes text in and text out, with reasoning effort, function tools and a 1M-token context window.

Mercury 2.5 is Inception Labs' diffusion language model for fast text generation and reasoning. It has a 260,000-token context window and supports tool calling and a thinking mode.

Mercury 3 is Inception's diffusion language model, the lab's most capable to date, built for agentic workflows and real-time applications. It supports tunable reasoning, tool calling, and structured output. The context window is 1M tokens.

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.

Ling-3.0-flash-Sante is inclusionAI's open-weight health and medicine mixture-of-experts model, built on Ling-3.0-flash with 124B total and 5.1B active parameters. It targets medical knowledge reasoning, clinical safety and evidence-based retrieval. It keeps coding skills.

Ling-3.0-flash-VL is inclusionAI's open-weight native multimodal mixture-of-experts model with 124B parameters and about 5.5B active per token. It adds image and video understanding. It suits visual reasoning, document and chart reading, and agentic GUI tasks.

Ling-3.0-flash is inclusionAI's open-weight instruct mixture-of-experts model with 124B parameters and about 5.1B active per token. It is built for token efficiency and production-scale agentic inference. It suits coding, document processing and lightweight agent workflows.

Ling-3.1-flash is inclusionAI's open-weight hybrid reasoning mixture-of-experts model with 25B active parameters out of 560B total. It is a text model with streaming, function calling, coding and agentic use.

Schematron V2 Small is inference.net's 3B extraction model that turns messy HTML into clean JSON matching a schema you supply. It is the family's highest-quality tier, with a 128K-token context and strict JSON schema output.

Schematron V2 Turbo is inference.net's 3B extraction model that turns messy HTML into clean JSON matching a schema you supply. It is the family's throughput-optimized option, faster than V2 Small, with a 128K-token context and strict JSON schema output.

LFM 2.5 24B A2B is Liquid AI's open-weight reasoning mixture-of-experts model with 24B total and 2.45B active parameters. It always reasons before it answers and supports tool use and coding. It has a 131,072-token context window and uses the LFM Open License v1.0.

LongCat-2.5 is Meituan's open-weight natively multimodal agentic model with a 1M-token context window. It is a sparse mixture-of-experts with 1.6T total and about 48B active parameters. It takes text and image input and returns text, for long-horizon agentic and visual coding.

Llama 3.1 70B Instruct is Meta's instruction-tuned model for chat, reasoning, and multilingual use. It supports coding, function calling, and streaming, with text in and text out.

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.

Llama 4 Scout 17B 16E is Meta's open-weight multimodal mixture-of-experts model with 17B active and 109B total parameters. It takes text and image input and has a 10M-token input context. It fits on a single H100 GPU.

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 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 1.1 is Meta's original multimodal reasoning model in the Muse Spark family, built for agentic tasks. It takes text, image, video, audio and file input, returns text, and has a 1M-token context window. It supports function calling and structured output.

Muse Spark 1.2 Contributor is the Contributor-mode tier of Meta's Muse Spark 1.2 reasoning model, for experimentation and early agentic work. Meta may use your prompts, files, and responses to train future models, so avoid confidential work.

Muse Spark 1.2 is Meta's multimodal reasoning model for complex agentic tasks. It accepts text, images, video, audio, and PDF documents, returns text, and offers a 1M-token context window.

Muse Spark 1.3 Contributor is the Contributor-mode tier of Meta's Muse Spark 1.3 reasoning model, for experimentation and early agentic work. Meta may use your prompts, files, and responses to train future models, so avoid confidential work.

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 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.

Microsoft-Decision-1 is a small 9B Microsoft model built for fast decisions on yes/no, multiple-choice, and rating questions. It returns a calibrated probability for each fixed answer option, not generated chat text. It has a 32,768-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 M2.7 is MiniMax's large language model for complex software engineering, agentic tool use and office productivity workflows. It supports complex agent harnesses, dynamic tool search and Agent Teams. It takes text and supports function calling.

MiniMax M2 is a large language model for complex software engineering, agentic tool use, and office productivity workflows. Supports complex agent harnesses, dynamic tool search, Agent Teams, and high-fidelity coding and document-editing tasks.

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.

MiniMax M3.1 Flash is MiniMax's open-weight multimodal model for complex agentic tasks, coding and cybersecurity. It accepts text, image and video input and has a 1M-token context window. It always thinks, with adjustable effort, and its weights are not yet released.

Mistral Large 3 is Mistral AI's open-weight multimodal Mixture-of-Experts model with 675B total and 41B active parameters. It takes text and image input, supports tool use and JSON output, and has a 256K-token context. License is Apache 2.0.

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 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 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.

Nemotron 3.5 Super VL 120B A12B is NVIDIA's vision-language model for reasoning, coding and tool use. It is a hybrid Mamba-2 and Transformer mixture-of-experts with 120B total and 12B active parameters. It takes text, image and video input.

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 Nano is OpenAI's fastest, most cost-efficient GPT-4.1 model, built for low-latency instruction following and tool calling at high volume. It has a 1,047,576-token input context and 32,768 output tokens, with text and image input.

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 Codex is OpenAI's version of GPT-5 for agentic coding in Codex and similar environments. It has a 400,000-token input context and 128,000 output tokens, with text and image input. It supports reasoning tokens, 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 Codex Max is OpenAI's maximum-effort variant of GPT-5.1 Codex for long-horizon agentic coding in the Codex environment. It has extended context and reasoning support, accepts text and image input, and supports streaming and function calling.

GPT-5.1 Codex Mini is OpenAI's smaller version of GPT-5.1 Codex for agentic coding where efficiency matters more than peak capability. It has a 400,000-token input context and 128,000 output tokens, with text and image input. It supports function calling and reasoning tokens.

GPT-5.1 Codex is OpenAI's version of GPT-5.1 for agentic coding in the Codex environment. It has a 400,000-token input context and 128,000 output tokens, with text and image input. It supports reasoning tokens, streaming, function calling and structured outputs.

GPT-5.2 Codex is OpenAI's Codex-environment variant of GPT-5.2, tuned for agentic coding workloads. It accepts text and image input and supports reasoning, 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-5.5 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.

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.

GPT-OSS Safeguard 20B is OpenAI's open-weight safety reasoning model built on gpt-oss-20b (Apache 2.0). It is a 21B-parameter MoE tuned for content classification, input and output filtering, and trust and safety judgments with reasoning traces.

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.

Laguna S 2.1 is Poolside's 118B-parameter mixture-of-experts coding model with 8B parameters active per token. It supports tool calling, and thinking and no-thinking modes. Open-weight under the OpenMDW-1.1 License.