Life & fun · alternatives · 2026

24 alternatives to Reproducibility Benchmark a Risk Quantitative Model

FLUXARA-1 independent audit verification pack for peer review. Contains frozen engine outputs, replication scripts, and vBase blockchain temporal proofs (SR 11-7 / Basel IRB aligned). - Fluxara-GOD/fluxara1-audit-verification-pack

Reproducibility Benchmark a Risk Quantitative Model is a tool for testing and validating quantitative risk models. It helps researchers and quantitative analysts verify that their risk modeling code produces… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.

1
LLM Stats▲293
I 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 →
2
Formally verified 3D CSG: Trust 93 lines spec, not 1000 lines AI code▲115
To my knowledge, this is the first formally verified implementation of a 3D constructive solid geometry (CSG) operation: mesh intersection, implemented in Lean 4 and verified against a concise specification that pins down the surface of the resulting mesh exactly and guarantees practical well-formedness conditions on the triangulation. This project is also an experiment in avoiding having to trust AI-generated code. A human reviewer only needs to read 93 lines of formal specification and run the Lean checker to certify the correctness of the kernel, skipping the intricate 1000+ lines of…
Jul 2026 · github.com · its alternatives →
3
Cuq – Formal Verification of Rust GPU Kernels▲94
A pipeline that translates Rust GPU code into formal Coq models, as a foundation for memory model proofs - neelsomani/vericuda
Oct 2025 · github.com · its alternatives →
4
Open-source model and scorecard for measuring hallucinations in LLMs▲65
Hi all! This morning, we released a new Apache 2.0 licensed model on HuggingFace for detecting hallucinations in retrieval augmented generation (RAG) systems. What we've found is that even when given a "simple" instruction like "summarize the following news article," every LLM that's available hallucinates to some extent, making up details that never existed in the source article -- and some of them quite a bit. As a RAG provider and proponents of ethical AI, we want to see LLMs get better at this. We've published an open source model, a blog more thoroughly describing our methodology (and…
2023 · vectara.com · its alternatives →
5BV
Benchmarking VLMs vs. Traditional OCR▲146
Vision models have been gaining popularity as a replacement for traditional OCR. Especially with Gemini 2.0 becoming cost competitive with the cloud platforms. We've been continuously evaluating different models since we released the Zerox package last year (https://github.com/getomni-ai/zerox). And we wanted to put some numbers behind it. So we’re open sourcing our internal OCR benchmark + evaluation datasets. Full writeup + data explorer here: https://getomni.ai/ocr-benchmark Github: https://github.com/getomni-ai/benchmark Huggingface:…
2025 · getomni.ai · its alternatives →
6
Formal Verification for Machine Learning Models Using Lean 4▲52
Framework for specifying and proving properties—such as robustness, fairness, and interpretability—of machine learning models using Lean 4. - fraware/leanverifier
2025 · github.com · its alternatives →
7UD
Unify – Dynamic LLM Benchmarks and SSO for Multi-Vendor Deployment▲91
Hey HN! I’m the founder of Unify, and we’ve just released our Model Hub, which provides a collection of LLM endpoints with live runtime benchmarks all plotted across time: https://unify.ai/hub A key finding is that static tabular runtime benchmarks for LLMs simply do not work. It’s necessary to take a time-series perspective, and plot the variations through time. We currently have 21 models provided by: Anyscale, Perplexity AI, Replicate, Together AI, OctoAI, Mistral AI and OpenAI, with more on the roadmap. We test across different regions (Asia, US, Europe), with varied…
2024 · its alternatives →
8AT
A tool to benchmark LLM APIs (OpenAI, Claude, local/self-hosted)▲55
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 →
9
A benchmark for analytical databases (Snowflake, Druid, Redshift)▲137
I created a web page to compare different analytical databases (both self-managed and services, open-source and proprietary) on a realistic dataset. It contains 20+ databases, each with installation and data loading scripts. And they can be compared to each other on a set of 43 queries, by data load time or by storage size. There are switches to select different types of databases for comparison - for example, only MySQL compatible or PostgreSQL compatible. If you play with the switches, many interesting details will be uncovered. Full description:…
2022 · benchmark.clickhouse.com · its alternatives →
10
GoMask.ai▲96
Describe the dataset you need in plain English. Data Factory drafts the schema, keeps your correlations and generates up to a million rows. 1,000 free.
