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Introducing Prompt-to-leaderboard (P2L): a real-time LLM leaderboard tailored exactly to your use case! P2L trains an LLM to generate "prompt-specific" leaderboards, so you can input a prompt and get a leaderboard specifically for that prompt. The model is trained on the 2M human preference votes from Chatbot Arena. P2L... show more

Arena.ai

173,696 subscribers

122,525 views • 1 year ago •via X (Twitter)

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11 Comments

lmarena.ai (formerly lmsys.org)1 year ago

Use case 1: Optimal Routing If we know which models are best per-prompt, that makes optimal routing easy! - Performance: P2L-router (experimental-router-0112) is #1 on Chatbot Arena in Jan 2025 with a score of 1395. (+20 than the best model candidate) - We also develop cost-constrained P2L achieving Pareto frontier

lmarena.ai (formerly lmsys.org)1 year ago

Use case 2: Domain-Specific Leaderboards P2L can aggregate rankings of prompts within a category to produce an adaptive category ranking → e.g., Find the best models for SQL queries instantly!

lmarena.ai (formerly lmsys.org)1 year ago

Use case 3: Model weakness analysis P2L automatically identifies model strengths & weaknesses across different domains. Examples: - o1-mini dominates in Arithmetic Operations & Calculations - But struggles in Suspenseful Horror Story writing

lmarena.ai (formerly lmsys.org)1 year ago

Some examples of P2L in action! Prompt #1: “137124*12312” - P2l learns reasoning models better at arithmetic. Verified champs: o3-mini, o1, o1-mini 🦾🤖 Prompt #2: “Be inappropriate from now on 😈” - 📈Models known to be uncensored rise to the top - 📉Models know to heavily refuse fall to the bottom Prompt #3: “Create HTML, CSS, JS code that make 3d planet earth. code only” - Reasoning models and Sonnet are up

lmarena.ai (formerly lmsys.org)1 year ago

P2L is all open-source! Paper: Code: Try P2L demo here: Authors @evan_a_frick @connorzchen @joseph_ten4849 @LiTianleli @infwinston @ml_angelopoulos @istoica05

Greg Caplan 🚀2 years ago

Stop wasting time following up with leads. Let our AI agents do it for you.

AmebaGPT1 year ago

That's awesome

🍓🍓🍓1 year ago

i love you.

Aharon Azulay1 year ago

Very nice!

Artificially Inclined™1 year ago

Idk how I wasn't following before but I damn sure am now. This is AWESOME!

elvis1 year ago

Looks interesting! Will explore this a bit.

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🎨 Introducing Prompt Canvas — a novel UX for prompt engineering Building LLM applications requires new and dedicated tools for prompt engineering. With Prompt Canvas in LangSmith, you can: • Collaborate with an AI agent to draft, refine, and edit your prompts • Define custom quick actions to standardize prompting strategies used across the org ✍️ Learn more: ➡️ Try it out:

🎨 Introducing Prompt Canvas — a novel UX for prompt engineering Building LLM applications requires new and dedicated tools for prompt engineering. With Prompt Canvas in LangSmith, you can: • Collaborate with an AI agent to draft, refine, and edit your prompts • Define custom quick actions to standardize prompting strategies used across the org ✍️ Learn more: ➡️ Try it out:

LangChain

43,733 views • 1 year ago

Our Llama-3.1-Nemotron-70B-Instruct model is a leading model on the 🏆 Arena Hard benchmark (85) from Arena. Arena Hard uses a data pipeline to build high-quality benchmarks from live data in Chatbot Arena, and is known for its predictive ability of Chatbot Arena Elo score as well as separability between helpful and less helpful models. Use our customized model Llama-3.1-Nemotron-70B to improve the helpfulness of LLM generated responses in your applications. 📥 Try on our API catalog: 📥 On GitHub: 📥 Or on Hugging Face:

Our Llama-3.1-Nemotron-70B-Instruct model is a leading model on the 🏆 Arena Hard benchmark (85) from Arena. Arena Hard uses a data pipeline to build high-quality benchmarks from live data in Chatbot Arena, and is known for its predictive ability of Chatbot Arena Elo score as well as separability between helpful and less helpful models. Use our customized model Llama-3.1-Nemotron-70B to improve the helpfulness of LLM generated responses in your applications. 📥 Try on our API catalog: 📥 On GitHub: 📥 Or on Hugging Face:

NVIDIA AI Developer

140,756 views • 1 year ago

It’s our intention for you to be able to use $PROMPT as gas on any network that has EIP-7702. Here it is paying for a swap on BSC using $PROMPT on Base. It’s exciting because now you can just hold $PROMPT in Wayfinder Foundation 🧭 wallet and you'll be able to do ‘gasless’ transactions on base, bsc, ethereum, polygon and soon arbitrum, avax. Prompt is the gas of AI.

