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Kenerate AI is an AI creative studio for video, images, voiceovers, and music. Explore creative tools, work with reference media, choose AI models, and manage creations in one place. Kenerate AI also supports an agent-style chat workflow for developing ideas into finished creative work.

Mistral Large 4 — Online Playground & Mistral Large 4 API Mistral Large 4 brings an online AI playground, developer API, and practical model guides together in one place. Use it to work through code, improve writing, ask questions about supplied documents, and understand images. When a prompt becomes useful enough to repeat, connect the same service to your application through the Mistral Large 4 API. The playground gives individuals and teams a place to try real tasks without writing integration code. Developers can move into an OpenAI-compatible REST interface with streaming responses, structured JSON output, image inputs, and function calling. Documentation, model comparisons, and deployment guides help explain the choices involved in building a workflow. About this service: mistrallarge4.com is independently operated and is not affiliated with Mistral AI. Its accounts, API keys, endpoint, and credit plans belong to this service. The underlying model is developed by Mistral AI. What you can do | Capability | Put it to work | | --- | --- | | AI conversations | Ask questions, explore ideas, and refine an answer through follow-up messages. | | Coding assistance | Explain functions, investigate errors, review implementation choices, and propose focused changes. | | English and Chinese writing | Draft, rewrite, and summarize text with a specified audience, tone, and terminology. | | Document questions | Supply relevant passages and ask for summaries, comparisons, or answers supported by the supplied text. | | Image understanding | Attach a public image URL to ask about a screenshot, diagram, or other visual context. | | Adjustable reasoning | Choose direct answers or higher reasoning effort according to the task. | | Structured responses | Request JSON in the playground or use JSON objects and JSON Schema through the API. | | Streaming API | Receive incremental response content for conversational applications. | | Function calling | Let the model request defined functions that your application validates and executes. | The documentation describes the supported inputs, controls, and service limits for each workflow. Who it is for | Audience | Example workflows | | --- | --- | | Software developers | Review a function, explain an unfamiliar module, or investigate a bug using relevant code and error messages. | | Writers and content teams | Turn a brief into a draft, adjust tone for a specific audience, or refine Chinese and English copy. | | Researchers and analysts | Compare supplied passages, summarize findings, and extract information into a structured response. | | Product and operations teams | Review screenshots, organize written feedback, and draft plans from provided context. | | Independent makers | Explore a product idea in the playground, then add an AI feature to an application. | | AI agent builders | Connect approved lookup functions and application tools to a conversational workflow. | For example, a developer can paste a failing function and its error message, ask for an explanation, and request a small proposed patch. A content team can provide a Chinese passage and ask for a rewrite that preserves product names, dates, and technical terminology. An analyst can attach a diagram URL and ask for an explanation alongside questions that still need clarification. Start in the playground Open the playground and sign in. Use an account on this website to send requests. Describe a concrete task. Include the relevant text, code, or public image URL, together with the result you need. Choose the response settings. Adjust the system prompt, reasoning effort, temperature, maximum output tokens, and text or JSON output. Review the answer. Check the result against your source material and follow up with missing context or corrections. Move a repeatable workflow into your app. Create an account API key and use the documented chat endpoint. A useful coding prompt: Review the following TypeScript function. Explain its purpose, identify edge cases supported by the code, and suggest one small improvement. Return a short explanation followed by the proposed change. A useful document prompt: Summarize the supplied passage for a product manager. Preserve the original dates and numbers, separate decisions from open questions, and cite the relevant passage for each conclusion. If the text does not answer a question, say so. Conversation context remains available while the playground page is open. Refreshing the page starts a new conversation, so copy answers you want to keep before leaving. Build with the Mistral Large 4 API The Mistral Large 4 API connects the service to applications, internal tools, and AI agents through an OpenAI-compatible chat interface. Start with a small text request, then add streaming, image inputs, structured output, or tools as the workflow requires. | Setting | Value | | --- | --- | | Base URL | https://mistrallarge4.com/api/v1 | | Chat endpoint | POST https://mistrallarge4.com/api/v1/chat/completions | | Model ID | mistral-large-4-0 | | Authentication | Authorization: Bearer | | Request format | Content-Type: application/json | | Reasoning effort | none or high | Create a key under Account → API Keys, then set MODELAPIKEY in your server environment to that account key. A minimal request looks like this: For a standard non-streaming response, read the answer from choices[0].message.content and token usage from usage. Keep API keys in your server environment. The API also supports: Streaming: set stream to true to receive Server-Sent Events and process incremental content. Image questions: combine text with an image_url content part pointing to an accessible public image. Structured output: use responseformat with jsonobject or a named json_schema to shape the response. Tool workflows: define functions in tools, inspect returned tool_calls, execute approved functions in your application, and return their results for the next model response. Reasoning control: choose none or high; reasoning tokens share the output budget with the visible answer. See the API guide for complete request examples, limits, and error handling. Use this site's endpoint, model ID, and account key together. Working with inputs and results The service accepts text and public image URLs. For document questions, extract the relevant text from a PDF before submitting it. Direct PDF, audio, and video uploads are not supported by the documented playground and chat API. The playground currently accepts up to 24 messages, 24,000 characters per message, a 96 KiB request body, and up to 16,384 output tokens. API limits differ. The underlying model's advertised context window should not be treated as the playground's input allowance; consult the current service limits when sizing a request. For code tasks, run the proposed change in your own environment. For document and image tasks, check names, numbers, quotations, and small visual details against the supplied material. Validate structured responses before using them in another system. Free access and paid usage Mistral Large 4 uses a freemium model. Signed-in users receive 30 free playground requests per day, resetting at midnight UTC+8 / Asia/Singapore. Requests admitted for generation count toward that allowance, including requests that subsequently fail or are cancelled. After the daily allowance, completed playground answers use credits. One-time credit packs and monthly subscriptions are available for additional usage. The daily free allowance applies to the playground; it does not establish a free allowance for API calls. Compare current prices and included credits on the pricing page, and read the usage guide before integrating billing-sensitive workflows. This service's billing is separate from Mistral AI's official API billing. About the organization The organization behind mistrallarge4.com was founded on May 1, 2026, and is led by Mason King, Founder & CEO, in Seattle, United States. The team has 10 full-time employees and focuses on accessible AI experimentation and developer integrations. This profile introduces the independent web service and its developer entry points. For product questions, account help, or collaboration inquiries, email support@mistrallarge4.com. Explore the product Website — Product overview and model information. Playground — Try text, code, and image questions online. Documentation — Read API examples, controls, and service limits. Pricing — Compare credit packs and subscriptions. Model Comparison — Explore evaluation questions and model choices. Deployment Guide — Review weight availability and deployment considerations. Blog — Browse tutorials and model guides.

