Fluence (BYOK)BYOK
About Fluence (BYOK)
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Models
Aion-2.0 is a variant of DeepSeek V3.2 tuned for immersive roleplaying and storytelling. It has a 131,072-token context window and supports reasoning, streaming and function calling with text input and output.
Aion 3.0 Mini is Aion Labs' reasoning model built on the DeepSeek family. Several specialized models contribute to each response, giving stronger narrative structure and a more nuanced treatment of darker themes, so it suits fiction. Context is 131,072 tokens.
Aion-3.0 is AionLabs' multi-model roleplaying and storytelling system, built on the GLM family of models. It has a 131,072-token context window and supports reasoning, streaming and function calling with text input and output.
Aion 3.5 Mini is Aion Labs' compact reasoning model built on the GLM family. Several specialized models contribute to each response, which suits narrative writing. It has a 262,144-token context window and takes text input.
Aion 3.5 is Aion Labs' reasoning model built on the GLM family of models. Several specialized models contribute to each response, giving stronger narrative structure and a more nuanced treatment of mature and darker themes, so it suits fiction writing. Context is 262,144 tokens.
Aion-RP 1.0 (8B) is a Llama 3.1 8B roleplaying model with a 32,768-token context window. It ranks highest in the character evaluation portion of the RPBench-Auto benchmark. It takes text input and streams text output.
Nova 2 Lite is Amazon's fast reasoning model for everyday workloads. It accepts text, image, video, and file input and generates text. It has a 1,000,000-token context window and supports tool calling.
Nova Lite 1.0 is Amazon's fast multimodal model for image, video, and text inputs. It generates text output and has a 300,000-token context window. It supports tool calling and streaming.
Nova Pro 1.0 is Amazon's multimodal model that balances accuracy, speed, and cost across a wide range of tasks. It takes text and image input and generates text. It has a 300,000-token context window and supports tool calling.
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 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 4.1 is Anthropic's model for complex analysis, advanced coding and multi-step reasoning. It supports tool use and streaming, with a 200,000-token context window and text, image and file input.
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.
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.
Seed 1.6 is ByteDance Seed's general-purpose multimodal model with adaptive deep thinking. It takes text, image and video input and supports tool use.
Seed 2.1 Turbo is ByteDance Seed's multimodal model for coding and long-horizon agent workflows. It takes text, image and video input, supports tool use, and has a 262,144-token context window.
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.
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.
Command R+ (08-2024) is Cohere's update of Command R+ with roughly 50% higher throughput and 25% lower latencies than the previous version. It is a text model with a 128K-token context window, streaming, and function calling.
DeepSeek V3.1 is DeepSeek's hybrid reasoning model with 671B parameters and 37B active. It supports both thinking and non-thinking modes through prompt templates. It also supports streaming and function calling.
DeepSeek V3 is DeepSeek's text chat model, building on the instruction following and coding abilities of earlier versions. It supports streaming and function calling.
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 Terminus is an open-weight update to DeepSeek V3.1. It keeps the original capabilities while addressing user reports on language consistency and agent capabilities. It supports reasoning, streaming and function calling.
DeepSeek V3.2 Exp is an experimental DeepSeek model built on V3.1-Terminus that adds DeepSeek Sparse Attention to improve long-context training and inference efficiency. It matches V3.1-Terminus on public benchmarks, and is released with open weights under the MIT license.
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.
Gemma 3 4B IT is Google's instruction-tuned 4B open model with text and image input. It has a 128K context window and multilingual support in over 140 languages. It supports tool calling.
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.
Granite 4.2 8B is the open-weight, mid-size dense reasoning model in IBM's Granite 4.2 family. It offers full, low-effort and non-thinking modes plus tool calling. Context window is 131,072 tokens, with up to 117,964 output tokens.
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 2 is Inception's extremely fast reasoning LLM, and the first reasoning diffusion LLM (dLLM). It has a 128,000-token context window, supports function calling and streaming, and takes text in and returns text.
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 2.6B is Liquid AI's compact 2.6B-parameter chat model for general assistant and coding tasks. It allows up to 32,768 output tokens. It takes text in and streams text out.