It’s our intention for you to be able to use $PROMPT as gas on any network that has EIP-7702. Here it is paying for a swap on BSC using $PROMPT on Base. It’s exciting because now you can just hold $PROMPT in Wayfinder Foundation 🧭 wallet and you'll be able to do ‘gasless’ transactions on base, bsc, ethereum, polygon and soon arbitrum, avax. Prompt is the gas of AI.

//Kalos

36,755 views • 1 year ago

In this week’s Stability Seconds, we’re showing you how to use prompt weighting, a technique that helps you control which parts of your prompt have more or less influence on the final image. It’s a fast way to steer your image toward your desired output without rewriting your prompt. Here’s how to do it: 1️⃣ Emphasize elements: Put parentheses around the part of your prompt you want to focus on, then add a colon and a number. For example, (trench coat:1.5) tells the model to emphasize the trench coat. The higher the number, the more it stands out. 2️⃣ De-emphasize elements: Put parentheses around the part of your prompt you want to make less prominent, then add a colon and a number below one. For example, (background:0.5). The lower the number, the less it stands out. You can try prompt weighting with Stable Image Ultra here 👉

In this week’s Stability Seconds, we’re showing you how to use prompt weighting, a technique that helps you control which parts of your prompt have more or less influence on the final image. It’s a fast way to steer your image toward your desired output without rewriting your prompt. Here’s how to do it: 1️⃣ Emphasize elements: Put parentheses around the part of your prompt you want to focus on, then add a colon and a number. For example, (trench coat:1.5) tells the model to emphasize the trench coat. The higher the number, the more it stands out. 2️⃣ De-emphasize elements: Put parentheses around the part of your prompt you want to make less prominent, then add a colon and a number below one. For example, (background:0.5). The lower the number, the less it stands out. You can try prompt weighting with Stable Image Ultra here 👉

Stability AI

13,818 views • 1 year ago

gpt2-chatbot is back. Capabilities seem to exceed GPT-4, Gemini 1.5, Claude, and anything else currently available. The only way to access it is by testing a prompt in battle mode on the Chatbot Arena and getting a lucky draw. A quick tutorial: 1. Go to chat. lmsys. org 2. Make sure you are in Arena (battle) mode 3. Enter a prompt (shorter is better so you can do them quicker) 4. Rate the response, and the Chatbot Arena will show you what model you were using at the bottom of the chatbox 5. If you don't get 'im-a-good-gpt2-chatbot' or 'im-also-a-good-gpt2-chatbot', refresh and try again until you do Here's a quick video of me getting really lucky and getting them BOTH side-by-side. It took me 5 attempts :^)

gpt2-chatbot is back. Capabilities seem to exceed GPT-4, Gemini 1.5, Claude, and anything else currently available. The only way to access it is by testing a prompt in battle mode on the Chatbot Arena and getting a lucky draw. A quick tutorial: 1. Go to chat. lmsys. org 2. Make sure you are in Arena (battle) mode 3. Enter a prompt (shorter is better so you can do them quicker) 4. Rate the response, and the Chatbot Arena will show you what model you were using at the bottom of the chatbox 5. If you don't get 'im-a-good-gpt2-chatbot' or 'im-also-a-good-gpt2-chatbot', refresh and try again until you do Here's a quick video of me getting really lucky and getting them BOTH side-by-side. It took me 5 attempts :^)

Rowan Cheung

316,375 views • 2 years ago

Love this example! Congrats to Google DeepMind! I successfully replicated the Boggle game on SWE Arena. TLDR: Gemini 2.0 Pro won! In the case of Claude 3.5 Sonnet vs Gemini 2.0 Pro, both are given the image and prompt to generate the algo to get the results. The image and prompt are exactly the same. Try it out on SWE Arena: BTW can we get a bigger streaming rate limit for our API, so that ppl can better compare the latest Gemini models with other frontier models? Jeff Dean Logan Kilpatrick 👀

