What's New
A hybrid-reasoning Mixture-of-Experts model built for production-scale AI agents. Features Hybrid Linear Attention architecture with 5 KDA layers for efficient long-range memory and 1 MLA layer for precise attention per stack. With 124B total parameters and only 5.1B active per token, it matches or beats InclusionAI's 1-trillion-parameter flagship on most benchmarks. Native 256K context, designed to scale toward 1M.
Fast Gemini model balancing multimodal reasoning, tool use, and cost
Fast Gemini model balancing multimodal reasoning, tool use, and cost
Agentic coding model from Poolside in the XS size class for local deployment
Preview Qwen flagship for million-token multimodal reasoning and long-horizon agentic workflows
Multimodal Kimi model with 1M context and toggleable max-effort thinking for long-horizon agent work
Multimodal MoE reasoning model (975B total, 41B active) for text, image, and audio
Cost-efficient GPT-5.6 model for fast, high-volume workloads
Frontier GPT-5.6 model for complex professional work, coding, and agentic workflows
Balanced GPT-5.6 model for capable, cost-efficient everyday work
xAI's latest Grok for chat, coding, agentic tools, and lower hallucination risk
Tencent Hy reasoning model for coding, instruction following, and agent tasks
Realtime speech-to-speech model with configurable reasoning, tool use, and robust voice-agent behavior
Agentic coding model from Poolside in the XS size class for local deployment
Meituan LongCat-2.0, a reasoning model with tool calling and a 1M-token context window
Everyday Claude agent model for coding, planning, browsing, and general work
Video generation and editing model for fast, conversational text- and image-to-video workflows
Open coding-reasoning model for repository tasks and self-improving agents
Large coding-reasoning model for agentic software tasks and RL search
Large coding-reasoning model for agentic software tasks and RL search
Open coding-reasoning model for repository tasks and self-improving agents
Multi-agent model for routing expert agents across complex analytical tasks
Quality-first multi-agent model for hard research, analysis, and competitions
Open flagship GLM for long-horizon coding agents and million-token context work
Coding-focused Kimi model, stronger on long-horizon repo work with less overthinking
Lower-latency Kimi Code variant for interactive edits and coding-agent loops
Claude model for creative writing, analysis, and controlled agent workflows
Cohere coding model for practical software engineering and agentic edits
MiMo pro model for strong multimodal reasoning and agent execution
Largest Nemotron 3 model for maximum open-weight reasoning and agent accuracy
Safety model for policy screening, moderation, and risk-aware routing workflows
Microsoft coding model built for fast, efficient assistance in everyday developer workflows
Multimodal Qwen workhorse for long-context agents, visual inputs, and coding
MiniMax multimodal model for long-context coding, perception, and agent planning
Newer StepFun flash model for faster agents, coding, and multimodal prompts
Top Claude Opus tier for the hardest reasoning, coding, and long-horizon agents
Nano Banana Pro for higher-fidelity image generation and design-heavy edits
Image model for prompt-driven generation, editing, and visual design workflows
Qwen frontier model tuned for agent frameworks, coding assistants, and long tasks
Cohere's stronger command model for multilingual agents and enterprise workflows
Fast Gemini model balancing multimodal reasoning, tool use, and cost
Fast Gemini model balancing multimodal reasoning, tool use, and cost
Low-latency Gemini model for high-volume multimodal and agent workloads
Low-latency Gemini model for high-volume multimodal and agent workloads
Streaming speech-to-text model for low-latency transcript deltas from live audio
Compact GPT model for low-latency assistance and high-volume workloads
Balanced Mistral model for enterprise assistants, multilingual work, and tools
Balanced Mistral model for enterprise assistants, multilingual work, and tools
Open Nemotron omni model combining reasoning with text, vision, and audio
Poolside's open-weight model for agentic coding and long-horizon work
Agentic coding model from Poolside in the XS size class for local deployment
Qwen vision-language model for visual reasoning, documents, and agent tasks
Fast DeepSeek V4 lane for economical reasoning, coding, and long-context work
Open MoE flagship with million-token context for coding and long agent runs
Default frontier GPT for coding, computer use, research, and knowledge work
Highest-accuracy GPT-5.5 tier for slower, precision-heavy reasoning and coding
Open MiMo model for multimodal coding agents and long-context automation
Stronger MiMo Pro tier for multimodal reasoning and coding-agent execution
Qwen vision-language model for visual reasoning, documents, and agent tasks
Multimodal Kimi workhorse for agent loops, coding tasks, and visual context
Image model for prompt-driven generation, editing, and visual design workflows
Flagship Qwen model for complex reasoning, coding, and agentic workflows
Tencent Hy reasoning model for coding, instruction following, and agent tasks
xAI's default Grok for chat, coding, agentic tools, and lower hallucination risk
Open multimodal Qwen MoE for local agents that need vision, audio, and code
Stronger Opus tier for advanced software work and high-stakes reasoning
Safety model for policy screening, moderation, and risk-aware routing workflows
Fast Grok coding model tuned for agentic engineering and iterative edits
Muse Spark is a natively multimodal reasoning model with support for tool-use, visual chain of thought, and multi-agent orchestration.