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-2.0 is Meituan's open-weight sparse mixture-of-experts language model with 1.6T total and 48B active parameters. It targets coding, repository-level changes, and agentic workflows. It supports streaming, function calling, and structured output.
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 3.3 70B Instruct is Meta's open-weight instruction-tuned model with 70B parameters and a 131072-token context window. It is tuned for multilingual dialogue, with tool use on par with Llama 3.1 70B. It suits chat, reasoning, and coding.
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.
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.
Llama Guard 4 12B is Meta's natively multimodal safety classifier with 12B parameters. It labels LLM prompts and responses as safe or unsafe and lists violated categories from the MLCommons hazards taxonomy. Text and image go in, text comes 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 1.1 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.
Phi 4 is Microsoft Research's open-weight model for complex reasoning that also runs efficiently with limited memory or when quick responses are needed. It has a 16,384-token context window and takes text in and gives text out.
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 M1 is MiniMax's large-scale open-weight reasoning model, designed for extended context and high-efficiency inference. It is a text model with a 1M-token context window, streaming and function calling.
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.
Codestral 2508 is Mistral's coding model, released at the end of July 2025. It specializes in low-latency, high-frequency tasks such as fill-in-the-middle, code correction and test generation. Context is 256,000 tokens, with function calling.
Ministral 3 14B 2512 is the largest model in the Ministral 3 family, with performance comparable to Mistral Small 3.2 24B. It takes text and image input, supports function calling and has a 262,144-token context window.
Ministral 3 3B 2512 is the smallest model in the Ministral 3 family, a small, efficient language model with vision. It takes text and image input, supports function calling and has a 131,072-token context window.
Ministral 3 8B 2512 is a balanced model in the Ministral 3 family, a small, efficient language model with vision. It takes text and image input, supports function calling and has a 262,144-token context window.
Mistral Large is Mistral AI's flagship model, the Mistral Large 2 release (mistral-large-2407), with a 128,000-token context window. It targets reasoning, code, JSON and chat, and supports function calling. It is a proprietary weights-available model.
Mistral Medium 3.1 is an updated version of Mistral Medium 3, a high-performance enterprise-grade language model. It takes text, image and file input, supports function calling and has a 131,072-token context window.
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.
Mistral Medium 3 is a high-performance enterprise-grade language model. It takes text, image and file input, returns text, supports function calling and has a 131,072-token context window.
Mistral Nemo is a 12B-parameter model built by Mistral in collaboration with NVIDIA. It has a 128k-token context length and supports function calling and streaming with text input and output.
Mistral Saba is a 24B-parameter language model designed for the Middle East and South Asia. It aims for accurate, contextually relevant responses with efficient performance. It has a 32,768-token context and supports function calling.
Mistral Small 3 is an open-weight 24B-parameter language model optimized for low-latency performance across common AI tasks. It has a 32,768-token context window and takes text input with streamed text output.
Mistral Small 3.1 24B Instruct builds on Mistral Small 3 (2501) and adds vision understanding. It handles long context up to 128k tokens, has 24 billion parameters, takes text and image input and supports function calling.
Mistral Small 3.2 24B Instruct is an open-weight 24B instruction model under Apache 2.0, with a 131k context window. It improves instruction following and function calling over Mistral Small 3.1 and takes text and image input.
Moondream 3 Preview is Moondream's vision-language model with a mixture-of-experts architecture, 9B total and 2B active parameters. It answers questions about images, captions them and reads text, with a 32K-token context. Moondream 3.1 is its successor.
Moondream 3.1 9B A2B is Moondream's mixture-of-experts vision-language model with 9B total and 2B active parameters. It handles object detection, pointing, OCR and structured output, with a 32,768-token context window and up to 28,672 output tokens.
Kimi K2 Thinking is Moonshot AI's open-weight reasoning model, extending the K2 series into agentic, long-horizon reasoning. It supports tool use and streaming, with a 256,000-token context window.
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 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.
Morph V3 Fast is Morph's fastest apply model for code edits. It runs at about 10,500 tokens/sec with 96% accuracy for rapid code transformations.