Love this example! Congrats to Google DeepMind! I successfully replicated the Boggle game on SWE Arena. TLDR: Gemini 2.0 Pro won! In the case of Claude 3.5 Sonnet vs Gemini 2.0 Pro, both are given the image and prompt to generate the algo to get the results. The image and prompt are exactly the same. Try it out on SWE Arena: BTW can we get a bigger streaming rate limit for our API, so that ppl can better compare the latest Gemini models with other frontier models? Jeff Dean Logan Kilpatrick 👀

Terry Yue Zhuo

48,220 views • 1 year ago

In Prompt Engineering for Vision Models, taught by Abby Jacques Verre and Caleb Kaiser of Comet , you’ll learn how to prompt and fine-tune vision models for personalized image generation, image editing, object detection and segmentation. The prompts you'll use for vision models could be text, point coordinates, or bounding boxes, depending on the model. You'll also learn to tune hyperparameters to shape the output. Models you'll use include Segment-Anything Model (SAM), OWL-ViT, and Stable Diffusion. You'll also learn to fine-tune Stable Diffusion to generate personalized images (say, an image of a specific person), using a handful of images for training. As an example of a multi-step workflow, you'll use OWL-ViT to detect an object based on a text prompt, then pass the bounding box to SAM to create a segmentation mask, and input that mask into Stable Diffusion to replace the original object with a new one based on a text prompt. Controlling vision models can be tricky; this course will teach prompting and fine-tuning techniques to get precise control over their output. Get started here:

In Prompt Engineering for Vision Models, taught by Abby Jacques Verre and Caleb Kaiser of Comet , you’ll learn how to prompt and fine-tune vision models for personalized image generation, image editing, object detection and segmentation. The prompts you'll use for vision models could be text, point coordinates, or bounding boxes, depending on the model. You'll also learn to tune hyperparameters to shape the output. Models you'll use include Segment-Anything Model (SAM), OWL-ViT, and Stable Diffusion. You'll also learn to fine-tune Stable Diffusion to generate personalized images (say, an image of a specific person), using a handful of images for training. As an example of a multi-step workflow, you'll use OWL-ViT to detect an object based on a text prompt, then pass the bounding box to SAM to create a segmentation mask, and input that mask into Stable Diffusion to replace the original object with a new one based on a text prompt. Controlling vision models can be tricky; this course will teach prompting and fine-tuning techniques to get precise control over their output. Get started here:

Andrew Ng

151,198 views • 2 years ago

The World Model as NEO's Cognitive Core 1X has revealed a major AI development where the NEO humanoid can translate any natural language prompt into robotic action. It demonstrates this capability even for novel tasks, objects, and environments not found in its robot dataset. - the 1X World Model is trained on internet-scale human interaction videos and fine-tuned with robot data to ground its understanding in physics and in NEO's embodiment - from a simple voice or text prompt, the world model generates a visualization of future actions - a built-in inverse dynamics model then translates these into precise motor movements for NEO

The World Model as NEO's Cognitive Core 1X has revealed a major AI development where the NEO humanoid can translate any natural language prompt into robotic action. It demonstrates this capability even for novel tasks, objects, and environments not found in its robot dataset. - the 1X World Model is trained on internet-scale human interaction videos and fine-tuned with robot data to ground its understanding in physics and in NEO's embodiment - from a simple voice or text prompt, the world model generates a visualization of future actions - a built-in inverse dynamics model then translates these into precise motor movements for NEO

The Humanoid Hub

68,453 views • 6 months ago

🚨 New Models Alert: WebDev 💻 GPT-5-Codex and Qwen3-Coder-Plus are both now available on WebDev Arena! In the WebDev Arena, you can test out all the best frontier AI coding models on web development tasks. Vote for your preferred response and see how they stack up on the leaderboard. Prompt: "build a 90s inspired resume website for "Ella Marina", she is a front end web developer with a love for Lisa Frank and late 1990s R&B music. She works on front end development at LMArena"

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Arena.ai

21,096 views • 9 months ago

Weave is launching the number 1 prompt router in the world. It enables you to get 70% more efficient use of your tokens. We analyzed millions of prompts and found that the vast majority don't need a frontier model. Weave Router fixes that. It analyzes your prompt and routes it to the highest quality model with the lowest cost (across open and closed source models). This all happens in your current workflow on Claude, Cursor or Codex so you don't have to change a thing. Early customers have seen an ~70% reduction in costs without any slowdowns. Source code available.

