AI · alternatives · 2026

Predict is a collaborative benchmarking platform where users propose skills to measure in language models, write evaluation prompts for those skills, and forecast which models will perform best when GPT-5 releases. The… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.

1. 1
   Recall Predict▲11

   Ungameable, community-powered AI benchmarks

   2025 · predict.recall.network · its alternatives →

2. 2
   GPT-4▲1,160

   More accurate, less cringe, smoother & useful daily chats!

   2023 · openai.com · its alternatives →

3. 3
   Fully client-side GPT2 prediction visualizer▲153

   Hi HN! I've found this visualization tool immensely helpful over the years for getting an intuition for how an LLM "sees" some piece of text, and with a bit of elbow grease decided to move all compute to client side so I could make it publicly available. I've found it particularly useful for - Understanding exactly how repetition and patterns affect a small LM's ability to predict correctly - Understanding different tokenization patterns and how it affects model output - Getting a general sense of how "hard" different prediction tasks are for GPT-style models Known problems (that I probably…

   2023 · perplexity.vercel.app · its alternatives →

4. 4
   Product Prediction▲255

   I asked GPT-3 to guess what the top makers will build next

   2021 · its alternatives →

5. 5IR
   I Remade the Fake Google Gemini Demo, Except Using GPT-4 and It's Real▲434

   2023 · sagittarius.greg.technology · its alternatives →

6. 6
   gpt-realtime-1.5 by OpenAI▲300

   Tighter instruction adherence in speech agents

   Feb 2026 · developers.openai.com · its alternatives →

7. 7
   Next-token prediction in JavaScript▲88

   What inspired this project today was watching this amazing video by 3Blue1Brown called "But what is a GPT?" on Youtube (https://www.youtube.com/watch?v=wjZofJX0v4M - I highly recommend watching it). I added it to the repo for reference. When it clicked in my head that "knowing a fact" is nearly synonymous with predicting a word (or series of words), I wanted to put it to the test, because it seemed so simple. I chose JavaScript because I can exploit the way it structures objects to aid in the modeling of language. For example: "I want to be at the beach", "I will do it later",…

   2024 · github.com · its alternatives →

8. 8
   PHBench▲398

   Predict the next Series A from a ProductHunt launch

   May 2026 · phbench.com · its alternatives →

9. 9
   GPT-4.1 in the API▲375

   Announcing GPT-4.1, GPT-4.1 mini, & GPT-4.1 nano in the API

   2025 · openai.com · its alternatives →

10. 10
    YourGPT 2.0▲215

    Today we are launching YourGPT 2.0, an update focused on making support, sales, and operational workflows work together more smoothly in one system. This release improves how workflows are built, how the platform connects to external tools, and how context is maintained across long, multi-channel interactions. AI Studio now makes it much easier to create and refine workflows. You can describe the process you want in natural language and the system generates the full workflow for you. It can also help debug issues by showing what happened at each step and explaining where things went off…

    Nov 2025 · yourgpt.ai · its alternatives →

11. 11
    PredictionIO▲172

    Open source machine learning to predict/learn user behavior

    2014 · prediction.io · its alternatives →

12. 12
    Deepmark AI▲97

    LLM benchmarking tool for task-specific metrics on your data

    2023 · github.com · its alternatives →

13. 13IC
    I co-wrote a novel with GPT-3 in 118 hours▲55

    2020 · docs.google.com · its alternatives →

14. 14LT
    Learning to (Learn at Test Time)▲13

    I’m excited to share a project I’ve been working on for over a year, which I believe will fundamentally change our approach to language models. We’ve designed a new architecture, which replaces the hidden state of an RNN with a machine learning model. This model compresses context through actual gradient descent on input tokens. We call our method “Test-Time-Training layers.” TTT layers directly replace attention, and unlock linear complexity architectures with expressive memory, allowing us to train LLMs with millions (someday billions) of tokens in context. Our instantiations, TTT-Linear…

    2024 · its alternatives →

15. 15AL
    Ask LLMs to predict anything based on news▲25

    Hi HN! We partnered with the Atlas team to build a tool called AI Predict [0] that allows anyone to ask any question about the future and get a thoroughly researched, AI-generated prediction on how likely it is to be true. How it works: Atlas replicated a Berkeley paper [1] that showed LLMs could make predictions as accurate as the crowd. We’re using a mix of models from OpenAI and Anthropic, with information retrieval powered by NewsCatcher [2]. The system is live and fully functional, though it might struggle with hyper-local questions outside of the public domain (e.g., “Will I have…

