AI Open Models

OpenAI-compatible LLM inference and chat completions across seven open-weight reasoning, coding, and vision models. Choose Kimi K3, Qwen3.6, Qwen3.8, GLM-5.2, GLM-5.3, GLM-5.3-Flash, or DeepSeek V4.1 Flash. Use them to generate text or JSON, analyze images, and call client-provided tools. Supports up to 4 images and selectable reasoning controls. Pricing is quoted dynamically from the prompt and m

Routes requests across open-weight models using task fit, tool calling, vision, JSON format, context, output capacity, provider availability, and price. Send POST application/json with model set to ai-open-models/auto and a non-empty messages array. task_type is optional; when omitted, Auto classifies the request. Supports function tools, JSON responses, and mixed text/image messages using public

Fusion Model. Internal reviewers examine the conversation and available tool results, then a final model returns the next OpenAI-compatible response, including tool calls when tools are provided. Supports repeated multi-turn calls, client-executed tools, and JSON responses. Internal review notes are not returned.

Converts one natural-language task into validated OpenAI-compatible JSON request bodies for multiple open-weight models. Uses the optional models list when supplied; otherwise uses all catalogue models compatible with the input. Supports text, visual input, and model-supported reasoning levels. Returns JSON request bodies only and does not execute inference.

Retrieve clean content from one webpage using Exa. Choose full text, query-focused highlights, or an AI-generated summary, with page metadata and retrieval status.

Run the same task against several explicitly selected open-weight models in parallel and return every answer separately. No ranking, verification, or synthesis.

POST application/json with required non-empty query. Exa-powered search returns ranked URLs and query-relevant highlights. Optional fields are objective, num_results (default 5), category, include_domains, exclude_domains, start_published_date, and end_published_date.

AI-generated answers powered by Exa web search. Ask a factual or open-ended question and receive a synthesized answer with source citations.

An LLM router for an open-weight inference service. It analyzes your request, selects a suitable compatible model, runs the selected model, and returns its answer in OpenAI-compatible format.

Conduct one live-web research task with 2-7 buyer-selected open-weight analyst models, then use a buyer-selected synthesis model to produce one structured, cited research report. Reconciles agreements, disagreements, contradictions, and uncertainties while preserving sources, analyst outputs, latency, usage, and validation results.

Run one live-web research task through 2–7 buyer-selected open-weight models. Every model receives the same retrieved evidence and returns a separate structured analysis with citations, latency, usage, and validation results. No ranking or synthesis.