Weave is launching the number 1 prompt router in the world. It enables you to get 70% more efficient use of your tokens. We analyzed millions of prompts and found that the vast majority don't need a frontier model. Weave Router fixes that. It analyzes your prompt and routes it to the highest quality model with the lowest cost (across open and closed source models). This all happens in your current workflow on Claude, Cursor or Codex so you don't have to change a thing. Early customers have seen an ~70% reduction in costs without any slowdowns. Source code available.

Adam Cohen

21,738 views • 1 month ago

Multi-Track Timeline Control for Text-Driven 3D Human Motion Generation paper page: Recent advances in generative modeling have led to promising progress on synthesizing 3D human motion from text, with methods that can generate character animations from short prompts and specified durations. However, using a single text prompt as input lacks the fine-grained control needed by animators, such as composing multiple actions and defining precise durations for parts of the motion. To address this, we introduce the new problem of timeline control for text-driven motion synthesis, which provides an intuitive, yet fine-grained, input interface for users. Instead of a single prompt, users can specify a multi-track timeline of multiple prompts organized in temporal intervals that may overlap. This enables specifying the exact timings of each action and composing multiple actions in sequence or at overlapping intervals. To generate composite animations from a multi-track timeline, we propose a new test-time denoising method. This method can be integrated with any pre-trained motion diffusion model to synthesize realistic motions that accurately reflect the timeline. At every step of denoising, our method processes each timeline interval (text prompt) individually, subsequently aggregating the predictions with consideration for the specific body parts engaged in each action. Experimental comparisons and ablations validate that our method produces realistic motions that respect the semantics and timing of given text prompts.

Multi-Track Timeline Control for Text-Driven 3D Human Motion Generation paper page: Recent advances in generative modeling have led to promising progress on synthesizing 3D human motion from text, with methods that can generate character animations from short prompts and specified durations. However, using a single text prompt as input lacks the fine-grained control needed by animators, such as composing multiple actions and defining precise durations for parts of the motion. To address this, we introduce the new problem of timeline control for text-driven motion synthesis, which provides an intuitive, yet fine-grained, input interface for users. Instead of a single prompt, users can specify a multi-track timeline of multiple prompts organized in temporal intervals that may overlap. This enables specifying the exact timings of each action and composing multiple actions in sequence or at overlapping intervals. To generate composite animations from a multi-track timeline, we propose a new test-time denoising method. This method can be integrated with any pre-trained motion diffusion model to synthesize realistic motions that accurately reflect the timeline. At every step of denoising, our method processes each timeline interval (text prompt) individually, subsequently aggregating the predictions with consideration for the specific body parts engaged in each action. Experimental comparisons and ablations validate that our method produces realistic motions that respect the semantics and timing of given text prompts.

AK

126,585 views • 2 years ago

i built a tool that lets you clone ANY video with AI saw a movie scene you want to recreate? a commercial? a music video? just upload it to this tool and get the exact structured prompt to generate it in sora 2 you no longer need to guess how to describe what you want to generate. show the tool the reference and it engineers the prompt for you. the tool analyzes: - camera movements and angles - lighting and color grading - scene composition and timing then converts everything into sora-optimized prompt structure this is how you recreate any visual style without prompt engineering experience RT + reply 'TOOL' and i'll send you access (must follow so i can dm)

i built a tool that lets you clone ANY video with AI saw a movie scene you want to recreate? a commercial? a music video? just upload it to this tool and get the exact structured prompt to generate it in sora 2 you no longer need to guess how to describe what you want to generate. show the tool the reference and it engineers the prompt for you. the tool analyzes: - camera movements and angles - lighting and color grading - scene composition and timing then converts everything into sora-optimized prompt structure this is how you recreate any visual style without prompt engineering experience RT + reply 'TOOL' and i'll send you access (must follow so i can dm)

