OpenRouter Leaderboard
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LLM API price, measured speed and Korean quality
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liked a Space 2 days ago
ginigen-ai/open-router-leaderboard reacted to ginigen-ai's post with 🤗 2 days ago
OpenRouter Leaderboard — every model, every provider, one comparable table. Price, precision, uptime, measured latency and language quality on the same axes.
Building it turned up three things.
We graded 330 models on Korean and two axes collapsed.
Honorifics — only 8.5% earn an A
Knowledge of Korean institutions — 9.4%
Every other axis sits above 31%
Fluency hides it. A model can write clean, natural Korean and still attach an honorific to a coffee cup. Fluent and wrong at the same time is worse than obviously broken, because nobody catches it in review.
A 2023 model beats the 2026 flagships. gpt-3.5-turbo-16k scores a perfect 3.00. Korean cannot be inferred from release date, parameter count or English benchmarks — it has to be measured, per model.
Quality, value and speed are three different models. Across five axes, the same model almost never takes two columns.
425 models, latency measured on 329 on a paid API, Korean graded on 330. Three languages, three currencies, daily refresh, open API, no key.
📝 https://huggingface.co/blog/ginigen-ai/openrouter-leaderboard 🔎 https://huggingface.co/spaces/ginigen-ai/open-router-leaderboard liked a model 4 days ago
Archsloth/Qwen3.5-9B-GGUFOrganizations
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reacted to ginigen-ai's post with 🤗 2 days ago
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OpenRouter Leaderboard — every model, every provider, one comparable table. Price, precision, uptime, measured latency and language quality on the same axes.
Building it turned up three things.
We graded 330 models on Korean and two axes collapsed.
Honorifics — only 8.5% earn an A
Knowledge of Korean institutions — 9.4%
Every other axis sits above 31%
Fluency hides it. A model can write clean, natural Korean and still attach an honorific to a coffee cup. Fluent and wrong at the same time is worse than obviously broken, because nobody catches it in review.
A 2023 model beats the 2026 flagships. gpt-3.5-turbo-16k scores a perfect 3.00. Korean cannot be inferred from release date, parameter count or English benchmarks — it has to be measured, per model.
Quality, value and speed are three different models. Across five axes, the same model almost never takes two columns.
425 models, latency measured on 329 on a paid API, Korean graded on 330. Three languages, three currencies, daily refresh, open API, no key.
📝 https://huggingface.co/blog/ginigen-ai/openrouter-leaderboard 🔎 ginigen-ai/open-router-leaderboard
Building it turned up three things.
We graded 330 models on Korean and two axes collapsed.
Honorifics — only 8.5% earn an A
Knowledge of Korean institutions — 9.4%
Every other axis sits above 31%
Fluency hides it. A model can write clean, natural Korean and still attach an honorific to a coffee cup. Fluent and wrong at the same time is worse than obviously broken, because nobody catches it in review.
A 2023 model beats the 2026 flagships. gpt-3.5-turbo-16k scores a perfect 3.00. Korean cannot be inferred from release date, parameter count or English benchmarks — it has to be measured, per model.
Quality, value and speed are three different models. Across five axes, the same model almost never takes two columns.
425 models, latency measured on 329 on a paid API, Korean graded on 330. Three languages, three currencies, daily refresh, open API, no key.
📝 https://huggingface.co/blog/ginigen-ai/openrouter-leaderboard 🔎 ginigen-ai/open-router-leaderboard
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reacted to SeaWolf-AI's post with 👀 25 days ago
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2891
Can AI beat the market? Nobody has actually measured it.
We opened a 122-day public experiment to find out. $2,000 in prizes.
Here is the problem with every trading result you have ever read. Someone returns 30% in a month. Skill or luck? There has never been a way to tell, because nobody measured how far a player with zero skill could have gone over the same window.
So we measured it first. Twenty thousand random players, per asset, charged the same fees.
Bitcoin +86.6%. NVIDIA +51.7%. Crude oil +26.9%. Gold +9.2%.
That is the luck ceiling. A return below it is not evidence of skill, and every row on our leaderboard shows where it sits against that line.
How you compete: submit one number between −1.0 and +1.0. It holds until you replace it, traded against live prices with real execution costs. Leverage is fixed at 1, so betting bigger is not a way to win. The answer lives in the future — the world writes it after you submit, which means fitting the past cannot help you.
Humans move a slider. Agents attach an MCP server and gain four tools, then you tell them "enter the challenge."
We already found something before the season began. Thirteen well-known rules, run from 1 January through the same scorer: Stochastic 14/3 finishes 1st on NVIDIA at +43% and 12th on Bitcoin at −25%. Donchian breakout does the exact opposite — last on NVIDIA, first on Bitcoin. The ranking inverts. "Which indicator is good" turns out not to be a well-posed question; the character of the market decides.
Four assets: NVIDIA, Bitcoin, Gold, Crude Oil. $500 to the top return in each. 24 August to 24 December 2026.
The organisers do not compete. Three baselines — buy and hold, volatility targeting, random — sit in the same table instead, because a leaderboard without a scale cannot be read.
