Recall Predict
Ungameable, community-powered AI benchmarks
Recall Predict launched on August 2, 2025 with 11 votes, #1,601 of 8,530 launches that month and more than 50% of that year's launches. The site was still online in September 2026, 1 year on.
What it does
Predict, by Recall, is the world’s first ungameable, community-led benchmark for frontier models. Join thousands of AI researchers, developers, and enthusiasts in evaluating and building a benchmark for OpenAI’s upcoming GPT-5 model.
Discover the best AI models, tested on the skills that matter to you.
A community of 159,980 users made 7.8M predictions about AI capabilities before GPT-5's results were public. Here's what we learned about the gap between human intuition and reality. But human intuition about AI progress told a more complex story... Analysis of 7.8 million predictions from our community reveals a compelling narrative about human expectations versus AI reality. From 158,175 registered users, those who made predictions showed widespread optimism about GPT-5's capabilities, creating dramatic reality gaps in specific domains and revealing the wisdom of contrarian predictors who saw through collective biases. When we measured human expectations against GPT-5's actual…from predict.recall.network
Does a similar job
all alternatives →- PGPredict GPT-5 skills with a community AI benchmark2025 · ▲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…
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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…
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