Dishaya / AI Pricing Comparison
AI Model Pricing Comparison, 2026
Why the same task can cost ten times more depending on the model, and how to read per-token pricing without getting fooled.
AI models are priced per token, and the spread is enormous: a frontier model can cost roughly ten times more per token than a strong open model that answers everyday work just as well. Because a given task uses a similar number of tokens on any model, that price gap flows straight to your bill. The practical takeaway is not "pick the cheapest" , it is "match each task to the cheapest model that can actually do it."
How AI pricing works
Almost all model APIs charge per token, roughly, per chunk of a word, and price input and output tokens separately, with output usually the more expensive of the two. So the cost of a task depends on three things: how many tokens go in, how many come out, and the per-token price of the model you chose. The first two are mostly fixed by the task; the third is your decision, and it is where an order-of-magnitude difference hides.
Illustrative price spread
The figures below are illustrative published list prices per million output tokens, as a rough snapshot. They are here to show the shape of the market, a wide spread between frontier and open models, not as a live price sheet.
| Tier | Example models | Relative cost |
|---|---|---|
| Frontier | GPT-class, Claude Opus-class | Highest (≈10×) |
| Mid / balanced | Claude Sonnet-class, Gemini Pro-class | Moderate (≈6×) |
| Strong open | GLM, Llama-class | Low (≈1×) |
| Cheapest open | DeepSeek-class | Lowest (<1×) |
Cheapest is not the goal
The mistake in the other direction is sending everything to the cheapest model. Frontier models genuinely lead on the hardest reasoning, long-context, and code tasks. The winning strategy is neither "always premium" nor "always cheap", it is to send each request to the cheapest model that clears the quality bar for that specific request, and escalate the rest. That is exactly what model routing automates.
How to compare fairly
- Compare on your own prompts. Benchmarks rarely match your workload. Run the same real prompt across models and judge the answers.
- Count both directions. A model with cheap input but expensive output can cost more on generation-heavy work.
- Include reliability. A slightly cheaper model that rate-limits or times out costs you retries and lost time.
- Watch for markup. Some platforms add margin over provider pricing; bringing your own keys removes it.
Compare live in Dishaya
Dishaya's Compare mode runs one prompt across several models side by side so you can see the quality-versus-price tradeoff on your actual work, and its router then picks the best-value model for you automatically on every prompt. You bring your own keys with no markup, or use Dishaya credits.
Why does the same AI task cost different amounts on different models?
Models are priced per token, and frontier models can cost roughly ten times more per token than strong open ones. Since a task uses a similar token count on any model, the price gap flows straight to your bill.
Is the most expensive AI model always the best?
No. Premium models lead on the hardest tasks, but for everyday work strong open models are often indistinguishable at a fraction of the price.