Strong GLM coding model for agentic engineering, terminals, and repository generation
Earlier Qwen multimodal workhorse for million-token agent and document tasks
Open Gemma instruction model for efficient chat and self-hosted deployments
Largest Gemma 4 instruction model for open, self-hosted chat and reasoning
Open Gemma instruction model for efficient chat and self-hosted deployments
Open Gemma instruction model for efficient chat and self-hosted deployments
StepFun flash model for efficient multimodal reasoning, coding, and tool use
Fast GLM vision model for screenshots, documents, and multimodal agent tasks
Reranking model for improving retrieval quality in search and recommendation systems
Nemotron model for efficient reasoning, coding, and specialized AI agents
Open MiniMax flagship for coding agents, office automation, and complex environments
Low-latency M2.7 variant for interactive coding plans and agent loops
MiMo omni model for text, image, video, audio, and agents
Earlier MiMo Pro model for multimodal agents, reasoning, and code tasks
Strong small GPT for coding subagents, quick tool use, and high-volume work
Cheapest GPT-5.4 lane for simple routing, extraction, and bulk automation
Fast Mistral production model for chat, extraction, and cost-sensitive agents
Nemotron multimodal model for visual reasoning and agentic AI workflows
Faster GLM-5 lane for coding agents that need lower latency
Nemotron middle tier for collaborative agents and high-volume reasoning workloads
Grok model for agentic tool use, reasoning, coding, and live assistance
Reasoning Grok for document-heavy analysis and long-horizon tool use
Agent-ready GPT for coding and computer-use workflows at a lower cost
More exact GPT-5.4 tier for demanding professional reasoning and agent tasks
Chat-tuned GPT model for conversational assistance, writing, and tool workflows
Low-latency Gemini model for high-volume multimodal and agent workloads
Image model for prompt-driven generation, editing, and visual design workflows
Qwen vision-language model for visual reasoning, documents, and agent tasks
Qwen vision-language model for visual reasoning, documents, and agent tasks
Qwen vision-language model for visual reasoning, documents, and agent tasks
Qwen instruction model for multilingual chat, reasoning, and tool use
Reasoning-first Gemini preview for agentic coding and complex problem solving
Advanced Gemini model for complex reasoning, coding, and multimodal analysis
Efficient Indian-language reasoning model for chat, coding, and multilingual work
Claude workhorse for coding agents, careful analysis, and production cost control
Qwen vision-language model for visual reasoning, documents, and agent tasks
Large open Qwen multimodal MoE for visual agents and long technical tasks
High-speed MiniMax model for low-latency coding and agent workflows
Prior MiniMax coding model for agent workflows, office edits, and automation
General GLM flagship for coding, analysis, and tool-heavy engineering workflows
Embedding model for semantic search, retrieval, clustering, and ranking pipelines
Coding-optimized GPT model for repository edits, reviews, and agentic software work
High-end Claude for difficult coding, planning, and slower expert reasoning
StepFun flash lane for quick multimodal reasoning and coding assistance
Safety model for policy screening, moderation, and risk-aware routing workflows
Budget GLM lane for fast coding help, routing, and everyday automation
Efficient GLM model for fast reasoning, coding, and agent workflows
Earlier Kimi frontier model for long-context agents, coding, and multimodal work
Earlier MiniMax agent model for practical coding and productivity tasks
Mature GLM model for dependable coding, reasoning, and structured agent tasks
New Gemini flash lane bringing frontier-style multimodal reasoning to cheaper runs
MiMo flash model for fast multimodal assistance and agent workflows
Small Nemotron 3 MoE for efficient coding, math, and long-context agents
Reliable GPT generation for broad coding, writing, and tool-assisted product work
Chat-tuned GPT model for conversational assistance, writing, and tool workflows
Code-specialist GPT for repository edits, reviews, and long-running software agents
Higher-accuracy GPT-5.2 variant for tougher reasoning and review workflows
Mistral's coding-agent model for repository work, terminal tasks, and software fixes
GLM vision model for visual reasoning, documents, and multimodal agents
Mistral coding agent model for repository tasks and software engineering workflows
