AskSoma vs KundliGPT

The most generous free trial in this comparison

KundliGPT gives you three questions with no signup at all, and a full reading as a guest without creating an account. Its site describes NASA JPL-backed Swiss Ephemeris calculations interpreted through Rashi, Nakshatra, Dasha periods, Drishti and Yogas — the correct Parashari furniture, named correctly. Signing up adds coins and a "Pro" model advertised as roughly twice as accurate but slower.

Credit where it is due: no-signup access is the single most user-respecting thing in this category. Most products, ours included, ask for an account before the good part. If you want to test whether AI astrology is worth anything to you at all, KundliGPT lets you find out in about ninety seconds, and that is a real service.

Where a per-question model stops fitting the subject

The structural difference is that KundliGPT is question-shaped and AskSoma is relationship-shaped. Coins meter questions; a reading that costs coins encourages you to ask the big one and stop. But a birth chart rewards the opposite behaviour — the useful material usually surfaces on the third or fourth exchange, when you stop asking "what does my chart say about career" and start asking "so why did the last two years feel like this".

There is also the "2x accuracy" framing on the Pro model, which is worth pausing on as a claim rather than as a slight. Accuracy in astrology is not a quantity anyone currently measures — there is no benchmark, no held-out test set, no agreed scoring. A bigger model produces more fluent and better-reasoned prose, which is genuinely valuable and not the same thing as being twice as right. We hold ourselves to the same standard: we publish which predictions landed rather than claiming a multiplier.

What no-signup access costs you technically

Reading a chart as a guest is convenient, and it has a specific technical consequence: with no account there is nothing to attach a history to. Each session starts from zero. Ask about your career today and your marriage next week, and the second conversation cannot know what the first established about your Dasha.

This matters more in Jyotish than in most subjects, because the framework is cumulative. Your Mahadasha lord colours everything for years at a time; the same transit means something different in a Venus period than in a Saturn one. A system that re-derives your chart every session gets the arithmetic right every time and still cannot build on itself — it is a calculator that happens to speak English.

AskSoma keeps the chart, the conversation and an explicit memory of what you have told it — that you are weighing a move, that your father has been ill, that the job started in March. That memory is also the thing that makes us worth leaving: everything we know about you is exportable and erasable on request, because a memory you cannot walk away from is a hostage, not a feature.

Where the astrology actually happens

The deepest difference between these two products is architectural, and it is invisible from the outside. In a chatbot-first design the language model does the astrology: it is given birth details and chart data and asked to reason about them in prose. Bigger model, better reasoning — hence "2x accuracy" being a coherent thing to advertise.

AskSoma inverts that. A deterministic engine computes the Vimshottari Dasha tree, the transits against your natal Moon, each planet's functional dignity for your ascendant, and the yogas that are actually present. Those results are handed to the language model as source-of-truth, and its job is narration, not judgement. It cannot move a date, because it never chose one.

The practical difference shows up on timing questions. Ask "when" of a model doing the astrology and you get a plausible-sounding date generated by a system that is fundamentally predicting text. Ask "when" of an engine and you get the window the Dasha arithmetic produces — which may be less satisfying, occasionally says "no strong window in the next year", and is at least a claim about your chart rather than about language.