Welcome to our weekly discussion hub where we talk about different models and API services.
Any general model or API discussions that aren’t posted here will be removed. Please stop making separate “Which model is best?” posts.
(Note: This thread isn’t for advertising your own services constantly. We might allow announcements for legitimate new services occasionally, but don’t expect promotional content to stay up.)
Thread Organization Guide
Look for these category sections below:
LARGE MODELS: 70B+ – Discussion about models with 70 billion parameters and above
MEDIUM-LARGE: 32B-70B – Talk about models between 32B and 70B parameters
MEDIUM: 16B-32B – Models ranging from 16B to 32B parameters
SMALL-MEDIUM: 8B-16B – Discussion of 8B to 16B parameter models
SMALL MODELS: Under 8B – Smaller models with less than 8B parameters
API SERVICES – Everything about API platforms (costs, speed, availability, etc.)
OTHER TOPICS – Model and API related stuff that doesn’t match other categories
Find the right category and reply there with your questions or thoughts!
This system helps keep everything organized so people can find what they need quickly.
I’ve been testing different API providers and wanted to share what I’ve learned. Everyone talks about pricing and model quality, but reliability matters most.
I’ve had projects crash because providers went down during busy hours or had wonky response times. Now I test across time zones and different days before using anything in production.
Some providers throttle way harder than their docs claim. Don’t trust the published rate limits - test your actual workload.
If you’re shopping around, set up backup providers. Yeah, manual switching sucks, but it’ll save your ass when your main provider craps out.
SMALL MODELS: Under 8B
Don’t sleep on tiny models. I’ve been running 3-7B models for simple tasks and they’re surprisingly solid. Way faster and basically free locally.
Perfect for content filtering, quick summaries, or repetitive work where you don’t need heavy thinking. Save the big models for when you actually need them.
I’ve been running several models in this range for different tasks and the sweet spot really depends on your hardware. 32B models often punch way above their weight if you can run them locally with decent quantization.
What surprised me was how much fine-tuning matters here. Some 40B models completely destroy certain 65B ones on specific tasks just because of better training data or methods.
If you’re considering this category, check your VRAM requirements carefully. The jump from 32B to 50B+ can mean the difference between running locally versus cloud hosting, which changes your costs dramatically.
These models handle complex reasoning better than smaller ones but aren’t overkill like 70B+ for most business stuff. Perfect middle ground if you’re not doing research-level work.
Been managing API integrations across multiple models for years, and the biggest pain isn’t picking the right model or service. It’s the switching costs and integration headaches when you want to test different options.
Most teams get locked into one provider because rebuilding integrations sucks. You pick OpenAI, then Claude releases something better, but switching means weeks of dev work.
That’s why I automate the whole API layer now. Built workflows that route requests to any model without touching code. Want to A/B test GPT-4 against Claude? Done in minutes. New model drops? Add it instantly.
If you’re juggling multiple API services, stop hardcoding integrations. Use automation to create a unified layer that handles all the switching logic. I’ve saved months of engineering time this way.
You can build these workflows easily with tools like Latenode. Check it out: https://latenode.com
Great to see another organized discussion thread! These category breakdowns are super helpful.
From working with different models and APIs, I’ve found parameter count matters way less than how you integrate everything. You can have the best 70B+ model, but if you’re manually switching between services or copying outputs around, you’re killing your efficiency.
I automate my entire workflow with Latenode. It handles API calls, processes responses, and chains multiple models together without me touching anything. Set it up once and forget it.
For anyone comparing APIs in the services section - don’t just look at cost per token. Look at how easy it is to automate your pipeline. That’s where real productivity gains happen.
Most people miss the workflow automation piece when they’re comparing raw model performance.
Thanks for setting this up again. Organizing by parameter count makes it way easier to navigate all these discussions. I’ve used this format for a few weeks now and finding conversations about models I care about is so much simpler. One suggestion - can we add a sticky comment at the top of each category once discussions get going? The categories get buried under long comment chains and newcomers can’t figure out where to post. But yeah, this weekly format definitely cuts down on scattered posts across the main forum.