DeepSeek chat model for instruction following, coding, and analysis
DeepSeek reasoning model for multi-step analysis, math, coding, and tools
Image model for prompt-driven generation, editing, and visual design workflows
Flagship Claude model for deep reasoning, coding, and long-horizon agents
Nano Banana Pro for higher-fidelity image generation and design-heavy edits
Preview Gemini flagship for complex reasoning, coding, and rich multimodal prompts
Sharper GPT-5 generation for coding, product work, and tool-assisted tasks
Chat-tuned GPT-5.1 for polished assistants, writing, and product conversations
Codex GPT for repository edits, code review, and practical software agents
Coding-optimized GPT model for repository edits, reviews, and agentic software work
Coding-optimized GPT model for repository edits, reviews, and agentic software work
Thinking Kimi model for slower research passes, planning, and hard technical questions
Kimi reasoning model for long-horizon research, planning, and tool use
Flagship Claude model for deep reasoning, coding, and long-horizon agents
Safety model for policy screening, moderation, and risk-aware routing workflows
Safety model for policy screening, moderation, and risk-aware routing workflows
Nemotron multimodal model for visual reasoning and agentic AI workflows
Efficient open MiniMax model built for coding agents and tool-heavy workflows
Fast Claude model for responsive assistance, classification, and lightweight agents
Claude Haiku 4.5 is Anthropic's fastest, most cost-efficient model, matching Sonnet 4's performance on coding, computer use, and agent tasks. It offers similar performance to Sonnet 4 at one-third the cost and more than twice the speed, making it ideal for high-volume, latency-sensitive applications and multi-agent orchestration.
Higher-accuracy GPT-5 tier for tough analysis, coding reviews, and planning
GLM-4.6 is the latest version of Z.ai's flagship model, bringing significant improvements over GLM-4.5. Key features include: 200K token context window (expanded from 128K), superior coding performance with better real-world application in Claude Code/Cline/Roo Code/Kilo Code, advanced reasoning with tool use during inference, stronger agent capabilities, and refined writing aligned with human preferences. GLM-4.6 achieves competitive performance with DeepSeek-V3.2-Exp and Claude Sonnet 4, reaching near parity with Claude Sonnet 4 (48.6% win rate) on CC-Bench real-world coding tasks.
Speech generation model for controllable voice, narration, and audio delivery
Speech generation model for controllable voice, narration, and audio delivery
Balanced Claude model for coding, analysis, agent workflows, and cost control
Claude Sonnet 4.5 is the best coding model in the world. It's the strongest model for building complex agents. It’s the best model at using computers. And it shows substantial gains in reasoning and math. Highest intelligence across most tasks with exceptional agent and coding capabilities.
DeepSeek-V3.2-Exp is an experimental iteration introducing DeepSeek Sparse Attention (DSA) to improve long-context training and inference efficiency while keeping output quality on par with V3.1. It explores fine-grained sparse attention for extended sequence processing.
Flagship Qwen3 model for coding agents, complex reasoning, and tool use
Qwen vision-language model for visual reasoning, documents, and agent tasks
GPT-5 Codex has been trained specifically for conducting code reviews and finding critical flaws. When reviewing, it navigates your codebase and analyzes code patterns to identify potential security vulnerabilities, performance issues, and bugs.
Qwen3-Next-80B-A3B-Base is the foundation model in the Qwen3-Next series, featuring revolutionary architectural innovations for ultimate training and inference efficiency. It introduces Hybrid Attention combining Gated DeltaNet (75% layers) and Gated Attention (25% layers) for efficient ultra-long context modeling, Ultra-Sparse MoE with 512 total experts but only 10 routed + 1 shared expert activated (3.7% activation ratio), and native Multi-Token Prediction for faster inference. With 80B total parameters and only ~3B activated per inference step, it achieves performance comparable to Qwen3-32B while using less than 10% training cost and delivering 10x+ throughput for 32K+ contexts. Trained on 15T tokens with training-stability-friendly designs including Zero-Centered RMSNorm and normalized MoE router parameters. Supports 256K context length, extensible to 1M tokens with YaRN scaling.