Morph V3 Large is Morph's high-accuracy apply model for complex code edits. It runs at about 4,500 tokens/sec with 98% accuracy for precise code transformations.
Nex-N2.5-Mini is Nex AGI's smaller agentic coding model, a MoE with 35B total and 3B active parameters. It is built for high-speed instruction following, real-time tool execution, and visually assisted computer use.
Nex-N2.5-Pro is Nex AGI's larger agentic coding model, a MoE with 397B total and 17B active parameters. It is built to turn goals into working, verified outcomes with a visual feedback loop and takes text and image input.
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 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.
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 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-5.1 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-5.1 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 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 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 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 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.
Sonar Pro is Perplexity's multimodal chat model with a 200,000-token context window. It accepts text and image input and streams text output.
Sonar is Perplexity's lightweight, fast chat model with citations and customizable sources. It accepts text and image input and has a 127,000-token context window.
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.
Ternary Bonsai 2 27B is PrismML's 27B reasoning model, derived from Qwen3.8-27B and compressed to ternary weights. It supports coding, math, tool calling and image input, with a 262K-token context window.
Ternary Bonsai 2 9B is PrismML's compact open-weight vision-language model, derived from Qwen3.5-9B with ternary weights. It handles conversation, reasoning and tool use, takes image input and has a 262K-token context window.
Qwen2.5 7B Instruct is Qwen's 7B text model for chat, with a 32,768-token context window. It supports streaming and function calling, and takes text in and returns text out.
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.
Qwen2.5-72B-Instruct is Alibaba Qwen's open-weight 72.7B-parameter general chat model. It is stronger at coding and maths than Qwen2, supports 29+ languages and handles function calling.
Qwen 2.5 Coder 32B is Alibaba's open-weight specialized coding model with 32B parameters. It targets code generation, completion and debugging across 92 programming languages, with function calling and streaming.
Qwen2.5 VL 72B Instruct is Qwen's open-weight 72B vision-language model. It takes text and images and answers in text, and reads charts, icons, graphics and page layouts.
Qwen3-14B is an open-weight 14.8B-parameter dense model from Alibaba Qwen that balances quality and latency. It supports reasoning, function calling, 100+ languages and dialects, instruction following and translation.
Qwen3 235B A22B Thinking 2507 is Qwen's open-weight Mixture-of-Experts model tuned for complex reasoning, with a 128,000-token context window. It supports function calling and streaming, and takes text in and returns text out.
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 30B A3B Instruct 2507 is Qwen's open-weight 30.5B-parameter mixture-of-experts language model with 3.3B active parameters per inference. It has a 262,144-token context window and supports function calling and streaming.
Qwen3-8B is Alibaba Qwen's open-weight small dense model with 8B parameters. It is fast and efficient, supports reasoning, and suits lightweight chat and tool use.
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 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 Coder 480B A35B is Qwen's Mixture-of-Experts code generation model with 480B total and 35B active parameters. It has a 262,144-token context window and supports function calling and streaming.
Qwen3 Next 80B A3B Instruct is an open-weight, instruction-tuned chat model in Qwen's Qwen3-Next series, built for fast, stable responses without thinking traces. It has a 262,144-token context window and supports function calling and streaming.
Qwen3 VL 235B A22B Instruct is Qwen's open-weight multimodal model that combines text generation with visual understanding of images and video. It has a 262,144-token context window and supports function calling.
Qwen3 VL 30B A3B Instruct is Qwen's open-weight multimodal model that combines text generation with visual understanding of images and videos. It targets instruction following and supports function calling.
Qwen3.5-122B-A10B is Qwen's open-weight native vision-language model with a hybrid architecture. It combines linear attention with a sparse mixture-of-experts, takes text, image and video input.
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.
Qwen3.5-35B-A3B is Qwen's open-weight native vision-language model with a hybrid of linear attention and a sparse mixture-of-experts. It takes text, image and video input, has a 262,144-token context window and supports reasoning.
Qwen 3.5 397B A17B is Alibaba's open-weight mixture-of-experts model with 397B total and 17B active parameters. It supports reasoning and function calling, and takes 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 Flash is the fast, low-cost native vision-language model of Alibaba's Qwen3.6 series on Model Studio. It improves on Qwen3.5 Flash in agentic coding, math and code reasoning, spatial understanding and object localization.
