Nice resource. Almost too comprehensive for someone who doesn't know all the sub-version names. Would be great to have a column of the score from lmarena leaderboard. Some prices are 0.00? Is there a page that each row could link to for more detail?
Would poss be further useful to have a release date column, license type, whether EU restricted and also right-align / comma-delimit those numeric cells
I like the idea of more comparisons of models. Are there plans to add independent analyses of these models or is it only an aggregation of input limits?
How do you see this differing from or adding to other analyses such as:
I want to point out you dodged the data question, and there's a reason for it.
I like your work visually on first glance, god knows you're right about gradio, even if its irrelevant.
But peddling extremely limited, out of date, versions of other people's data, trumps that, especially with this tagline. "A website to compare every AI model: LLMs, TTSs, STTs"
It is a handful of LLMs, then one TTS model, then one STT model, both with 0 data. And it's worth pointing out, since this endeavor is motivated by design trumping all: all the columns are for LLM data.
I made https://aimodelreview.com/ to compare the outputs of LLMs over a variety of prompts and categories, allowing a side by side comparison between them. I ran each prompt 4 times for different temperature values and that's available as a toggle.
I was going to add reviews on each model but ran out of steam. Some users have messaged me saying the comparisons are still helpful to them in getting a sense of how different models respond to the same prompt and how temperature affects the same models output on the same prompt.
Hey, this is pretty insightful! Wonder if, in the course of researching to build this website you reached any conclusions as to what’s the AI assistant currently ahead.
now imagine going one step further and actually running a prompt across every AI model and showing you the best answer and the AI model that generated it
Those tools exist, they do not need to be imagined. Look into the related comments. Also they do little, but increase the labor of getting an answer. Not exactly an improvement of AI for the user to spend more time reviewing AI answers.
There are only two audio transcription models. Is this generally true, are there no open source ones like llama but for transcribing? Or just small dataset on that site
It looks like the site is only listing hosted models from major providers, not all models available on huggingface, civit.ai, etc. -- Looking at the image generation and chat lists there are many more models that are on huggingface that are not listed.
Note: Text to Speech and Audio Transcription/Automatic Speech Recognition models can be trained on the same data. They currently require training separately as the models are structured differently. One of the challenges is training time as the data can run into the hundreds of hours of audio.
There are lots and lots of models, covering various use cases (e.g., on device, streaming/low-latency, specific languages). People somehow think OpenAI invented audio transcription with whisper in 2022 when other models exist and have been used in production for decades (whisper is the only one listed on that website).
I'd like to share a personal perspective/rant on AI that might resonate with others: like many, I'm incredibly excited about this AI moment. The urge to dive headfirst into the field and contribute is natural after all, it's the frontier of innovation right now.
But I think this moment mirrors financial markets during times of frenzy. When markets are volatile, one common piece of advice is to “wait and see”. Similarly, in AI, so many brilliant minds and organizations are racing to create groundbreaking innovations. Often, what you're envisioning as your next big project might already be happening, or will soon be, somewhere else in the world.
Adopting a “wait and see” strategy could be surprisingly effective. Instead of rushing in, let the dust settle, observe trends, and focus on leveraging what emerges. In a way, the entire AI ecosystem is working for you: building the foundations for your next big idea.
That said, this doesn't mean you can't integrate the state of the art into your own (working) products and services.
Your proposal makes a lot of sense. I assume a number of companies are integrating sota models into their products.
That being said, there is no free lunch: when you're doing this, you're more reactive than proactive. You minimize risk, but you also lose any change to have a stake [1] in the few survivors that will remain and be extremely valuable.
Do this long enough and you'll have no idea what people are talking about in the field. Watch the latest Dwarkesh Patel episode to get a sense of what I am talking about.
[1] stake to be understood broadly as: shares in a company, knowledge as an AI researcher, etc.
Thank you for your thoughtful response! I completely agree that there's a tradeoff between being proactive and reactive in this kind of strategy: minimizing risk by waiting can mean missing out on opportunities to gain a broader "stake".