DecisionsApi is a developer-focused AI platform for testing decision models in an online playground and integrating them through an API. Developers provide text, JSON objects, or arrays of text as state, define typed Choice, Score, or Noul (yes/no) questions, and receive structured answers with probabilities, confidence, usage, and request details. The platform supports customer-request routing, agent next-step selection, evidence checks, safety gates, and human-review workflows, with API key management, usage history, and one shared credit balance across the playground and API.

Decision API is a developer platform that aggregates decision AI models — Jev, Laya, Kev, Solar Decide, Span, Liquid, D1 and more — behind one playground and one unified API for classifying, scoring, and routing text. Instead of prompting an LLM and parsing prose, you send a state (a ticket, message, or record) plus structured questions and get back typed answers with probability distributions: a label (Choice), a rating along your rubric (Score), or a yes/no judgment with probability (Noul). Use it for customer-request routing, agent next-step selection, and claim-versus-evidence checking. An evaluation workspace lets you A/B test two models or question phrasings on the same cases before wiring one into production, and a single endpoint (POST /v1/systemone, Bearer auth) keeps the integration identical across all models — switching is a one-string change. New accounts get 100 free credits; paid packs are one-time ($10/$100/$1,000), credits never expire. Note: decisionapi.org is an independent platform — not the OpenAI Decisions API preview — and is the current home of the tool previously seen at decisions-api.org.

Decisions API is an independent developer platform that brings multiple decision models — Jev, Laya, Kev, Solar Decide, and Span — into one playground and one unified API for classifying, scoring, and routing text. Instead of prompting an LLM and parsing prose, you send a state (a ticket, message, or record) plus structured questions and get back typed answers with probability signals: a label (Choice), a rating along your rubric (Score), or a yes/no judgment with probability (Noul). Use it for customer-request routing, agent next-step selection, and claim-versus-evidence checking. An evaluation workbench lets you A/B test two models or question phrasings on the same cases before wiring one into production, and a single endpoint (POST /v1/systemone, Bearer auth) keeps the integration identical across models. New accounts get 100 free credits; paid packs are one-time ($10/$100/$1,000) and credits never expire. Note: decisions-api.org is an independent platform, not the OpenAI Decisions API preview announced at DevDay.