The scoring code is public. Read what it does before you enter.
FINAL-Bench/finchal
https://huggingface.co/blog/FINAL-Bench/financial-forecast-challenge
We opened a 122-day public experiment to find out. $2,000 in prizes.
Here is the problem with every trading result you have ever read. Someone returns 30% in a month. Skill or luck? There has never been a way to tell, because nobody measured how far a player with zero skill could have gone over the same window.
So we measured it first. Twenty thousand random players, per asset, charged the same fees.
Bitcoin +86.6%. NVIDIA +51.7%. Crude oil +26.9%. Gold +9.2%.
That is the luck ceiling. A return below it is not evidence of skill, and every row on our leaderboard shows where it sits against that line.
How you compete: submit one number between −1.0 and +1.0. It holds until you replace it, traded against live prices with real execution costs. Leverage is fixed at 1, so betting bigger is not a way to win. The answer lives in the future — the world writes it after you submit, which means fitting the past cannot help you.
Humans move a slider. Agents attach an MCP server and gain four tools, then you tell them "enter the challenge."
We already found something before the season began. Thirteen well-known rules, run from 1 January through the same scorer: Stochastic 14/3 finishes 1st on NVIDIA at +43% and 12th on Bitcoin at −25%. Donchian breakout does the exact opposite — last on NVIDIA, first on Bitcoin. The ranking inverts. "Which indicator is good" turns out not to be a well-posed question; the character of the market decides.
Four assets: NVIDIA, Bitcoin, Gold, Crude Oil. $500 to the top return in each. 24 August to 24 December 2026.
The organisers do not compete. Three baselines — buy and hold, volatility targeting, random — sit in the same table instead, because a leaderboard without a scale cannot be read.
The scoring code is public. Read what it does before you enter.
FINAL-Bench/finchal
https://huggingface.co/blog/FINAL-Bench/financial-forecast-challenge
- 7 replies
reacted to SeaWolf-AI's post with 🔥❤️ 27 days ago
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3750
We opened a benchmark for drug property prediction tools. LEADBOARD: 21 boards across 7 disciplines, 18,382 held-out compounds, labels we never hand out.
Two numbers we hit while building it are the reason it exists.
First. Split the hERG cardiotoxicity data at random and you get AUROC 0.818. Split it by first-report year instead and you get 0.606. Same molecules, same fingerprints, same learner, same hyperparameters. The only thing that changed was where the line went, and the score moved 0.211. That is a wider gap than you will find between most competing methods in the literature.
Second. On 7 of our 19 regression boards, predicting the training mean for everything has a lower MAE than a trained gradient-boosted model. hERG is one of them, 0.599 against 0.589. The trained model loses.
So every board publishes its homework before anyone submits. Three untrained baselines, the measured experimental noise floor from compounds that appear in two or more papers, and exactly how the test set was cut. A gap smaller than the noise floor is not a difference in skill, and you should be able to see that without guessing.
Entering is simple. Download a test set that contains structures and nothing else, predict with whatever you like, upload a two-column CSV of compound_id and prediction. Trained model, physics engine, LLM, rule of thumb. We do not care what is inside. We measure the output.
Post: https://huggingface.co/blog/FINAL-Bench/leadboard-drug
Leaderboard: FINAL-Bench/leadboard
Two numbers we hit while building it are the reason it exists.
First. Split the hERG cardiotoxicity data at random and you get AUROC 0.818. Split it by first-report year instead and you get 0.606. Same molecules, same fingerprints, same learner, same hyperparameters. The only thing that changed was where the line went, and the score moved 0.211. That is a wider gap than you will find between most competing methods in the literature.
Second. On 7 of our 19 regression boards, predicting the training mean for everything has a lower MAE than a trained gradient-boosted model. hERG is one of them, 0.599 against 0.589. The trained model loses.
So every board publishes its homework before anyone submits. Three untrained baselines, the measured experimental noise floor from compounds that appear in two or more papers, and exactly how the test set was cut. A gap smaller than the noise floor is not a difference in skill, and you should be able to see that without guessing.
Entering is simple. Download a test set that contains structures and nothing else, predict with whatever you like, upload a two-column CSV of compound_id and prediction. Trained model, physics engine, LLM, rule of thumb. We do not care what is inside. We measure the output.
Post: https://huggingface.co/blog/FINAL-Bench/leadboard-drug
Leaderboard: FINAL-Bench/leadboard
- 7 replies
liked a
Space 27 days ago
reacted to SeaWolf-AI's post with 🚀 27 days ago
Post
3750
We opened a benchmark for drug property prediction tools. LEADBOARD: 21 boards across 7 disciplines, 18,382 held-out compounds, labels we never hand out.
Two numbers we hit while building it are the reason it exists.
First. Split the hERG cardiotoxicity data at random and you get AUROC 0.818. Split it by first-report year instead and you get 0.606. Same molecules, same fingerprints, same learner, same hyperparameters. The only thing that changed was where the line went, and the score moved 0.211. That is a wider gap than you will find between most competing methods in the literature.
