Basically it boils down that for most queries google/gemini-2.5-flash is the workhorse fast/cheap/good enough.
Add in multimodality, 1M context and it is such a Swiss army knife.
It is cheap and performant enough to run 100k queries. (Took a bit over a day and cost around 30 Euros for a major document classification task). Yes in theory this could have been done with fine-tuned BERT or maybe even with some older methods but it saved way too much time.
There is another factor that may explain why Flash is #1 in most categories on OpenRouter - Flash has gotten reasonably decent at less common human languages.
Most cheap (including Flash Lite) and local models mostly have English focused training.
This was my initial assessment as well. Also note:
> Grok I forgot about until it was too late.
I was surprised by how much I prefer Grok to others. Even its persona is how I prefer it, detailed without volunteering unwanted information or sycophanty. In general I'd use Grok-3 more than 4 which is good enough for common uses.
I suspect that Claude would be best, only if I gave it a long complex task with enough instructions up front so it could grind away on it while I was doing something else and not waiting on it.
Oftentimes I ask simple factual questions that I don't know the answer to. This is something it should excel at, yet it usually fails, at least on the first try. I guess I subconsciously ignore questions that are extremely easy to google (if you ignore the worst AI in existence) or can be found by opening the [insert keyword] wikipedia article. You don't need AI for those.
Amusingly enough, my rule of thumb for if an LLM is likely to be able to answer a question is "could somebody who just read the relevant Wikipedia page answer this?"
The most recent one I have was not in English. It was a translation question of a slang word between two non-English languages. It failed miserably (just made up some complete nonsense). Google had no trouble finding relevant pages or images for that word (without any extra prompt), so it was rather unique and not that obscure. Disclaimer: I'm not using any extra prompts like "don't make shit up and just tell me you don't know".
Most recent technical I can remember (and now would be a good time to have the actual prompt) was that I asked whether MySQL has a way to run UPDATE without waiting for lock. Basically ignore rows that are locked. It (Sonnet 4 IIRC) answered of course and gave me an invalid query in the form of `UDPATE ... SKIP LOCKED`;
I can't imagine what damage this does if people are using it for questions they don't/can't verify. Programming is relatively safe in this regard.
But as I noted in my other reply, there will be a bias on my side, as I probably disregard questions that I know how to easily find answers to. That's not something I'd applaud AI for.
Key comment author made at the end: result evals were done blind: nice.
I have always juggled a multitude of API keys and this article has almost convinced me to try Open Router. It is easy enough to purchase compute credit from Chinese companies, but I could save myself some time.
The thing I liked most in the analysis was the emphasis on speed and cost.
The speed and cost issue is important. I recently read that many AI startups in the USA are favoring faster and cheaper models from China (China is quietly overtaking America with open AI models - iX Broker: https://ixbroker.com/blog/china-is-quietly-overtaking-americ...). The Economist has a similar article but it is paywalled.
I use a 64GB M2 MacBook Pro. I tend to find any model that's smaller than 32B works well (I can just about run a 70B but it's not worth it as I have to quit all other apps first).
My current favorite to run on my machine is OpenAI's gpt-oss-20b because it only uses 11GB of RAM and it's designed to run at that quantization size.
I also really like playing with the Qwen 3 family at various sizes and I'm fond of Mistral Small 3.2 as a vision LLM that works well.
Only when I'm offline (on planes for example) - I've had both Mistral Small and gpt-oss-20b be useful for Python and JavaScript stuff.
If I have an internet connection I'll use GPT-5 or Claude 4 or Gemini 2.5 instead - they're better and they don't need me to dedicate a quarter of my RAM or run down my battery.
Useful info! I have an M1 Mac with 64 GB and haven't experimented with offline models recently. I'll come back to this when I need my AI Maccie snackie, haha. Offline Apple Intelligence isn't at the level I want it, yet.
Him using different ones is why I use Perplexity, I get to try different models and honestly its pretty darn decent, gives me everything in an organized way, I can see all the different links, and all the files it outputs can be downloaded as a simple zip file. It has everything from GTP5 to Deepseek R1 and even Grok.
There's other sites similar to perplexity that host multiple models as well, I have not tried the plethora of others, I feel like Perplexity does the most to make sure whatever model you pick it works right for you and all its output is usefully catalogued.
I also use Perplexity APIs, specifically their combined web search tool + decent models. Useful combination and easier that what I used to do: using a search API like Brave and rolling my own code to combine LLM and search.