That said, my perspective focuses more on strategic timing rather than complete passivity. It's about being engaged with understanding trends, staying informed, and preparing to act decisively when the right opportunity emerges. It's less about "waiting on the sidelines" and more about deliberate pacing, recognizing that it’s not always necessary to be at the bleeding edge to create value.
I'll definitely check out Dwarkesh Patel’s latest episode. I assume it is the Gwern one, right? Thanks!
Azure charges differently based on deployment zone/latency guarantees, OpenAI doesn't let you pick your zone so it's equivalent to the Global Standard deployment (which is the same cost).
Great! I wish there was a "bang to buck" value. Some way to know the cheapest model I could use for creating structured data from unstructured text, reliably. Using gpt4o-mini which is cheap but wouldn't know if anything cheaper could do the job too.
Take a look at Gemini Flash 1.5. I had videos I needed to turn into structured notes, and the result was satisfactory (even better than the Gemini 1.5 Pro, for some reason). https://jampauchoa.substack.com/i/151329856/ai-studio.
According to this website, the cost is half of the gpt4-o mini. 0.15 vs 0.07 per 1M token.
I love the idea of openrouter. I hadn't realized until recently though that you don't necessarily know what quantization a certain provider is running. And of course context size can vary widely from provider to provider for the same model. This blog post had great food for thought https://aider.chat/2024/11/21/quantization.html
I haven't found a model at the price point of GPT-4o mini that is as capable. Based on the hype surrounding Llama 3.3 70B, it might be that one though. On Deepinfra, input tokens are more expensive, but the output token is cheaper so I would say they are probably equivalent in price.
Also, best bang for the buck is very subjective, since one person might need it to work for one use case vs somebody else, who needs it for more.
Tangent question: is there anything better on the desktop than ChatGPT's native client? I find it too simple to organize chats but I'm having a hard time evaluating the dozen or so apps (most are disguise for some company's API service). Any recommendations? macOS/Linux compatibility preferred.
Peesonally im a Typing Mind user but it got too slow and buggy with long cbaglts. Ended up with boltai which is a natice mac app and found it very good after months of heavy use. I think it could also improve navigation coloring or iconography to help distinguish chats better but its my favorite so far.
I'm working on a native LLM client that is beautiful and fast[1], developed in Qt C++ and QML - so it can run on Windows, macOS, Linux (and mobile). Would love to get your feedback once it launches.
Telosnex: every platform, native. Also, has web. Anthropic, OpenAI, Mistral, Groq, Gemini, and any local LLM on literally every platform. and you can bring your own API keys, and the best search available. Pay as you go, with everything at cost if you pay $10/month. Otherwise, free. Everythings stored in simple JSON.
A small suggestion, a toggle to exclude between "free" and hosted models.
Reason is, I'm obv. interested in seeing the cheaper models first but am not interested in self-hosting which dominate the first chunk of results because they're "free".
This page has up to date information of all models and providers: https://artificialanalysis.ai/leaderboards/providers
We also on other pages cover Speech to Text, Text to Speech, Text to Image, Text to Video.
Note I'm one of the creators of Artificial Analysis.
One helpful addition would be Requests Per Minute (RPM), which varies wildly and is critical for streaming use cases -- especially with Bedrock where the quota is account wide.
These are hard to keep updated. I find they usually fall off. It would be cool to have one, but honestly, this one already doesn't even have 4o and pro on it which if it was being maintained, it obviously would. Updating a table shouldn't take days. It's like a one minute event.
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[ 3.3 ms ] story [ 158 ms ] threadAs per llmarena I'll definitely add it, a lot of other people recommended it as well.
over time will make the website more descriptive and detailed!
How do you see this differing from or adding to other analyses such as:
https://artificialanalysis.ai
https://huggingface.co/spaces/TTS-AGI/TTS-Arena
https://huggingface.co/spaces/hf-audio/open_asr_leaderboard
https://huggingface.co/spaces/TIGER-Lab/GenAI-Arena
Great work on all the aggregation. The website is nice to navigate.
I'll try to make it as user-friendly as possible. Most of the websites are ugly + too technical.