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 Flash is Alibaba's fast vision-language reasoning model for multimodal agents. It suits visual coding, search and computer interaction, with strengths in object recognition, spatial understanding and real-world visual perception.
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 Prime is a higher-throughput variant of Qwen3.8 Max from Alibaba's Qwen team. It takes text, image and video input and supports reasoning 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.
Qwen3.8 Omni Flash is Alibaba Cloud Qwen's native multimodal model for text, image, audio and video input at 1M context. It is built for agentic programming, knowledge work, GUI operation and audio and video workflows.
Beam 1.0 is Reflection's open-weights mixture-of-experts model with 501B total and 23B active parameters. It is built for token-efficient reasoning, agentic tool use, and software engineering. Weights are Apache-2.0.
Fugu Max is the largest tier of Sakana AI's Fugu line. It has a 1M-token context and takes text, images, and files as input and returns text. It is a distinct tier alongside Fugu and Fugu Ultra.
Fugu Ultra v2 is the higher-performance model in Sakana AI's Fugu family, a learned multi-agent orchestration system. It takes text, images, and files as input and returns text, with reasoning and function calling.
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.
L3.1 Euryale 70B is Sao10k's text-generation model for creative roleplay. It is the successor to Euryale L3 70B v2.1 and takes text input with streamed text output.
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 3.5 Flash is StepFun's open-weight sparse Mixture-of-Experts model with 196.81B total parameters and about 11B active per token. It uses a 45-layer transformer with 288 routed experts, Top-8 selection, and 256K context for reasoning and agentic tasks.
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.
Hy4 is Tencent Hunyuan's 1M-token-context model for agents, coding and long-horizon work, the official release after Hy4 Preview. It accepts text and image input, supports tool calling and structured outputs, and has optional reasoning on by default.
Inkling Small is an open-weight multimodal mixture-of-experts model from Thinking Machines Lab. It has 12B active parameters out of 276B total. It takes text, image, and audio input and returns text.
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.
Pareto 26.10 is Unbiased's composite model that blends several language models into one answer. It takes text and image input and returns text over a 1,048,576-token context window, with 131,072 max completion tokens. It is aimed at research, coding and agentic workflows.
Pareto 26.9 is the September 2026 release of Unbiased's blended model that combines frontier and open models into one answer. It takes text and image input with a 262,144-token context. It targets research, coding and agentic workflows and was superseded by Pareto 26.10.
Solar Mini 4 is Upstage's small, fast tier in the Solar 4 generation. It is a text-only model with a 524,288-token context window. It supports function calling and reasoning, and is a separate model from Solar Pro 4.
Solar Pro 4 is Upstage's cost-efficient large language model with a 524K context window. It is built for long-horizon tasks and agentic workflows, and supports function calling and reasoning with text input and output.
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.
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 Air is the open-weight, lightweight variant of Z.ai's GLM-4.5 flagship family, built for agent-centric applications. It supports reasoning, function calling, and streaming, with a 131,072-token context window.
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 4.5V is Z.ai's open-weight vision-language foundation model for multimodal agent applications. It takes text and image input and returns text, with reasoning and function calling. The context window is 65,536 tokens.
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-4.6V is the vision open-weight variant of Z-AI's GLM-4.6 model. It takes text and image input. It suits document intelligence, code-screenshot interpretation, and multimodal reasoning.
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-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-Turbo is Z.ai's fast GLM-5 variant, tuned for agent workloads such as OpenClaw. It handles tool calling, instruction following, and scheduled and long-running tasks. It has a 200K-token context window, up to 128K output tokens, and thinking mode.
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-Legal is Z.ai's model for professional legal workflows, built on GLM-5.3-FlashX. It covers legal research, contract review, and document drafting. It takes text, image, video, and file input, returns text, and has a 1M-token context window.
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.
GLM 5V Turbo is Z.ai's first native multimodal agent foundation model, built for vision-based coding and agent-driven tasks. It takes image, text, and video input and returns text, with a 202,752-token context window.