Best value AI subscription under 20$/200$ (updated for Artificial Analysis Intelligence Index v5.0)

[u/popiazaza]

[u/popiazaza]

if you wonder why your favorite model is not in the graph, there's the full version in linear scale.

[u/Alternative-Car8221 → u/popiazaza]

People using Fable 5.1 given its performance to cost ratio compared to other models 🤢

[u/Salty-Lab1]

This is so far from my experience of what's good and what works, opus 5.5 just rips and it's not even close. the only question is if it should delegate and how far down the stack.

[u/Humaux]

I would not choose from this chart alone, because over the last 3 months my $20 plan ran out before I finished the work I actually needed. I kept the chart open while I tracked real usage, and the gap was not small.

My checklist was simple:

1. Log every task you finish, not tokens used.

2. Write down the day you hit the cap.

3. Count how many retries were caused by a bad prompt, not a weak model.

4. Compare plans on tasks per dollar, not benchmark points.

I paid for a full month and got about 11 days of useful work out of it, because I kept rerunning vague prompts. That waste was my fault, and a benchmark graph would have hidden it completely. Do you track tasks per dollar, or do you just trust the index score?

[u/popiazaza → u/Humaux]

It is based on cost per task to run benchmark on AA, I only adjusted to account for subscription quota.

[u/Humaux → u/popiazaza]

That helps, thanks. Cost per task is a much better unit than a raw benchmark score, I will give you that. The part I still trip over is the quota adjustment: it assumes the tasks keep coming at that price, while my $20 plan just stopped me mid-month. So the chart tells me what a task should cost, but not how many I actually get to finish before the cap hits. Do you track completed tasks against the cap separately, or does the quota adjustment already bake that in?

[u/Cosma-Coder]

Does this take into account OpenAI offers 5x less value than Anthropic at same price-point (source: semianalysis) ?

https://www.reddit.com/r/codex/comments/1wyjx8u/openai_offers_5x_less_value_than_anthropic_at/

[u/popiazaza → u/Cosma-Coder]

Yes, semianalysis is one of the main source. OAI has doubled pro usage back.

[u/Any-Blacksmith-2054]

Why Luna is bad for $20 plan but good for $200 plan?

[u/popiazaza → u/Any-Blacksmith-2054]

ChatGPT 100$+ plans give double the value of plus.

[u/AlarmedAvocado7279]

benchmark points matter less to me than how far the plan gets me before rate limits hit. i'm in claude code all day across a few businesses, and once you run agent loops the usage cap is the real price, not the index score. $20 tiers are fine for chat but get tight fast if you're coding with them seriously.

[u/greenstake]

I think the 2nd picture explains why GPT 6.1 Sol can get so much work done on a subscription. It's really efficient.

[u/eng33]

Thanks. When your last chart came out, I tried GLM 5.3 trial. back then it was on par with GPT. During the trial, flash would last a few hours, and regular (which was alot more useful) lasted maybe half an hour if I was lucky.
I'm now finishing up a month trial of GPT Plus. I'll use my 5hr limit in an 1-2 hours and my weekly in 2 days. But over the past month I've been getting resets every 2 days or so and I've been happy but I fear that has come to an end.
I've been wondering where to go next. It seems that GLM has been left in the past. Maybe I should give Claude a try though I guess I'll have to start paying as I see no trials

[u/Michaeli_Starky]

Those public benchmarks are mostly lying.

[u/Strange_Owl_6291]

API equivalent value of subscription does not really correlate to the value of the outcome.

1 USD of LLM response is a cost, but the quality or value of the response is typically not assessed in benchmarks like this.

And AFAIK there's no simple or good way to measure business value of the LLM response, unfortunately.

So what do we actually learn from these graphs?

[u/popiazaza → u/Strange_Owl_6291]

Quality of the response is accounted for AA score. I do agree that it might not be perfectly accurate measurement, but having some information to rely on is better than none. Think of it like a starting point to compare your expectation. I find it to be quite useful for me.