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OpenAI's fast, cost-efficient GPT-6 model for high-volume chat, classification, and lightweight agentic tasks, with dialable reasoning effort and a 1.05M-token context window.
OpenAI's mid-tier reasoning model with configurable effort levels, a 1.05M-token context window, and text+image input for coding and agentic workflows.
Xiaomi's MIT-licensed efficiency-tier omnimodal reasoning model: a 309B/15B sparse MoE with a 1M-token context, native image, video and audio understanding, and a caller-controlled thinking switch.
Xiaomi's open-weight 1.02T-parameter omnimodal reasoning model with a 1M-token context, accepting text, image, video and audio input and returning text.
Anthropic's frontier reasoning model with a 1M-token context, adjustable reasoning effort, and multimodal input for code, research, and long-document work.
Moonshot AI's open-weights, always-reasoning 2.8T MoE flagship with a 1M-token context, native tool use, and multimodal input.
OpenAI's fast, cost-efficient GPT-5.6 tier for high-volume chat, classification, and lightweight agentic tasks with a 1.05M-token context window.
OpenAI's flagship GPT-5.6 reasoning model for agentic coding, cybersecurity, and long-horizon analysis, with a 1.05M-token context window.
xAI's proprietary reasoning model with a 500,000-token context window, mandatory configurable reasoning effort, and text and image input.
Anthropic's adaptive-reasoning Sonnet-class model with a 1M-token context window, strong Design Arena coding and UI results, and always-on reasoning selectable from low to max effort.
Anthropic's frontier Mythos-class model with always-on adaptive reasoning and a one-million-token context window, built for deep code and research analysis rather than real-time chat.
An open-weight 428B-parameter (~23B active) MoE model from MiniMax with a 1M-token context window, multimodal input, and a configurable thinking mode, suited to long-context, agentic, and coding-assist workloads.
Anthropic's frontier reasoning model with a 1M-token context window, built for complex agentic workflows, graduate-level analysis, and long-context tasks.
Google's fast, cost-optimised multimodal Gemini model with a million-token context window, built for high-throughput text, image, audio, video, and file input.
OpenAI's long-context instruct model, built for complex multi-step reasoning, long-document analysis, and tool orchestration across a 1M+ token context window.
Anthropic's frontier-tier closed-weight instruct model with a one-million-token context window, built for complex, long-horizon reasoning and coding rather than real-time interaction.
Google DeepMind's open-weight, multimodal Mixture-of-Experts model with a 262K-token context window, tuned for interactive-latency, moderate-complexity tasks.
Google DeepMind's open-weights 30.7B dense multimodal model with a 256K-token context window and optional chain-of-thought thinking, instruction-tuned for long-context and multimodal tasks.
OpenAI's efficient GPT-5.4 mini instruct model, built for coding and agentic workloads with optional reasoning depth and a 400K-token multimodal context window.
Open-weights 119B MoE model from Mistral AI (128 experts, 4 active, 6.5B active per token) unifying instruct, opt-in reasoning, and vision in a single 262K-context checkpoint served at an interactive latency class.
xAI's non-reasoning Grok 4.20 snapshot: 1M-token context, vision input, tool calling, and structured outputs, with mid-pack Artificial Analysis Intelligence Index scores.
xAI's multi-agent orchestration model: parallel sub-agent debate synthesized into one answer, built for deliberate reasoning over speed.
OpenAI's configurable chain-of-thought reasoning model with a 1M-token context window and native text, image, and file input.
Google's frontier reasoning model with mandatory chain-of-thought inference, a 1M-token context window, and full multimodal input for long-horizon, complex tasks.
A hybrid-reasoning Claude model from Anthropic with a 1M-token context window, selectable reasoning effort, and tool calling for coding, research, and agentic work.
One of Anthropic's Claude Opus 4 models — a one-million-token context window, optional adaptive thinking, and native document understanding for reasoning-heavy and agentic work.
Google's multimodal Flash model with a 1M+ token context and opt-in reasoning effort, built for fast Inference on complex, mixed-media tasks.
Mistral AI's dense 14B multimodal Instruct model with Apache-2.0 open weights, a 262,144-token context window and native tool calling.
Mistral AI's open-weight flagship: a 675B-parameter (41B active) Granular MoE model with native vision input, function calling, and a 262,144-token context window, built for long-document and multimodal workflows.
Anthropic's frontier Claude Opus 4.5: a proprietary, text-and-image model built for complex coding, research, and long-document reasoning with optional extended thinking.
Anthropic's fastest Claude 4 model — built for high-throughput agentic and coding workloads, with a 200K-token context window, optional extended thinking, vision input, and strong safety benchmarks.
Anthropic's instruct-tuned Claude Sonnet 4.5 — built for agentic coding, computer-use, and long-context reasoning via the Claude API.
OpenAI's compact GPT-5 variant with tunable reasoning effort, a 400K-token context window, and multimodal input for interactive document and image tasks.
OpenAI's fastest, lightest GPT-5 tier for high-volume classification, summarisation, and short completions — 400K context, vision and file input, mandatory reasoning traces.
Mistral AI's code-specialised API model for fill-in-the-middle completion, code correction and test generation, with a 256K-token context window.
Google's fastest, lowest-cost Gemini 2.5 model — a multimodal instruct model with a 1M-token context window, built for realtime, high-throughput Inference.
Deprecated 24B open-weights multimodal instruct model from Mistral AI with 131k context, tool calling, and strong document vision — use Small 4 for new work.
Google's closed-weight, reasoning-tuned flagship with a 1M-token context window, multimodal input, and native tool calling.
Google DeepMind's efficiency-tier multimodal model with a 1M-token context window, optional chain-of-thought thinking, and native text, image, audio, video, and file input.
OpenAI's closed-weight GPT-4 Turbo brings a 128K-token context window and text+image input to complex, long-context tasks at interactive latency.
OpenAI's GPT-3.5 Turbo is a fast, text-only chat model built for high-volume, simple conversational tasks via the Chat Completions API, with tool calling and JSON mode support.