    2024 · aipredict.fun · its alternatives →

16. 16
    ML-Dev-Bench – Benchmarking AI Agents on Real-World AI Workflows▲6

    We’re excited to share ML-Dev-Bench, a new open-source benchmark that tests AI agents on real-world ML development tasks. Unlike typical coding challenges or Kaggle-style competitions, our benchmark simulates end-to-end ML workflows including: - Dataset handling and preprocessing - Debugging model and code failures - Implementing new model architectures - Fine-tuning and improving existing models With 30 diverse tasks, ML-Dev-Bench evaluates agents across critical stages of ML development. To complement this, we built Calipers, a framework that provides systematic performance evaluation and…

    2025 · github.com · its alternatives →

17. 17AS
    A surprisingly effective way to predict token importance in LLM prompts▲14

    We explored a novel method to gauge the significance of tokens in prompts given to large language models, without needing direct model access. Essentially, we just did an ablation study on the prompt using cosine similarity of the embeddings as the measure. We got surprisingly promising results when comparing this really simple approach to integrated gradients. Curious to hear thoughts from the community!

    2023 · heatmap.demos.watchful.io · its alternatives →

18. 18ΤB
    Τ³-Bench is out – can agents handle complex docs and live calls?▲12

    τ-Bench is an open benchmark for evaluating AI agents on grounded, multi-turn customer service tasks with verifiable outcomes. It's been great to see the community adopt it since launch — this is now the third iteration. With τ³-Bench, we're extending it to two new settings: knowledge-intensive retrieval and full-duplex voice. τ-Knowledge: agents must navigate ~700 interconnected policy documents to complete multi-step tasks. Best frontier model (GPT-5.2, high reasoning) hits ~25%. The surprising part: even when you hand the model the exact documents it needs, performance only reaches ~40%.…

    Mar 2026 · its alternatives →

19. 19
    Minimal LLM Post-Training Experiments on an 8GB GPU (SFT, DPO, GRPO)▲21

    Minimal, readable LLM post-training experiments on one 8GB GPU. Measures forgetting, seed variance, and RL emergence. - pochenai/nano-llm-posttraining

    Aug 2026 · github.com · its alternatives →

20. 20IM
    I made a game testing your prompting skills▲10

    Also inspired by this HN submission: https://www.chiark.greenend.org.uk/~sgtatham/quasiblog/findl... The model is gpt-4o-mini-2024-07-18.

    2024 · app4.hc11.org · its alternatives →

21. 21
    Predictionary▲5

    Let’s see who can predict the future.

    Feb 2026 · predictionary.com · its alternatives →

22. 22IP
    I published 100 AI generated books on Amazon▲20

    To be specific, the content is generated by a GPT-2 based model. https://amzn.to/2TCc0v2 Let me know if you have any questions :-)

    2020 · its alternatives →

23. 23TT
    Trained Tiny Tales GPT(30M model)from scratch and deployed in $15▲18

    For the last few weeks, I have been working on training an LLM from scratch and deploying it in production on Google Cloud Platform. Finally, I trained a 30 million parameter model on 1 billion tokens and deployed it as a web service. You can access the LLM using this site - https://kunalmishra.info The following steps were taken to build Tiny Tales GPT 1. Downloaded and preprocessed 8GB of dataset using multiprocessing library. 2. Tokenized the data using byte pair encoding to create 1 billion tokens sharded in different bin files. 3. Defined a training setup and trained the model…

    2024 · kunalmishra.info · its alternatives →

24. 24BA
    Benchmark AI on your actual code (GPT-5, Claude, Grok, Gemini, o3)▲7

    I built CodeLens.AI - a tool that compares how 6 top LLMs (GPT-5, Claude Opus 4.1, Claude Sonnet 4.5, Grok 4, Gemini 2.5 Pro, o3) handle your actual code tasks. How it works: - Upload code + describe task (refactoring, security review, architecture, etc.) - All 6 models run in parallel (~2-5 min) - See side-by-side comparison with AI judge scores - Community votes on winners (blind voting) - Each evaluation gets reflected in the overall AI model leaderboard, showing us best ones Why I built this: Existing benchmarks (HumanEval, SWE-Bench) don't reflect real-world developer tasks. I wanted to…

    2025 · codelens.ai · its 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 →