Life & fun · alternatives · 2026
24 alternatives to Calculate confidence score for OpenAI JSON output
Calculate confidence score for structured output generated by LLMs like OpenAI - ManiDoraisamy/promptrepo-score
This tool helps users evaluate the reliability of JSON responses generated by OpenAI's API. It analyzes output to calculate a confidence score, allowing developers to assess how trustworthy the structured data is. The… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Prices are shown for the 1 we have checked on their own sites, 1 of them free or with a free tier.
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ManyPI▲35410x your revenue by finding your ideal customers
Dec 2025 · manypi.com · its alternatives →
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Logprobs for OpenAI Structured Outputs
2025 · arena-ai.github.io · its alternatives →
- 3AT
I recently built a small open-source tool to benchmark different LLM API endpoints — including OpenAI, Claude, and self-hosted models (like llama.cpp). It runs a configurable number of test requests and reports two key metrics: • First-token latency (ms): How long it takes for the first token to appear • Output speed (tokens/sec): Overall output fluency Demo: https://llmapitest.com/ Code: https://github.com/qjr87/llm-api-test The goal is to provide a simple, visual, and reproducible way to evaluate performance across different LLM providers, including…
2025 · llmapitest.com · its alternatives →
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We've open-sourced Klarity - a tool for analyzing uncertainty and decision-making in LLM token generation. It provides structured insights into how models choose tokens and where they show uncertainty. What Klarity does: - Real-time analysis of model uncertainty during generation - Dual analysis combining log probabilities and semantic understanding - Structured JSON output with actionable insights - Fully self-hostable with customizable analysis models The tool works by analyzing each step of text generation and returns a structured JSON: - uncertainty_points: array of {step, entropy,…
2025 · github.com · its alternatives →
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When building workflows that rely on LLMs, we commonly use structured output for programmatic use cases like converting an invoice into rows or meeting transcripts into tickets or even complex PDFs into database entries. The model may return the schema you want, but with hallucinated values like `invoice_date` being off by 2 months or the transcript array ordered wrongly. The JSON is valid, but the values are not. Structured output today is a big part of using LLMs, especially when building deterministic workflows. Current structured output benchmarks (e.g., JSONSchemaBench) only validate…
Apr 2026 · interfaze.ai · its alternatives →
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Hey HN, we’re building an open specification that lets agents discover and invoke APIs with natural language, built on the OpenAPI standard. agents.json clearly defines the contract between LLMs and API as a standard that's open, observable, and replicable. Here’s a walkthrough of how it works: https://youtu.be/kby2Wdt2Dtk?si=59xGCDy48Zzwr7ND. There’s 2 parts to this: 1. An agents.json file describes how to link API calls together into outcome-based tools for LLMs. This file sits alongside an OpenAPI file. 2. The agents.json SDK loads agents.json files as tools for an LLM that…
2025 · github.com · its alternatives →
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LLM Stats▲293I was overspending on GPT-4o. It was really hard to compare different models I could switch to, so I built this LLM comparison tool. It shows leaderboards, pricing, and performance data across 100+ LLMs (including all major providers and open-source models). Key features: - Live pricing comparisons - Benchmark Scores (MMLU, HumanEval, GPQA, etc.) - Context length vs cost analysis - Speed/throughput tests across providers - Quality vs price visualizations - Open source (all data verifiable) Try it out: https://llmstats.com I'd like to know your opinion :) Tech stack: Next.js,…
Oct 2025 · llm-stats.com · its alternatives →
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Evidently AIfree▲382Open-source evaluations and observability for LLM apps
2024 · evidentlyai.com · its alternatives →
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traceAI▲273Open-source LLM tracing that speaks GenAI, not HTTP.
