Which AI models are you using? And why?
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The large AI labs (Anthropic, OpenAI, Grok, Gemini, etc) are constantly releasing new versions and improvements.
People on social media often advocate for one specific model claiming that it performs tasks better than others.
For Hello Inbox we're using Anthropic because we tested responses with other models and found it to give the best responses for our use case.
But I'm curious to hear from others building or using AI why they are using a specific model? and why?
Which model did you pick?
Why did you pick that model?
Why do you think it's better than others?
What tasks are you performing?
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Did you notice a big difference in response quality between Anthropic and Gemini?
Hello Inbox
@oliver_graf1 Yes, especially for reasoning. We analyze deliverability test reports and generate recommendations for our clients, and Anthropic's Claude Opus 4.5 and Sonnet 4.6 crafted the best responses for our use case. However, I tested different models back in April so it's possible things have evolved since then.
@ismaelyws That’s interesting. Reasoning quality can make a big difference for this kind of analysis. I’d be curious to see how the newer models compare if you test them again now.
Hello Inbox
@oliver_graf1 Yes, I'm curious as well. Problem is, time. It's moving so fast it's hard to keep up with all the newest releases. So I'm trying to figure out how often and how much time I should be spending testing the newest models. I also want to test the top open source models against Claude Opus to see how they compare.
@ismaelyws Does it know when a simple 2 line reply is enough?
Hello Inbox
@moses_habila Yes, but prompt design is key. Including strict constraints within the prompt returned the results I wanted.
I've been using Opus 4.7 throughout. Sometimes deepseek r1 and sonnet 3.7 for smaller tasks or research but to do a task they almost always hallucinate and the only one I can basically depend on is Opus. I have also been using gemini image banana pro for images; tried many different ones for graphics and none outputs the quality I need.
Hello Inbox
@mikesabet Yes Opus seems to perform the best. I really want to test the latest open source models to see how they compare against Opus. BTW, DeepSeek released V4 Pro recently. Have you experimented with any other open source models besides DeepSeek R1?
Hello Inbox
@mikesabet For image models you may find this useful https://www.gradually.ai/en/ai-image-models/
@ismaelyws OpenRouter does not yet have it as a model so not yet but the v3 pro is not bad neither. I find r1 more reliable for long tasks over various days.
Thanks for the image link. I will check it out
Hello Inbox
@mikesabet You're welcome. LMK if you end up finding something better for images.
We're on GPT for the generation path in a flashcard tool I build, and the model was honestly the least interesting decision in it.
What moved quality was forcing structured output. Every call goes through one service with a JSON schema attached, so the model returns an array of objects and never formats anything itself. Before that we asked for a delimited text blob in the prompt and burned weeks on quoting and escaping bugs that were really the model improvising. Once serialization moved out of the prompt and into code, a lot of what had looked like model quality turned out to be prompt design.
The one place the model does still show is counting. Ask for 40 items, get 34. We over-request and truncate in code rather than trusting the number, and that behavior was about the same everywhere we tested.
Hello Inbox
@siarheihamanovich thanks for sharing. Yes, good prompt design is key. So if the behavior was about the same everywhere you tested what made you ultimately settle with ChatGPT? Cost?
@ismaelyws Cost is part of it, but it wasn't the only part.
It was the schema contract. We send the JSON schema with strict mode on, so the API itself rejects a response that doesn't match the shape, instead of us hoping the model complied and writing a parse-then-retry path for when it didn't. That deleted a whole class of retry from our code.
Strict mode rejects `minItems`, so you literally cannot ask the schema for "at least 40 items." That's why our count check lives in code, and it's the kind of thing you only learn by shipping.
Cost shows up a level down. Card generation is narrow and repetitive, so it runs on a small model and only the harder paths get the expensive one.