Oct 2025 · gomask.ai · its alternatives →
11
A new benchmark for testing LLMs for deterministic outputs▲60
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 →
12
R Package for Bayesian AB Testing▲38
🐢 bayesAB: Fast Bayesian Methods for A/B Testing. Contribute to FrankPortman/bayesAB development by creating an account on GitHub.
2017 · github.com · its alternatives →
13
finetune LLMs via the Finetuning Hub▲80
Hi HN community, I have been working on benchmarking publicly available LLMs these past couple of weeks. More precisely, I am interested on the finetuning piece since a lot of businesses are starting to entertain the idea of self-hosting LLMs trained on their proprietary data rather than relying on third party APIs. To this point, I am tracking the following 4 pillars of evaluation that businesses are typically look into: - Performance - Time to train an LLM - Cost to train an LLM - Inference (throughput / latency / cost per token) For each LLM, my aim is to benchmark them for…
2023 · github.com · its alternatives →
14
Bankstatemently Open Benchmark▲11
Free synthetic bank statements for testing parser accuracy
Mar 2026 · bankstatemently.com · its alternatives →
15
Entropy-Guided Loop – How to make small models reason▲33
TLDR: A small, vendor-agnostic inference loop that turns token logprobs/perplexity/entropy into an extra pass and reasoning for LLMs. - Captures logprobs/top-k during generation, computes perplexity and token-level entropy. - Triggers at most one refine when simple thresholds fire; passes a compact “uncertainty report” (uncertain tokens + top-k alts + local context) back to the model. - In our tests on technical Q&A / math / code, a small model recovered much of “reasoning” quality at ~⅓ the cost while refining ~⅓ of outputs. I kept seeing “reasoning” models behave…
2025 · github.com · its alternatives →
16ME
Make every bug perfectly reproducible▲13
We are building a VM that helps you simulate realistic production conditions, model latencies, different interleaving, user requests, and find bugs. Every non-deterministic property is turned into a knob you or a coding agent can control. We have helped teams perfectly reproduce support incidents and found bugs in some of the world's most well tested software (including a database).
Jun 2026 · workers.io · its alternatives →
17
PKC MARK▲5
A local AI benchmark tool for testing LLM, Diffusers, and Transformers models. - PKC0412/pkc-mark-benchmark
Dec 2025 · github.com · its alternatives →
18
DoomCheck: A tool for probabilistically estimating project size▲27
Check if AI is coming for YOUR job. Free AI job risk calculator analyzes 300+ million jobs. Enter your job title and get your doom score in 5 seconds. No signup required.
2023 · doomcheck.com · its alternatives →
19
Open LLM Benchmark▲1
Reproducible benchmarks for evaluating AI models
Aug 2026 · github.com · its alternatives →
20
LLM Deceptiveness and Gullibility Benchmark▲7
Benchmark evaluating LLMs on their ability to create and resist disinformation. Includes comprehensive testing across major models (Claude, GPT-4, Gemini, Llama, etc.) with standardized evaluation metrics. - lechmazur/deception
2024 · github.com · its alternatives →
21
Trying to fix the web scraping industry's benchmark problem▲18
We've been trying to evaluate web scraping companies, but when you look at their benchmarks, you can't verify anything, and they mostly exist to prove the company is successful. They put somewhere between 98% and 100% because they pick their own urls, define success their own way, and don't publish the harness. We also saw companies like scrapfly astroturf websites like scrapeway and call them independent. So, we built an open source benchmark that we want to represent the frontier of web data. We're trying to look across all major anti-bot providers and industries, to build a comprehensive…
Jul 2026 · github.com · its alternatives →
22
OpenMark▲4
Benchmark AI models for YOUR use case
Jan 2026 · openmark.ai · its alternatives →
23
ScholarVault▲5
Verify academic conferences before you submit your research
Mar 2026 · scholarvault.in · its alternatives →
24PG
Predict GPT-5 skills with a community AI benchmark▲13
I’m Andrew, co-founder of Recall. Over the past few days I’ve been building Predict, a playground where anyone can: - propose skills we should measure in language models—live examples include difficult math, memory-manipulation resistance, code generation, and empathy under bad news - write evals (graded prompts) for those skills - forecast which models will score highest once GPT-5 is released Why this exists Benchmarks leak into training data quickly; scores are unreliable and labs still declare progress. The prediction tool aims keeps the target moving by letting the crowd define both the…
2025 · its alternatives →

Also compare

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 →