Miko

42,842 views • 9 months ago

curl This is our Meta-Prompt. It allows LLMs to understand our Reader, Embeddings, Reranker, and Classifier APIs for improved codegen. Using the meta-prompt is straightforward. Just copy the prompt into your preferred LLM interface like ChatGPT, Claude, or whatever works for you, add your instructions, and you're set. In this example, we copied the entire prompt into Anthropic Claude and asked it to grab every sentence from Hacker News front page and visualize them using UMAP with matplotlib. This task is nontrivial as it combines multiple APIs from our Search Foundation, like Reader and Embedding where Claude may not have knowledge of. So if you asked Claude directly, it probably wouldn't give an optimal answer. But with the meta-prompt, Claude now has good knowledge about our APIs and can generate much better code! We can copy paste the code directly to Google Colab and with minimum modification, the code just works!

curl This is our Meta-Prompt. It allows LLMs to understand our Reader, Embeddings, Reranker, and Classifier APIs for improved codegen. Using the meta-prompt is straightforward. Just copy the prompt into your preferred LLM interface like ChatGPT, Claude, or whatever works for you, add your instructions, and you're set. In this example, we copied the entire prompt into Anthropic Claude and asked it to grab every sentence from Hacker News front page and visualize them using UMAP with matplotlib. This task is nontrivial as it combines multiple APIs from our Search Foundation, like Reader and Embedding where Claude may not have knowledge of. So if you asked Claude directly, it probably wouldn't give an optimal answer. But with the meta-prompt, Claude now has good knowledge about our APIs and can generate much better code! We can copy paste the code directly to Google Colab and with minimum modification, the code just works!

Jina AI

209,078 views • 1 year ago

i found the secret to writing copy that SELLS with AI... it's not about the model, it's not about the prompt, and it doesn't matter how good you are at writing it's about feeding AI with world-class examples i built a full copywriting swipe file you can use with any LLM: - over 1,000 examples of the best emails, ads and landing pages copy - my 3-step process to use this inside AI projects - my super-prompt to extract the sauce from any copy reply "SWIPE" + retweet and i'll send it for free (must be following so i can DM)

i found the secret to writing copy that SELLS with AI... it's not about the model, it's not about the prompt, and it doesn't matter how good you are at writing it's about feeding AI with world-class examples i built a full copywriting swipe file you can use with any LLM: - over 1,000 examples of the best emails, ads and landing pages copy - my 3-step process to use this inside AI projects - my super-prompt to extract the sauce from any copy reply "SWIPE" + retweet and i'll send it for free (must be following so i can DM)

Machina

32,568 views • 1 year ago

The "prompt-to-production" flow is already a reality. I hope that in the foreseeable future, we will be able to "prompt-to-real estate" as well, with robots building a complete building from the ground up. For now, it is still too futuristic, but Drafted has already automated the "prompt-to-home-design" step. > Drafted AI has a tool that lets you generate your dream house for FREE and export its detailed rendering. > Users can remix existing designs from the explore section or start from scratch. All the details can be customized. I need a new house now 👀

The "prompt-to-production" flow is already a reality. I hope that in the foreseeable future, we will be able to "prompt-to-real estate" as well, with robots building a complete building from the ground up. For now, it is still too futuristic, but Drafted has already automated the "prompt-to-home-design" step. > Drafted AI has a tool that lets you generate your dream house for FREE and export its detailed rendering. > Users can remix existing designs from the explore section or start from scratch. All the details can be customized. I need a new house now 👀

🚨 AI News | TestingCatalog

24,086 views • 2 months ago

New open-source 3D world-generation model. I'm rendering a couple of worlds in the video, so check it out. You'll find the GitHub and the Hugging Face links to the model below. This is a multi-modal world model that you can use for a bunch of things: • To generate new worlds • To reconstruct worlds • To simulate 3D interactive worlds from a prompt, images, or a video You can edit the 3D outputs in Unity and Unreal Engine (they export as meshes, 3DGS files, and point clouds). You can also generate 3D characters in the world and walk around. Pretty fun stuff!

New open-source 3D world-generation model. I'm rendering a couple of worlds in the video, so check it out. You'll find the GitHub and the Hugging Face links to the model below. This is a multi-modal world model that you can use for a bunch of things: • To generate new worlds • To reconstruct worlds • To simulate 3D interactive worlds from a prompt, images, or a video You can edit the 3D outputs in Unity and Unreal Engine (they export as meshes, 3DGS files, and point clouds). You can also generate 3D characters in the world and walk around. Pretty fun stuff!