That said I have been having too much fun running Melisearch to build a local search index for many web sites that I use for reference and combine that with a small Python app that also uses local models running on Ollama. I will probably wrap this into an example to add to one of my books: not that practical but fun.
I have noticed this too - often when one model volunteered the wrong answer - such as making up a nonexistent API, I asked another, and it gave me the exact same thing! It's highly unlikely that two totally independent models would make up the same fictional thing.
There must be something strange going on (most likely training on each others' wrong outputs, but I dunno)
This is definitely something very early LLMs could do that has kind of gotten beat out of them. I used to ask ChatGPT to simulate a text adventure game, but now if you try that you always get exactly the same one.
i use gemini flash and pro for pretty much everything. Why? they offer it free to test.
I tried signup for openai wayy too much friction, they start asking for payment without even you using any free credits, guess what that's one sure way to lose business.
same for claude, i couldn't even get claude through vertex as its available only in limited regions, and i am in asia pasific right now.
This is an interesting overview, thank you. Different tasks, different models, all-day-usage and pretty complete (while still opinionated, which I like).
However, checking the results my personal overall winner if I had to pick only ONE probably would be
deepseek/deepseek-chat-v3-0324
which is a good compromise between fast, cheap and good :-) Only for specific tasks (write a poem...) I would prefer a thinking model.
Basically it boils down that for most queries google/gemini-2.5-flash is the workhorse fast/cheap/good enough.
Add in multimodality, 1M context and it is such a Swiss army knife.
It is cheap and performant enough to run 100k queries. (Took a bit over a day and cost around 30 Euros for a major document classification task). Yes in theory this could have been done with fine-tuned BERT or maybe even with some older methods but it saved way too much time.
There is another factor that may explain why Flash is #1 in most categories on OpenRouter - Flash has gotten reasonably decent at less common human languages.
Most cheap (including Flash Lite) and local models mostly have English focused training.
How do you run so many, I’m constantly exhausting the resources can’t even concurrently call 20 times?
This was my initial assessment as well. Also note:
> Grok I forgot about until it was too late.
I was surprised by how much I prefer Grok to others. Even its persona is how I prefer it, detailed without volunteering unwanted information or sycophanty. In general I'd use Grok-3 more than 4 which is good enough for common uses.
I suspect that Claude would be best, only if I gave it a long complex task with enough instructions up front so it could grind away on it while I was doing something else and not waiting on it.
> Almost all models got almost all my evaluations correct
I find this the most surprising. I have yet to cross 50% threshold of bullshit to possibly truth. In any kind of topic I use LLMs for.
It's useful to build up an intuition for what kind of questions LLMs can answer and what kind of questions they can't.
Once you've done that your success rate goes way up.
Oftentimes I ask simple factual questions that I don't know the answer to. This is something it should excel at, yet it usually fails, at least on the first try. I guess I subconsciously ignore questions that are extremely easy to google (if you ignore the worst AI in existence) or can be found by opening the [insert keyword] wikipedia article. You don't need AI for those.
Amusingly enough, my rule of thumb for if an LLM is likely to be able to answer a question is "could somebody who just read the relevant Wikipedia page answer this?"
Although that changed this year with o3 (and now GPT-5) getting really good at using Bing for search: https://simonwillison.net/2025/Apr/21/ai-assisted-search/
Would you be willing to share some of those chats?
The most recent one I have was not in English. It was a translation question of a slang word between two non-English languages. It failed miserably (just made up some complete nonsense). Google had no trouble finding relevant pages or images for that word (without any extra prompt), so it was rather unique and not that obscure. Disclaimer: I'm not using any extra prompts like "don't make shit up and just tell me you don't know".
Most recent technical I can remember (and now would be a good time to have the actual prompt) was that I asked whether MySQL has a way to run UPDATE without waiting for lock. Basically ignore rows that are locked. It (Sonnet 4 IIRC) answered of course and gave me an invalid query in the form of `UDPATE ... SKIP LOCKED`;
I can't imagine what damage this does if people are using it for questions they don't/can't verify. Programming is relatively safe in this regard.
But as I noted in my other reply, there will be a bias on my side, as I probably disregard questions that I know how to easily find answers to. That's not something I'd applaud AI for.
Key comment author made at the end: result evals were done blind: nice.
I have always juggled a multitude of API keys and this article has almost convinced me to try Open Router. It is easy enough to purchase compute credit from Chinese companies, but I could save myself some time.