Jev AI Model Official Website Jev AI Model is the model-focused home of the Jev decision engine: classify, score, and route text with structured results and probabilities — free online playground for signed-in users, paid credits for API calls. Official app: https://jevaimodel.net Creator: the Jev team (companion site to the Jev AI workspace at thejevai.com) Price: Freemium — playground free after sign-in; API usage via paid credits Login required: Yes — even the playground requires signing in Platform: Web playground + REST API (POST /v1/systemone) with a typed SDK Main use: Turning state plus typed questions (Choice / Score / Noul) into probability-backed decisions inside your code Last checked: 2026-09-23 Visit Jev AI Model We are not the official developer of Jev AI Model. This page helps users find, understand, and use the product. What Is Jev AI Model? Jev AI Model is not a chatbot — it is a decision model. You send state (a ticket, message, form fields, or the structured context your agent already sees) plus up to eight typed questions — Choice for classification, Score for ordered levels, Noul for yes/no — and one request returns typed answers with probabilities and confidence in 70–500ms, ready to branch on in code (if result.urgent: escalate()). This site adds two things the main product page doesn't. First, a published benchmark: JevBench v1.3.0 scores 52 systems across 534 decisions (snapshot Sep 21, 2026), with Jev 1.13.0 ranked #1 at 74.4, ahead of SemIf (73.1) and djev (73.0) — covering decision quality, probability calibration, speed, and cost per decision. Second, a 21-example interactive showcase with a clear specialty: agent trace evaluation — feeding an agent's tool-call trace into questions like taskcompletion, failuremode, and grounded_answer to grade whether an AI agent actually completed the task and whether its final response is grounded in its tool results. How to Use Jev AI Model Open the official Jev AI Model website and sign in — the playground is free for signed-in users. Pick one of the 21 decision workflows (e.g. agent trace evaluation) or build your own: enter state as Text or JSON. Define up to eight typed questions (Choice / Score / Noul), then click Generate decisions to see typed output with probabilities. Copy the POST /v1/systemone JSON preview, buy API credits, create a key, and wire the decision into your service via REST or the typed SDK. Is Jev AI Model Safe? The site runs on the jevaimodel.net domain over standard HTTPS and publishes API, Pricing, and Docs pages. Note the account gate: unlike the main Jev AI site, where the playground works without an account, here even the playground requires sign-in, and API calls use paid credits. Any state you send (tickets, messages, agent traces) leaves your system for classification, so treat customer-sensitive content accordingly — the same caution as any hosted AI API. The model's design supports safety use cases itself (pre-action checks around deletion and payment). No deceptive redirects were observed during review. Last checked September 2026. Why Is Jev AI Model Popular? As teams ship AI agents, evaluating and controlling them became the bottleneck of 2026: did the agent finish the task, is its answer grounded, when should a human step in? Jev AI Model answers exactly that with typed, probability-backed outputs — and backs its claims with a published, dated benchmark rather than adjectives. The free playground (just a sign-in) lets developers pressure-test it on a real scenario in minutes. Jev AI Model Not Working? If Jev AI Model fails to load: Refresh the page. Open it in another browser. Disable ad blockers temporarily. Check whether the original jevaimodel.net deployment is still online. Try again later if the developer is updating the app. Jev AI Model Alternatives Jev AI (thejevai.com) — the same decision engine with a no-login playground OpenAI Structured Outputs — typed JSON from GPT models you orchestrate yourself LangSmith — LLM/agent evaluation and tracing platform Frequently Asked Questions What is the official Jev AI Model website? The official website is https://jevaimodel.net. Is Jev AI Model free? The online playground is free once you sign in; API requests use paid credits. Do I need an account to use Jev AI Model? Yes — this site requires sign-in even for the playground (the companion thejevai.com playground needs no account). Who created Jev AI Model? The Jev team, which also publishes JevBench — an independent evaluation of decision-model quality across 52 systems. Is Jev AI Model still available? Yes, the site was live as of September 23, 2026.