The model pairs developers actually choose between in 2026. Each page has a live calculator that swaps the same prompt across both models so you can see the real difference, not just the headline rate.
The headline price per million tokens is a useful starting point, but it is almost never the full picture. Three factors routinely flip cost comparisons in practice: tokenization efficiency, caching support, and the input-to-output ratio of your specific workload.
Tokenization efficiency varies by vendor. The same English sentence costs ~10% fewer tokens on Claude than on GPT. The same Russian sentence costs ~30% fewer tokens on Gemini than on GPT. For multilingual products, this can swing the comparison entirely.
Caching support is the biggest single discount available. If your application sends a long static system message, all three major vendors will cache it and bill subsequent reads at ~10% of the standard rate. The vendor with the larger context window — and thus the larger cacheable prefix — often wins on total cost even if the per-token rate is higher.
Input/output ratio matters because output costs ~5× more than input across the board. A document Q&A workload (long input, short output) cares mostly about the input rate. A content generation workload (short input, long output) cares mostly about the output rate. Run the math on your own ratio before deciding.