The thing I liked most in the analysis was the emphasis on speed and cost.
The speed and cost issue is important. I recently read that many AI startups in the USA are favoring faster and cheaper models from China (China is quietly overtaking America with open AI models - iX Broker: https://ixbroker.com/blog/china-is-quietly-overtaking-americ...). The Economist has a similar article but it is paywalled.
Which of these can I run locally on a 64GB Mac Mini Pro? And how much does quantization affect the quality?
I use a 64GB M2 MacBook Pro. I tend to find any model that's smaller than 32B works well (I can just about run a 70B but it's not worth it as I have to quit all other apps first).
My current favorite to run on my machine is OpenAI's gpt-oss-20b because it only uses 11GB of RAM and it's designed to run at that quantization size.
I also really like playing with the Qwen 3 family at various sizes and I'm fond of Mistral Small 3.2 as a vision LLM that works well.
Thanks. Do you get any value from those for coding?
Only when I'm offline (on planes for example) - I've had both Mistral Small and gpt-oss-20b be useful for Python and JavaScript stuff.
If I have an internet connection I'll use GPT-5 or Claude 4 or Gemini 2.5 instead - they're better and they don't need me to dedicate a quarter of my RAM or run down my battery.
Useful info! I have an M1 Mac with 64 GB and haven't experimented with offline models recently. I'll come back to this when I need my AI Maccie snackie, haha. Offline Apple Intelligence isn't at the level I want it, yet.
I'm pretty new to AI and have access to a few models in Kagi. I just never know which to pick, kind of annoys me I might not be using the best
I use Kagi as well and, from my experiments, deepseek gives me answers I like the most.
Him using different ones is why I use Perplexity, I get to try different models and honestly its pretty darn decent, gives me everything in an organized way, I can see all the different links, and all the files it outputs can be downloaded as a simple zip file. It has everything from GTP5 to Deepseek R1 and even Grok.
There's other sites similar to perplexity that host multiple models as well, I have not tried the plethora of others, I feel like Perplexity does the most to make sure whatever model you pick it works right for you and all its output is usefully catalogued.
I also use Perplexity APIs, specifically their combined web search tool + decent models. Useful combination and easier that what I used to do: using a search API like Brave and rolling my own code to combine LLM and search.
That said I have been having too much fun running Melisearch to build a local search index for many web sites that I use for reference and combine that with a small Python app that also uses local models running on Ollama. I will probably wrap this into an example to add to one of my books: not that practical but fun.
> To access their best models via the API, OpenAI now requires you to complete a Know-You-Customer process similar to opening a bank account.
While this is true, you can download the OpenAI open source model and run it in Ollama.
The thinking is a little slow, but the results have been exceptional vs other local models.
https://ollama.com/library/gpt-oss
openai/gpt-oss-120b is in this blog post.
> Six of the eleven picked the same movie
This is surely the greatest weakness of current LLMs for any task needing a spark of creativity.
I have noticed this too - often when one model volunteered the wrong answer - such as making up a nonexistent API, I asked another, and it gave me the exact same thing! It's highly unlikely that two totally independent models would make up the same fictional thing.
There must be something strange going on (most likely training on each others' wrong outputs, but I dunno)
This is definitely something very early LLMs could do that has kind of gotten beat out of them. I used to ask ChatGPT to simulate a text adventure game, but now if you try that you always get exactly the same one.
Curious, what kind of prompt gives you the same text adventure game?
Surely it is a question of prompting some context(in UI mode) or with additional kicker of temperature (if using API)?
At the very least some set up prompt such as "Give me 5 scenarios for text adventure game" would break the sameness?
There have always been theories that OpenAI and other LLM providers cache some responses - this could be one hypothesis.
I'm now imagining 5 hipster AIs writing those stories--different in predictable ways.
i use gemini flash and pro for pretty much everything. Why? they offer it free to test.
I tried signup for openai wayy too much friction, they start asking for payment without even you using any free credits, guess what that's one sure way to lose business.
same for claude, i couldn't even get claude through vertex as its available only in limited regions, and i am in asia pasific right now.
This is an interesting overview, thank you. Different tasks, different models, all-day-usage and pretty complete (while still opinionated, which I like).
However, checking the results my personal overall winner if I had to pick only ONE probably would be
which is a good compromise between fast, cheap and good :-) Only for specific tasks (write a poem...) I would prefer a thinking model.