I like your work visually on first glance, god knows you're right about gradio, even if its irrelevant.
But peddling extremely limited, out of date, versions of other people's data, trumps that, especially with this tagline. "A website to compare every AI model: LLMs, TTSs, STTs"
It is a handful of LLMs, then one TTS model, then one STT model, both with 0 data. And it's worth pointing out, since this endeavor is motivated by design trumping all: all the columns are for LLM data.
I was going to add reviews on each model but ran out of steam. Some users have messaged me saying the comparisons are still helpful to them in getting a sense of how different models respond to the same prompt and how temperature affects the same models output on the same prompt.
See https://huggingface.co/models?pipeline_tag=automatic-speech-...
Note: Text to Speech and Audio Transcription/Automatic Speech Recognition models can be trained on the same data. They currently require training separately as the models are structured differently. One of the challenges is training time as the data can run into the hundreds of hours of audio.
But I think this moment mirrors financial markets during times of frenzy. When markets are volatile, one common piece of advice is to “wait and see”. Similarly, in AI, so many brilliant minds and organizations are racing to create groundbreaking innovations. Often, what you're envisioning as your next big project might already be happening, or will soon be, somewhere else in the world.
Adopting a “wait and see” strategy could be surprisingly effective. Instead of rushing in, let the dust settle, observe trends, and focus on leveraging what emerges. In a way, the entire AI ecosystem is working for you: building the foundations for your next big idea.
That said, this doesn't mean you can't integrate the state of the art into your own (working) products and services.
That being said, there is no free lunch: when you're doing this, you're more reactive than proactive. You minimize risk, but you also lose any change to have a stake [1] in the few survivors that will remain and be extremely valuable.
Do this long enough and you'll have no idea what people are talking about in the field. Watch the latest Dwarkesh Patel episode to get a sense of what I am talking about.
[1] stake to be understood broadly as: shares in a company, knowledge as an AI researcher, etc.
That said, my perspective focuses more on strategic timing rather than complete passivity. It's about being engaged with understanding trends, staying informed, and preparing to act decisively when the right opportunity emerges. It's less about "waiting on the sidelines" and more about deliberate pacing, recognizing that it’s not always necessary to be at the bleeding edge to create value.
I'll definitely check out Dwarkesh Patel’s latest episode. I assume it is the Gwern one, right? Thanks!
11labs, deepgram, etc.
BTW impressive idea and upvoted on PH as well.
OpenAI and Azure should be the same, it's weird that it shows it as different. I'll look into fixing this.
currently #2 on PH, any help would be appreciated!
[0] https://azure.microsoft.com/en-us/pricing/details/cognitive-...
According to this website, the cost is half of the gpt4-o mini. 0.15 vs 0.07 per 1M token.
Also, best bang for the buck is very subjective, since one person might need it to work for one use case vs somebody else, who needs it for more.
[1] https://rubymamistvalove.com/client.mp4
A small suggestion, a toggle to exclude between "free" and hosted models.
Reason is, I'm obv. interested in seeing the cheaper models first but am not interested in self-hosting which dominate the first chunk of results because they're "free".
https://whatllm.vercel.app
The tables are very similar - though you've added a custom calculator which is a nice touch.
Also for the Versus Comparison, it might be nice to have a checkbox that when clicked highlights the superlative fields of each LLM at a glance.
This page has up to date information of all models and providers: https://artificialanalysis.ai/leaderboards/providers We also on other pages cover Speech to Text, Text to Speech, Text to Image, Text to Video.
Note I'm one of the creators of Artificial Analysis.
1. Maybe explain what Chat Embedding Image generation Completion Audio transcription TTS (Text To Speech) means?
2. Put a running number on the left, or at least just show total?
you're missing a lot
TTS: 11labs, PlayHT, Cartesia, iFLYTEK, AWS Polly, Deepgram Aura
STT: Deepgram (multiple models, including Whisper), Gladia Whisper, Soniox
just off the top of my head (it's my dayjob!)
the website is updated, don't worry :)