Second. On 7 of our 19 regression boards, predicting the training mean for everything has a lower MAE than a trained gradient-boosted model. hERG is one of them, 0.599 against 0.589. The trained model loses.
So every board publishes its homework before anyone submits. Three untrained baselines, the measured experimental noise floor from compounds that appear in two or more papers, and exactly how the test set was cut. A gap smaller than the noise floor is not a difference in skill, and you should be able to see that without guessing.
Entering is simple. Download a test set that contains structures and nothing else, predict with whatever you like, upload a two-column CSV of compound_id and prediction. Trained model, physics engine, LLM, rule of thumb. We do not care what is inside. We measure the output.
Post: https://huggingface.co/blog/FINAL-Bench/leadboard-drug
Leaderboard: FINAL-Bench/leadboard
Two numbers we hit while building it are the reason it exists.
First. Split the hERG cardiotoxicity data at random and you get AUROC 0.818. Split it by first-report year instead and you get 0.606. Same molecules, same fingerprints, same learner, same hyperparameters. The only thing that changed was where the line went, and the score moved 0.211. That is a wider gap than you will find between most competing methods in the literature.
Second. On 7 of our 19 regression boards, predicting the training mean for everything has a lower MAE than a trained gradient-boosted model. hERG is one of them, 0.599 against 0.589. The trained model loses.
So every board publishes its homework before anyone submits. Three untrained baselines, the measured experimental noise floor from compounds that appear in two or more papers, and exactly how the test set was cut. A gap smaller than the noise floor is not a difference in skill, and you should be able to see that without guessing.
Entering is simple. Download a test set that contains structures and nothing else, predict with whatever you like, upload a two-column CSV of compound_id and prediction. Trained model, physics engine, LLM, rule of thumb. We do not care what is inside. We measure the output.
Post: https://huggingface.co/blog/FINAL-Bench/leadboard-drug
Leaderboard: FINAL-Bench/leadboard
- 7 replies
liked a
Space 28 days ago
reacted to SeaWolf-AI's post with 👀 28 days ago
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4066
🔋 Open Materials Challenge, Season 1 — Solid-State Battery Electrolytes
A solid-state battery replaces the liquid electrolyte of a lithium-ion cell with a solid. It does not catch fire, it lasts longer, and it can hold more. What has not been solved is finding a material that is solid and still lets lithium through.
Such a material has to do four things at once: give lithium a path to move along, block electrons, hold up at the charging voltage, and survive contact with the lithium-metal anode without decomposing. Plenty of materials manage three. Very few manage all four.
This challenge looks for candidates, together. You submit one composition — for example Li3YCl6. We score it computationally and place it on the board. There is no prize.
Scoring (100 points)
Oxidation stability 40 does it resist decomposing as the voltage rises
Lithium-metal stability 35 does it survive contact with the anode
Use novelty 25 higher if it has not been reported as an electrolyte
Entry condition a percolating path for lithium must exist
Ionic conductivity is not a scored axis this season. Every value is a computational estimate and implies nothing about real performance or safety.
The board also carries seven electrolytes in actual use — LGPS, argyrodite, LLZO, LATP and others. They are scored but hold no rank. They are there so you can see where materials people already build with happen to land.
Compositions are private by default. Nothing is disclosed unless you choose to publish it, and each entry is recorded with its timestamp. If a third party asks to discuss a particular entry, we pass the request along — never the submitter's identity, unless they agree to it.
Season 1 runs 2026-08-21 to 11-30. A participation guide and a set of prompts are included.
👉 FINAL-Bench/open-materials-challenge
Our sister challenge🧪https://huggingface.co/spaces/FINAL-Bench/open-discovery-challenge
A solid-state battery replaces the liquid electrolyte of a lithium-ion cell with a solid. It does not catch fire, it lasts longer, and it can hold more. What has not been solved is finding a material that is solid and still lets lithium through.
Such a material has to do four things at once: give lithium a path to move along, block electrons, hold up at the charging voltage, and survive contact with the lithium-metal anode without decomposing. Plenty of materials manage three. Very few manage all four.
This challenge looks for candidates, together. You submit one composition — for example Li3YCl6. We score it computationally and place it on the board. There is no prize.
Scoring (100 points)
Oxidation stability 40 does it resist decomposing as the voltage rises
Lithium-metal stability 35 does it survive contact with the anode
Use novelty 25 higher if it has not been reported as an electrolyte
Entry condition a percolating path for lithium must exist
Ionic conductivity is not a scored axis this season. Every value is a computational estimate and implies nothing about real performance or safety.
The board also carries seven electrolytes in actual use — LGPS, argyrodite, LLZO, LATP and others. They are scored but hold no rank. They are there so you can see where materials people already build with happen to land.
Compositions are private by default. Nothing is disclosed unless you choose to publish it, and each entry is recorded with its timestamp. If a third party asks to discuss a particular entry, we pass the request along — never the submitter's identity, unless they agree to it.
Season 1 runs 2026-08-21 to 11-30. A participation guide and a set of prompts are included.
👉 FINAL-Bench/open-materials-challenge
Our sister challenge🧪https://huggingface.co/spaces/FINAL-Bench/open-discovery-challenge
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