How do I open the Compare Prices grid?

Two ways: click the 'Compare Prices' tab at the top of the calculator card on the home page, or navigate directly to /tools/compare-prices. The standalone page is also linked from the main navigation under 'Tools'.

Can I share my comparison with teammates?

Yes — the page URL captures the current state. Send the link in Slack and your teammate sees the same grid. Useful for procurement and architecture-review meetings.

Is the data live or cached?

Live from OpenRouter (prices) and a blended Arena AI + Artificial Analysis pipeline (quality), refreshed on a 60-minute incremental cache. So the grid is at most an hour stale.

Where do I go after the grid to project monthly cost?

Once you've picked a winner, go to /tools/api-cost-estimator and plug in the model + your expected monthly token volume. The estimator does the per-1M math against your real workload mix.

Onboarding New Engineers to LLM Cost via Compare Prices

What This Walkthrough Covers

The Compare Prices tool is the fastest way to put a shortlist of LLMs in a single grid — input cost, output cost, context window, and quality score in stacked columns you can scan vertically. Provider dropdowns let you mix models across Anthropic, OpenAI, Google, Meta, DeepSeek, Mistral, and xAI without leaving the page. This guide walks through using the Compare Prices grid as an onboarding tool for engineers new to LLM cost — a workflow that takes 90 seconds once you've done it twice. Related reading: quality per dollar LLM ranking 2026, LLM color-coded quality badges explained, and why the cheapest LLM isn't always the best value.

Step 1: Set Up Your Provider Dropdowns

Open /tools/compare-prices. The page loads with seven provider dropdowns: Anthropic, OpenAI, Google, Meta, DeepSeek, Mistral, xAI. For this workflow, you'll want to click through 2-3 of them and tick the models you want in the grid. The dropdowns are independent — checking a model in one doesn't affect the others, and the grid updates live as you check or uncheck.

Step 2: Read the Grid

The grid has columns for input cost per 1M, output cost per 1M, context window, and quality score. Scan vertically by column to compare like-to-like. The model ID (Anthropic's "claude-sonnet-4-7", OpenAI's "gpt-5", etc.) is what you'd paste into your SDK — useful for the copy-to-implementation step. Watch for output-to-input ratios — a 5× ratio means generation-heavy workloads cost 5× more than the input rate suggests.

Step 3: Make the Decision

Three lenses to apply: (1) is the quality score above the floor for your workload, (2) does the Value column (quality ÷ input cost) rank competitively, (3) does the output cost match your expected reply length? The grid surfaces all three in one view, which is the productivity unlock over scrolling pricing pages. For tighter quality / cost shortlisting, combine with the Filter panel on the main calculator before opening the grid.

Step 4: Operationalize

Once you've picked a winner, plug the model ID and your token volume into /tools/api-cost-estimator for a monthly bill projection. For multi-model routing decisions, also check multi-model routing with quality scores. The grid pulls prices live from OpenRouter and quality from a blended Arena AI + Artificial Analysis pipeline — both refresh on a 60-minute incremental cache, so the comparison reflects current rates not a baked-in snapshot. Try the comparison yourself at /tools/compare-prices — it's the fastest way to stack model cost, context, and quality in a single grid.