Apr 2026 · github.com · its alternatives →
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OpenUI▲322Thesys just open-sourced their generative UI rendering engine. Interesting timing given where Google a2ui and Vercel's json-render are headed. The difference worth noting: a2ui and json-render both treat JSONL as the contract between the LLM and the renderer. Thesys is betting that's the wrong primitive. Their engine uses a code-like syntax (OpenUI Lang) instead — LLM writes it, renderer executes it. The argument is that LLMs are fundamentally better at generating code than generating structured data, so you get cleaner output and ~67% fewer tokens. The broader vision seems to be a…
Mar 2026 · openui.com · its alternatives →
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APIEval-20▲121An open benchmark for AI agents that test APIs
May 2026 · resources.kusho.ai · its alternatives →
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Hey, I'm Martyn and I recently joined Zuplo. OpenAPI is a huge part of what we do, but getting a document up to scratch out of the gate, especially if you're not super well versed in the actual specification and what you should use and when. So we built a suite of tools to help with this called Rate My OpenAPI. It will score your OpenAPI document out of 100, as well as giving you individual scores in 4 key areas; completeness, SDK generation, security and documentation. Along with the score, you also get details of what the issues, or errors are, as well as guidance on what they actually…
2024 · github.com · its alternatives →
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DocumentAI▲30Process any document to get structured JSON output using AI
2024 · finigami.com · its alternatives →
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We previously open-sourced a rubric for assessing APIs for agent-readiness (carefully designed by some colleagues who are deeply involved with the OpenAPI initiative). We hosted a nice web app for scoring an API according to this rubric (a mix of deterministic and LLM-based assessment), and learned people were hacking it into their CI/CD pipelines for regression testing and longitudinal tracking. So we just released a nice CLI to make that easier, with a decent free tier.
Jun 2026 · github.com · its alternatives →
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The LLM Evaluation Framework. Contribute to confident-ai/deepeval development by creating an account on GitHub.
2023 · github.com · its alternatives →
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Hey HN, Joe and Ethan from Tonic.ai here. We just released a new open-source python package for evaluating the performance of Retrieval Augmented Generation (RAG) systems. Earlier this year, we started developing a RAG-powered app to enable companies to talk to their free-text data safely. During our experimentation, however, we realized that using such a new method meant that there weren’t industry-standards for evaluation metrics to measure the accuracy of RAG performance. We built Tonic Validate Metrics (tvalmetrics, for short) to easily calculate the benchmarks we needed to meet in…
2023 · github.com · its alternatives →
- 19AG
Hey HN, We just shipped a new AI-powered feature... BUT the "AI" piece is largely in the background. Instead of relying on a chatbot, we've integrated AI (with strict input & output guardrails) into a workflow to handle two specific tasks that would be difficult for traditional programming: 1. Identifying the most relevant base URL from HAR files, since it would be tedious to cover every edge case or scenario to omit analytics, tracking, and other network noise. 2. Generating synthetic data for API requests by passing the API context and faker-js functions to GPT-4. The steps are broken down…
2024 · docs.multiple.dev · its alternatives →
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We've been building data pipelines that scrape websites and extract structured data for a while now. If you've done this, you know the drill: you write CSS selectors, the site changes its layout, everything breaks at 2am, and you spend your morning rewriting parsers. LLMs seemed like the obvious fix — just throw the HTML at GPT and ask for JSON. Except in practice, it's more painful than that: - Raw HTML is full of nav bars, footers, and tracking junk that eats your token budget. A typical product page is 80% noise. - LLMs return malformed JSON more often than you'd expect, especially with…
Mar 2026 · github.com · its alternatives →
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Deepmark AI enables a unique testing environment for language models (LLM) assessment on task-specific metrics and on your own data so your GenAI-powered solution has predictable and reliable performance. - IngestAI/deepmark
2023 · github.com · its alternatives →
- 22AF
We’ve built an AI risk assessment tool designed specifically for GenAI/LLM applications. It's still early, but we’d love your feedback. Here’s what it does: 1. it performs comprehensive AI risk assessments by analyzing your codebase against different AI regulation/framework or even internal policies. It identifies potential issues and suggests fixes directly through one click PRs. 2. the first framework the platform supports is OWASP Top 10 for LLM Applications 2025, upcoming framework will be ISO 42001 as well as custom policy documents. 3. we're a small, early stage team, so the…
2025 · gettavo.com · its alternatives →
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Get structured, fully typed, and validated JSON outputs from OpenAI and Anthropic models. - dzhng/zod-gpt
2023 · github.com · its alternatives →
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Jev-shaped (TypeSafe System One) classification wrapper over OpenAI-like clients: probabilities and confidence instead of prose - zhulinchng/jevper
30d ago · github.com · its alternatives →
Also compare
- ManyPI alternatives
- Value likelihoods for OpenAI structured output alternatives
- A tool to benchmark LLM APIs (OpenAI, Claude, local/self-hosted) alternatives
- Klarity – Open-source tool to analyze uncertainty/entropy in LLM output alternatives
- A new benchmark for testing LLMs for deterministic outputs alternatives
- Agents.json – OpenAPI Specification for LLMs alternatives
Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →