classification

openai's decisions api on gpt-6 luna. send text and images with typed questions (choice, score, yes/no); get a probability for every answer in near real time, with no text generation. direct api.

liquid ai's 587m decision model for text, images and speech. send a state with images or an audio clip and typed questions (choice, score, yes/no); get a probability for every answer. research release

liquid ai's open 3b decision model for text and images. send a state and up to 8 images with typed questions (choice, score, yes/no); get a probability for every answer in one pass, no generation.

perplexity's open decision model for text and images. send a state, up to 4 images and typed questions (choice, score, yes/no); get a calibrated probability for every answer, with no text generation.

the largest decision 2.0 model from vllm semantic router. send a state and typed questions (choice, score, yes/no); get a probability for every answer in one pass. 29.37b parameters, 32k-token input.

autotrust's open decision model with vision. send text and up to 8 images with typed questions (choice, score, yes/no); get a calibrated probability for every answer, with no text generation.

the 9b decision 2.0 model from vllm semantic router. send a state and typed questions (choice, score, yes/no); get a probability for every answer in one forward pass, with no text generation. 7.94b parameters, 16,384-token input, about 18.4 ms per question.

the 4b decision 2.0 model from vllm semantic router. send a state and typed questions (choice, score, yes/no); get a probability for every answer in one forward pass, with no text generation. 4.21b parameters, 16,384-token input, about 12.9 ms per question.

the reasoning variant of decision 2.0 sol 2b, stronger on multi-step problems (arithmetic, code execution, causal and logical questions) at the same speed from vllm semantic router. send a state and typed questions (choice, score, yes/no); get a probability for every answer in one forward pass, with no text generation. 1.88b parameters, 16,384-token input, about 7.5 ms per question.

the 2b decision 2.0 model from vllm semantic router. send a state and typed questions (choice, score, yes/no); get a probability for every answer in one forward pass, with no text generation. 1.88b parameters, 16,384-token input, about 7.2 ms per question.

a small, fast decision 2.0 model from vllm semantic router. send a state and typed questions (choice, score, yes/no); get a probability for every answer in one forward pass, with no text generation. 0.75b parameters, 16,384-token input, about 6.0 ms per question.

the smallest and fastest decision 2.0 model from vllm semantic router. send a state and typed questions (choice, score, yes/no); get a probability for every answer in one forward pass, with no text generation. 0.60b parameters, 8,192-token input, about 4.9 ms per question.

typesafe's system one model. send a state and typed questions (choice, score, noul); get calibrated, structured answers your code can act on directly.