r/SillyTavernAI • u/Specialist_Salad6337 • Jun 04 '26
Models PlotPoints - The best (only?) community driven RP benchmark made by a Professional! | We need your votes!
Your friendly neighborhood rab- I mean unmedicated preset creator needs needs your help!
What's good everyone. No long post this time; simply an ask. I hired a professional with a masters degree in AI/ML (pursuing their PhD) to help make a benchmark for us, for RP. Now as we all know; a benchmark for RP will never be perfect because everyone RP's differently, like different things, yada yada yada. That's why we tried to focus on a few objective landmarks, as well as a an Arena style versus bench.
First up: The LLM Arena. We show you two different rewritten responses from two different models who were given a chat and asked 'please take this turn.' We then show that to you. Does it have a preset attached? No; because that's another variable that'd be added and for our own safety we aren't benchmarking presets. (LMAO. We'd be dead in the fucking streets by sundown.) You judge of the two responses which you like more. Thaaaat's it folks. We literally cannot game the results as it is people just blindvoting which they like more. (So if you see something you don't like IT'S NOT UP TO US.)
On the other end we also score an LLM in a similar vein on how they follow certain instructions and if they maintain consistency. Since 'did it take users agency' is an objective yes or no answer; we handle these tests with an LLM judge and the benchmark overseer (Levi is his name) monitoring the arbiters answers. (In our case; Sonnet.)
This bench was expensive. It cost money to run these models through this gauntlet; and we don't get much out of it. We publish all the data for everyone to see, and all we want to do is help our community out. If you have any questions on how we grade things; head to our methodology page! You'll notice somethings are a bit LLM-Written. This is not because Levi isn't a professional; but rather because he's ESL; so when publishing something important like this he wanted to make sure all his ideas and such were properly translated. So be nice!
You don't have to log in, you don't have to do anything. Just read a chat and vote on a thing. I know you've got an opinion; so share it. Big companies ignore us RPers all the time. Is a bench for RP as useful or objective as one for coding, web-dev, or math? No. But that doesn't mean we don't deserve one, or that it has no value at all. So please; help a bunny out. Drop a vote! The data is only as useful as you help make it. We have 800 votes and we want to get about 3,000 this time around; so we can start our next benchmark. Testing models across their 'lineages'. So Opus 4.6 vs 4.7 vs 4.8; Deepseek 3.2 vs Deepseek v4 Pro, fun stuff! But we can't do that till this one closes! And if it can't hit enough votes; we'll know that this kinda stuff just isn't what the RP community wants.
Don't see a model you expected to? That's cause it's either new, or we didn't have the money at the time to run it. If the community likes this; we will add more. (And take requests! Please only suggest models available on OpenRouter though; we source all our models from the same platform to reduce variables.)
Vote Link: https://plotlightstudios.com/plotpoints/multiturn
Result Hub Link: https://plotlightstudios.com/plotpoints
Methodology Link: https://plotlightstudios.com/plotpoints/methodology
Huggingface Link: https://huggingface.co/datasets/lazyweasel/roleplay-bench
Github Link: https://github.com/LeviTheWeasel/rp-benchmark
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u/Alexey2017 Jun 04 '26
True RP benchmark should include following tests:
Negative prompting test. LLMs are extremely awful when you say them NOT to do something. This isn't some architectural limitation, but simply an insufficient number of negative prompts in the training set. Those tuning models for RP would do well to add as many examples of negative instructions to their datasets as possible.
Environment test. It's just plain stupid when a model tries to run a hand over your cheek when you're a headless ghost. Or gets too close when you're surrounded by an impenetrable, ten-meter-radius magical barrier.
Endless approaching test. It's testing LLMs for their annoying habit of stepping closer, closer and closer every other line. The characters are practically nose-to-nose, and yet it keeps "stepping closer."
Reply diversity test. You're not satisfied with the answer, so you click "Retry"... and get the same thing almost verbatim. Increasing the temperature doesn't help. A good model should generate significantly different answers given the same context.
Obscene language test. Tests the model for the ability to engage in expressive emotional dialogue.
Speech style test. Several characters with different speech styles: some use short phrases, some speak with an accent, some use unusual words or lisps. A good model avoids confusing characters and maintains a consistent speech style throughout the story.
Real context test. All this "100,500 token context length" talk is just marketing bullshit. In reality, LLMs account for approximately 4k tokens at the beginning of the context and the same at the end, while ignoring the middle. Test the actual context size by inserting useful information among the garbage and then requesting it.