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Open-weight Qwen multimodal MoE model with about 3B active parameters, a 262K-token context, optional reasoning and text and image input.
Z.ai's 754B-parameter open-weight MoE model with integrated reasoning, built for long-horizon agentic engineering and state-of-the-art coding benchmark performance.
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.
A compact open-weights multimodal model from Google DeepMind, with a 128K token context window and optional built-in reasoning.
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.
OpenAI's compact closed GPT-5.4 model for text and image input, with configurable reasoning effort and a 400,000-token context.
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.
NVIDIA's open-weight text MoE model with optional reasoning, 12B active parameters and a long context window.
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 chain-of-thought reasoning model in the Grok 4.20 generation, built for deliberate multi-step inference across text and image input.
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.
OpenAI's closed GPT-5.4 Pro reasoning model for complex analysis and very long text and image inputs, with configurable reasoning effort.
A dense, open-weight 9.65B-parameter multimodal model from Qwen with a 262K-token native context window and text, image, and video input.
Open-weight Qwen multimodal MoE model with about 10B active parameters, image input, optional extended thinking and up to 120K tokens through Stav.
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.
Qwen's open-weight multimodal MoE model with 397B total and 17B active parameters, image input and a 262,144-token context.
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.
Qwen's open-weight 80B/3B-active MoE coding model with a 262K context, tool calling and Apache-2.0 licence, suited to repository-scale code tasks and IDE assistants.
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 Apache-2.0 Ministral 3 3B Instruct model: text and image in, text out, 256k context, built for fast simple workloads.
Mistral AI's dense 8B Instruct model with text and image input, a 262,144-token context window and open Apache-2.0 weights.
DeepSeek's open-weight MoE model with optional reasoning, a 164K-token context window and solid tool use. Released under the MIT licence.
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.
A 70B-parameter, fully open-weight instruction model from the Swiss AI Initiative, built for transparency and multilingual coverage rather than peak benchmark performance.
Nous Research's open-weight 405B hybrid reasoning model based on Llama 3.1, with a 131K-token context and text input and output.
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.
OpenAI's open-weight 21B mixture-of-experts reasoning model under Apache 2.0, with adjustable reasoning effort and a 131K-token context.
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.
Qwen's open-weight Mixture-of-Experts model for instruction following, with a 262K-token context window. It answers directly, without a separate reasoning phase.
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.
OpenAI's higher-compute o3 reasoning model for deep, batch-style math, code, and research tasks via the Responses API.
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.
BSCs spanske og flerspråklige 40B-modell, instruct-varianten ALIA-40b-instruct-2601 (huggingface.co/BSC-LT/ALIA-40b-instruct-2601), Apache-2.0.
Meta's open-weights, dense 70B instruct model with a 128k-token context window, built for self-hosted or Sovereign Inference.
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 original GPT-4: a proprietary, text-only instruct model for professional writing, reasoning, and basic tool-calling workflows, with an 8k-token context window and a 2021 training cutoff.
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.
OpenAI's open-weight, reasoning-tuned Mixture-of-Experts model — Apache-2.0 licensed, 131K context, self-hostable.
MiniMax's open-weights multimodal MoE model with a 1M-token context window and configurable reasoning effort.