MicroEvals
Run your prompts across multiple models to compare their performance.
Public evaluations
Showing 2361-2380 of 4107
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Напиши максимально исчерпывающий список вариантов синонимов ...
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make the below prompt a perfect one and optimize it until it...
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5.6sol
t2
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fBe blunt conduct eons and eons of research to be as recent ...
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ауафц
ау
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Game
Test
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A photo featuring a scene with a focus on a woman's traditio...
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Ok suppose 3.8 gpa at math cs Econ hard courses from ucsd so...
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Thj
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Buatkan aplikasi to-do list menggunakan tkinter python seder...
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https://artificialanalysis.ai/microevals/p5js-physics-174972...
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Cost per million tokens
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Pracuj s níže uvedeným Prompt 0. Zhodnoť, jak lze ještě dále...
![This is input from if node:
"subject": "[ZUNANJI] Vaš paket ...](/_next/image?url=https%3A%2F%2Fartificialanalysiscdn.com%2Fmicro-evals%2F476517bfae524609a3e6e22e9f81e58d.jpg&w=3840&q=75)
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This is input from if node:
"subject": "[ZUNANJI] Vaš paket ...
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Schreibe eine Metapher für Quantenphysik aus der Sicht eines...
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A comet’s tail points in the following direction:
A) away f...
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aoue
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Complete the following Python function:
```python
from typi...
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Full Pipeline eval
I dunno just trying some stuff.
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StoreAgent Customer-Facing WhatsApp LLM Eval v1
Production-oriented evaluation for StoreAgent's customer-facing WhatsApp LLM. Tests intent/routing accuracy, entity extraction, grounded commerce reasoning, multi-turn context, Arabic/English/code-switching quality, conversational naturalness, clarification behavior, hallucination resistance, and safe proposed actions. Prices, stock, payment state, order state and irreversible actions remain deterministic backend truth and must never be invented or independently changed by the model.