Data Extraction: The Right Question Isn't "Which Model"
Once you've narrowed a model shortlist on the main TokenRate calculator, the Compare Prices side-by-side view is where you stack them for a decision. Each row shows the provider, the model ID (the one you'd paste into your SDK), per-1M input and output costs, the context window, and the blended quality score. For Data Extraction the picking decision usually defaults to "use the model my last project used" — which is roughly the worst possible heuristic in 2026, because the price-quality frontier has shifted three times in the past year. The right question is "which combination of price tier, context length, and quality score fits Data Extraction traffic patterns?" The Compare Prices grid is built for that question. This guide walks through three picks for Data Extraction and shows how to grid them. 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.
The Workload Profile of Data Extraction
Data Extraction workloads have a few distinguishing characteristics: input-heavy (document content), structured-output (JSON or fields), quality non-negotiable (extraction errors propagate downstream); often run at scale on document libraries. That profile tells you which columns of the Compare Prices grid matter most. Quality first (Q 70+ for production extraction), input cost second (documents are large), output cost third (structured output is compact). It also tells you which tier you should be in. For most Data Extraction traffic, the Balanced tier is the production default — quality high enough to ship to real users at scale, price low enough to make the unit economics work.
Top Pick: Claude Sonnet 4.7
For Data Extraction, Claude Sonnet 4.7 is the default candidate. Pricing: $3.00 input / $15.00 output per 1M tokens. Context: 200K. Quality score: 80. Tier: balanced. Why it wins: high accuracy on structured-output workloads at a price point that scales to document libraries. Add it to the Compare Prices grid and the Value column makes the case visually (26.7 quality per dollar of input cost). Where it loses: when very long documents (50K+ tokens each) push the bill into territory where a cheaper long-context model dominates.
Runner-Ups and When to Pick Them
**GPT-5** ($1.25 / $10.00, Q82) — pick this when quality is non-negotiable and the bill is a rounding error against the value of correct output. **Gemini 2.5 Pro** ($1.25 / $10.00, Q78) — pick this when you want the production-default balance of quality (78) and price ($1.25 input). All three live in the same Compare Prices view so the comparison is one screen, not three browser tabs. For workload-specific cost modeling, run your token volume through /tools/api-cost-estimator.
Compare-Prices Workflow for Data Extraction
Workflow: (1) open /tools/compare-prices, (2) check the three picks across their provider dropdowns, (3) sort the resulting grid by Value column, (4) shortlist the top 1-2, (5) run an A/B against your real Data Extraction traffic for a week. The shortlisting step is where 90% of the time savings happen — the grid eliminates obvious losers (low quality, wrong context, output-cost surprises) in seconds. 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. Open /tools/compare-prices now, pick your provider dropdowns, and pin the shortlist that matches your workload.