Qwen3-Next-80B-A3B-Instruct is the first in the Qwen3-Next series, featuring groundbreaking architectural innovations. It uses Hybrid Attention combining Gated DeltaNet and Gated Attention for efficient ultra-long context modeling, High-Sparsity MoE with 512 experts (10 activated + 1 shared) achieving extreme low activation ratio, and Multi-Token Prediction for improved performance and faster inference. With 80B total parameters and only 3B activated, it outperforms Qwen3-32B-Base with 10% training cost and 10x throughput for 32K+ contexts. The model performs on par with Qwen3-235B-A22B-Instruct-2507 while excelling at ultra-long-context tasks up to 256K tokens (extensible to 1M with YaRN). Architecture: 48 layers, 15T training tokens, hybrid layout of 12*(3*(Gated DeltaNet->MoE)->(Gated Attention->MoE)).
Qwen3-Next-80B-A3B-Thinking is the thinking variant of the Qwen3-Next series, featuring the same groundbreaking architecture as the instruct model. Leveraging GSPO, it addresses stability and efficiency challenges of hybrid attention + high-sparsity MoE in RL training. It uses Hybrid Attention combining Gated DeltaNet and Gated Attention for efficient ultra-long context modeling, High-Sparsity MoE with 512 experts (10 activated + 1 shared), and Multi-Token Prediction. With 80B total parameters and only 3B activated, it demonstrates outstanding performance on complex reasoning tasks — outperforming Qwen3-30B-A3B-Thinking-2507, Qwen3-32B-Thinking, and even the proprietary Gemini-2.5-Flash-Thinking across multiple benchmarks. Architecture: 48 layers, 15T training tokens, hybrid layout of 12*(3*(Gated DeltaNet->MoE)->(Gated Attention->MoE)). Supports only thinking mode with automatic <think> tag inclusion, may generate longer thinking content.
Kimi K2 0905 is the September update of Kimi K2 0711. It is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion total parameters with 32 billion active per forward pass. It supports long-context inference up to 256k tokens, extended from the previous 128k. This update improves agentic coding with higher accuracy and better generalization across scaffolds, and enhances frontend coding with more aesthetic and functional outputs for web, 3D, and related tasks. The model is trained with a novel stack incorporating the MuonClip optimizer for stable large-scale MoE training.
Kimi K2-Instruct-0905 is the latest, most capable version of Kimi K2, achieving state-of-the-art performance in frontier knowledge, math, and coding among non-thinking models. This Mixture-of-Experts model features 32 billion activated parameters and 1 trillion total parameters, meticulously optimized for agentic tasks. Key features include enhanced agentic coding intelligence, extended context length to 256K tokens, and a hybrid architecture trained with MuonClip optimizer on 15.5T tokens. The model achieves 65.8% on SWE-bench Verified (single attempt), 47.3% on SWE-bench Multilingual, and excels at tool use with 70.6% on Tau2-retail. It is a reflex-grade model without long thinking, designed to act and execute complex tasks seamlessly.
Flagship Indian-language reasoning model for enterprise multilingual applications
Grok 4 Fast is a high-speed variant of Grok-4, optimized for faster inference while maintaining strong reasoning capabilities. It offers improved throughput and lower latency compared to the standard Grok-4 model.
Grok Code Fast 1 is a speedy and economical reasoning model that excels at agentic coding. Built from scratch with a brand-new model architecture, it features a pre-training corpus rich with programming-related content and post-training datasets that reflect real-world pull requests and coding tasks. The model has mastered the use of common tools like grep, terminal, and file editing, making it ideal for integration with IDEs. It is exceptionally versatile across the full software development stack and is particularly adept at TypeScript, Python, Java, Rust, C++, and Go.
Translation model for multilingual conversion, localization, and cross-language workflows
Nano Banana image model for fast generation, edits, and character-consistent assets
Cohere reasoning model for multilingual enterprise agents, tools, and complex workflows
NVIDIA-Nemotron-Nano-9B-v2 is a large language model (LLM) trained from scratch by NVIDIA, and designed as a unified model for both reasoning and non-reasoning tasks. It responds to user queries and tasks by first generating a reasoning trace and then concluding with a final response. The model's reasoning capabilities can be controlled via a system prompt. If the user prefers the model to provide its final answer without intermediate reasoning traces, it can be configured to do so, albeit with a slight decrease in accuracy for harder prompts that require reasoning. Conversely, allowing the model to generate reasoning traces first generally results in higher-quality final solutions to queries and tasks.
GLM-4.5V is a multimodal (vision-language) model based on GLM-4.5-Air (106B total, 12B active) that extends hybrid reasoning to images and video. It achieves state-of-the-art results across 40+ VLM benchmarks (image reasoning, video understanding, GUI tasks, chart/document parsing, grounding) while supporting a Thinking Mode switch for deep reasoning. Released under MIT with FP8/BF16 variants and tooling in Transformers, vLLM, and SGLang.
