Sol, Terra or Astra: which AI is cheapest for coding

Sol, Luna or Astra: the monthly bill for coding with AI

The question is no longer whether artificial intelligence can write code, but how much it costs to keep it running all day. Comparing Sol, Terra, Luna and Astra — the names given to the latest versions of the big models — has become as routine as comparing mortgages. And the range is scary: some people pay 100 euros a month for a single plan, while others juggle three 20-dollar subscriptions so they don't run out of quota mid-afternoon.

What does each AI coding plan cost?

The range runs from 20 to 100 euros a month. A ChatGPT plan comes in at around 22 euros; Claude's standard subscription sits at 20; and above that is the 100-euro plan, the one that stings most when the bills come due. The underlying question is whether it's worth keeping when the same work can be done with two cheaper subscriptions.

The multi-account strategy has caught on: three 20-dollar accounts, rotated depending on how demanding each task is, cost the same as one and multiply your quota. Dirt cheap, as those who do it put it. With one caveat hanging over the whole conversation: when this ends, prices will multiply five- or tenfold.

How much does a 20-dollar subscription get you?

It depends on how hard you push the model. At maximum effort, about 45 minutes a day; at high effort, up to 75. That's not clock time but compute time: a complex task can eat through the quota in a couple of queries. That's why the comparison between Terra, Sol and Astra is always framed in terms of cost per task, not list price.

That's where the misunderstanding trips people up. A quick calculation turns those 45 minutes into 213 dollars per eight-hour day, 1,066 a week and more than 4,200 a month — figures that, jokes aside, would cover two programmers. The mistake is confusing the model's internal consumption with what you actually pay, which is a fixed 20-dollar monthly fee. The confusion, though, shows just how opaque the real cost of compute remains to the people using it.

High effort, low temperature: how to cut consumption costs

When spending spirals, the settings matter. Lowering the temperature and working with short contexts cuts consumption noticeably; with long contexts the model guzzles like crazy, and one intensive session can burn through 100 euros. Tuned properly, the daily bill drops to 3 or 4.

That's what people with real hours under their belt report, not the marketing brochures. The difference between running the model at full power for everything and saving it for problems that deserve it is the difference between a sustainable bill and a money pit.

Astra, Kimi and Minimax: the model marketplace

It's not all about Sol. Astra emerges as the favorite for very specific jobs: it generates working WordPress plugins and multi-page conversion funnels in a single attempt, with no notable failures. Kimi also gets mentions for extracting diagrams from code, along with an inference cost comparator that lets you fine-tune how much you pay per query.

The enthusiasm runs into the fine print. Astra burns through its tokens in five minutes and its weekly limit in two days, forcing you to ration it. Meanwhile, interest in Opus 5.5 is growing after a stretch when the top-tier versions seemed weaker. And an uncomfortable question hangs in the air: whether the providers are squeezing users or not.

Where the analysis gets stuck

The anecdote that best sums up the state of things: solving the boot sequence of a microcontroller with signed and encrypted firmware, hooking up the debugger, flashing the chip and verifying the memory dump. Days of work done in a single session.

One question remains unanswered. For problems that are already solved and documented, the model delivers. For genuinely new problems, with no prior trace, nobody knows if it works. That's where the math stops.