← All guides
Registry · 4 min read

The 2026 model registry: what changed this month

A living update log for major model families, context windows, and pricing snapshots.

Updated Jul 18, 2026 · Reviewed by PromptCostLab
A bright aisle lined with organized data center server racks
A model registry is infrastructure for decisions: organized, dated, and easy to audit. Image: Helpameout / Wikimedia Commons

Token and pricing information changes frequently. Use this guide for planning and verify model-specific limits and prices with the provider before production billing.

Why a living registry matters

AI model catalogs change too quickly for an undated comparison table. Providers introduce new families, retire aliases, change context or output limits, and update input, output, batch, and cache pricing. A useful registry therefore needs a verification date and a clear statement that it is a planning reference.

What PromptCostLab records

Each registry entry includes the provider, model family, context reference, input price, output price, cached-input reference, and a broad usage tier. These fields make quick comparison possible while keeping the table readable.

The registry deliberately separates model metadata from the calculator interface. Updating a record can feed the model directory, provider calculators, and comparison pages without rewriting each page.

How to verify a model before launch

Confirm the exact API model identifier in the provider documentation, check current pricing for your region and usage tier, and record the date. Then test a representative request and compare the provider’s returned usage with your planning estimate.

  • Confirm the API model ID and availability.
  • Check input, output, cache, and batch pricing.
  • Confirm context and maximum output limits.
  • Store the verification date with your application configuration.

Do not choose by recency alone

The newest flagship is not automatically the best production route. High-volume classification, extraction, and chat often benefit from a faster model. Complex reasoning may justify a premium route. Evaluate quality, latency, reliability, and cost together.

Using the registry well

Filter the PromptCostLab model directory to create a shortlist, measure your prompt, and calculate cost at realistic traffic. Treat the result as the beginning of evaluation—not as a substitute for a benchmark using your own data.

Sources & further reading

Prices, token behavior, and model limits change. These primary provider docs are the right place to verify the details before shipping.

Put this into practice.Paste your own prompt into the calculator and see its context and cost tradeoffs.Open PromptCostLab calculator →