GPT-5 is our flagship model for coding, reasoning, and agentic tasks across domains. The best model for coding and agentic tasks with higher reasoning capabilities and medium speed.
Chat-tuned GPT model for conversational assistance, writing, and tool workflows
A faster, more cost-efficient version of GPT-5 for well-defined tasks. Great for well-defined tasks and precise prompts with high reasoning capabilities at reduced cost.
GPT-5 nano is our fastest, cheapest version of GPT-5. It's great for summarization and classification tasks with average reasoning capabilities and very fast speed.
GPT-OSS-120B is an open-weight, 116.8B-parameter Mixture-of-Experts (MoE) language model from OpenAI designed for high-reasoning, agentic, and general-purpose production use cases. It activates 5.1B parameters per forward pass and is optimized to run on a single H100 GPU with native MXFP4 quantization. The model supports configurable reasoning depth, full chain-of-thought access, and native tool use, including function calling, browsing, and structured output generation. It achieves near-parity with OpenAI o4-mini on core reasoning benchmarks. Note: While referred to as '120b' for simplicity, it technically has 116.8B parameters.
The gpt-oss-20b model (technically 20.9B parameters) achieves near-parity with OpenAI o4-mini on core reasoning benchmarks, while running efficiently on a single 80 GB GPU. The gpt-oss-20b model delivers similar results to OpenAI o3‑mini on common benchmarks and can run on edge devices with just 16 GB of memory, making it ideal for on-device use cases, local inference, or rapid iteration without costly infrastructure. Both models also perform strongly on tool use, few-shot function calling, CoT reasoning (as seen in results on the Tau-Bench agentic evaluation suite) and HealthBench (even outperforming proprietary models like OpenAI o1 and GPT‑4o). Note: While referred to as '20b' for simplicity, it technically has 20.9B parameters.
Flagship Claude model for deep reasoning, coding, and long-horizon agents
Claude Opus 4.1 is a hybrid reasoning model that pushes the frontier for coding and AI agents, featuring a 200K context window. It delivers superior performance and precision for real-world coding and agentic tasks, handling complex multi-step problems with rigor and attention to detail. With extended thinking capabilities, it offers instant responses or extended step-by-step thinking visible through user-friendly summaries. It advances state-of-the-art coding performance to 74.5% on SWE-bench Verified, excels at agentic search and research, and produces human-quality content with exceptional writing abilities. It supports 32K output tokens and adapts to specific coding styles while delivering exceptional quality for extensive generation and refactoring projects.
Cohere vision model for multilingual document analysis, OCR, and image understanding
Efficient Qwen model for fast chat, extraction, and high-volume workloads
Qwen coding model for software agents, repository edits, and code reasoning
GLM-4.5 is an Agentic, Reasoning, and Coding (ARC) foundation model designed for intelligent agents, featuring 355 billion total parameters with 32 billion active parameters using MoE architecture. Trained on 23T tokens through multi-stage training, it is a hybrid reasoning model that provides two modes: thinking mode for complex reasoning and tool usage, and non-thinking mode for immediate responses. The model unifies agentic, reasoning, and coding capabilities with 128K context length support. It achieves exceptional performance with a score of 63.2 across 12 industry-standard benchmarks, placing 3rd among all proprietary and open-source models. Released under MIT open-source license allowing commercial use and secondary development.
GLM-4.5-Air is a more compact variant of GLM-4.5 designed for efficient Agentic, Reasoning, and Coding (ARC) applications. It features 106 billion total parameters with 12 billion active parameters using MoE architecture. Like GLM-4.5, it is a hybrid reasoning model providing thinking mode for complex reasoning and tool usage, and non-thinking mode for immediate responses. Despite its compact design, GLM-4.5-Air delivers competitive performance with a score of 59.8 across 12 industry-standard benchmarks, ranking 6th overall while maintaining superior efficiency. It supports 128K context length and is released under MIT open-source license allowing commercial use.
Efficient GLM model for fast reasoning, coding, and agent workflows
Nemotron model for efficient reasoning, coding, and specialized AI agents
Qwen3-235B-A22B-Thinking-2507 is a state-of-the-art thinking-enabled Mixture-of-Experts (MoE) model with 235B total parameters (22B activated). It features 94 layers, 128 experts (8 activated), and supports 262K native context length. This version delivers significantly improved reasoning performance, achieving state-of-the-art results among open-source thinking models on logical reasoning, mathematics, science, coding, and academic benchmarks. Key enhancements include markedly better general capabilities (instruction following, tool usage, text generation), enhanced 256K long-context understanding, and increased thinking depth. The model supports only thinking mode with automatic <think> tag inclusion.