Why Multimodal Pricing Matters in a Cost Comparison
TokenRate's new Compare Prices grid puts every model's per-token rates, context window, and quality score in a single side-by-side view. The point: stop flipping between provider pricing pages and OpenRouter tabs. You pick a provider dropdown, check the models you want, repeat for each provider, and the grid stacks every pick into one comparison table. Most teams compare LLMs on per-token input price and call it a day. Multimodal Pricing is one of the under-attended dimensions that shows up in the Compare Prices grid — and ignoring it is where the surprise bills come from. Vision-capable LLMs charge by image (sometimes by image resolution or tile count). The compare-prices grid surfaces the per-token rate; the per-image rate is documented per-provider. See also: filter LLM models by tier, cost, quality, Value column vs tokens-per-dollar, and how to pick an LLM by quality score and cost.
How Multimodal Pricing Appears in the Grid
Most providers convert images to token-equivalents under the hood (e.g., a 1024×1024 image ≈ 1,000-2,000 input tokens). The grid's per-token rate then applies — but the conversion ratio varies by provider.
Reading the Spread
Cost per vision query in 2026: GPT-5 ~$0.001-$0.003 per image; Claude Opus 4 ~$0.024 per image; Gemini 2.5 Pro ~$0.001-$0.003 per image. That's a 10x+ spread driven by the underlying token-equivalent.
Practical Implications
For high-volume vision workloads (10K+ images/day), the provider choice has bigger bill impact than the model choice. Gemini and GPT often win on volume; Claude wins on visual reasoning quality.
Workflow: From Grid to Decision
In the Compare Prices grid, check the per-token rate and ballpark the image conversion via the provider's documentation. For monthly projection at your image volume, use /tools/api-cost-estimator.