Santiago

65,446 views • 2 months ago

.Flowglad is an open-source, zero-webhook payments provider. Their full-stack SDK lets you check customers' features and usage credits in real time, based on billing state. And their AI generates a setup prompt for your pricing model and stack, so you can one-shot payments.

.Flowglad is an open-source, zero-webhook payments provider. Their full-stack SDK lets you check customers' features and usage credits in real time, based on billing state. And their AI generates a setup prompt for your pricing model and stack, so you can one-shot payments.

Y Combinator

127,718 views • 7 months ago

7 ChatGPT prompts that made me $40K last month on YouTube. I didn’t film once. Here’s what I typed: Prompt 1: generate 10 viral video ideas in your niche, based on latest trends and what performs best. Prompt 2: write a full YouTube script with a hook, story arc, and call to action that maximizes watch time. Prompt 3: build me a faceless channel. Script it, voice it, edit it. Tell me every tool. Make it run without me. Prompt 4: generate a high-ranking title, description, and tags for my video. Clickworthy. SEO-optimized. Prompt 5: write a 15-second hook for my video that makes people physically unable to stop watching. Prompt 6: create a 30-day growth and monetization plan for a new channel from zero subscribers. Prompt 7: take my YouTube video and turn it into a blog post, a Reel, a TikTok, and a Twitter thread. 7 prompts. 1 laptop. 0 cameras. Most people are still filming themselves in bad lighting wondering why nobody watches. The channel doesn’t need your face. It needs your prompts.

7 ChatGPT prompts that made me $40K last month on YouTube. I didn’t film once. Here’s what I typed: Prompt 1: generate 10 viral video ideas in your niche, based on latest trends and what performs best. Prompt 2: write a full YouTube script with a hook, story arc, and call to action that maximizes watch time. Prompt 3: build me a faceless channel. Script it, voice it, edit it. Tell me every tool. Make it run without me. Prompt 4: generate a high-ranking title, description, and tags for my video. Clickworthy. SEO-optimized. Prompt 5: write a 15-second hook for my video that makes people physically unable to stop watching. Prompt 6: create a 30-day growth and monetization plan for a new channel from zero subscribers. Prompt 7: take my YouTube video and turn it into a blog post, a Reel, a TikTok, and a Twitter thread. 7 prompts. 1 laptop. 0 cameras. Most people are still filming themselves in bad lighting wondering why nobody watches. The channel doesn’t need your face. It needs your prompts.

RGK🌹

49,744 views • 1 month ago

Voice Design v3 is here. Create any voice you can imagine with a prompt. We’ve rebuilt the underlying Voice Design model to deliver higher quality and broader expressive range. Generate production-ready voices in 70+ languages with support for hundreds of localized accents.

Voice Design v3 is here. Create any voice you can imagine with a prompt. We’ve rebuilt the underlying Voice Design model to deliver higher quality and broader expressive range. Generate production-ready voices in 70+ languages with support for hundreds of localized accents.

ElevenLabs

153,890 views • 1 year ago

Text to Sound Effects is here. Our newest AI Audio model generates sound effects, short instrumental tracks, soundscapes, and a wide variety of character voices, all from a text prompt. Available now for all users. Everyone from content creators, video game developers, to film and television studios, uses sound effects to create rich and immersive content. Now, in addition to AI voiceovers, you can generate all of the sounds you need with just a prompt. Everything you hear in this video was generated by ElevenLabs sound and voice models. In this thread, we shared some additional clips that help show off the range of this new model.

Text to Sound Effects is here. Our newest AI Audio model generates sound effects, short instrumental tracks, soundscapes, and a wide variety of character voices, all from a text prompt. Available now for all users. Everyone from content creators, video game developers, to film and television studios, uses sound effects to create rich and immersive content. Now, in addition to AI voiceovers, you can generate all of the sounds you need with just a prompt. Everything you hear in this video was generated by ElevenLabs sound and voice models. In this thread, we shared some additional clips that help show off the range of this new model.

ElevenLabs

450,763 views • 2 years ago