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Data extraction

Compare Claude Sonnet, GPT-5 Mini, Gemini Flash for invoice and form extraction. From $30/mo for 200K docs. JSON mode tested.

Your usage

Default assumptions
Monthly requests200,000
Avg input tokens2000
Avg output tokens300

When to use this scenario

Structured extraction converts unstructured documents — invoices, purchase orders, medical intake forms, legal clauses, customs declarations — into typed JSON records. Accuracy here is binary: a missing VAT number or wrong line-item total is a hard failure that creates downstream reconciliation work, not a soft quality issue.

Claude Sonnet excels at precise field extraction with low hallucination rates on edge-case document layouts. At $3/million input tokens, processing 200,000 invoices per month (avg 2K tokens each) runs $1,200 in input. The structured-output capability (native JSON mode, schema enforcement) further reduces post-processing bugs.

Choose the primary based on layout complexity. Multi-column PDFs with merged cells, handwritten overrides, or mixed languages stress models differently. Run a 500-document validation set across candidates before committing; extraction F1 variation between models on messy real-world documents routinely exceeds 15 points.

Common pitfalls

  • Relying on JSON mode without schema validation downstream — models occasionally emit valid JSON that violates your schema silently
  • Using the same prompt for typed (computer-generated) and scanned (OCR'd) documents — scanned text requires more lenient fuzzy matching in the prompt
  • Ignoring confidence calibration: a model that always returns a value is worse than one that returns null for ambiguous fields you can re-queue for human review
  • Overbuilding context: including full document text when only the header and line-items section matters doubles input tokens and cost unnecessarily

Recommended routing

Sorted by best value for your usage
PRIMARY
Claude 4.6 Sonnet
Anthropic · quality 89 · 85 tok/s
Monthly cost$2.1K
Vs baseline−-91%
P50 latency1.1s
Use this
FALLBACK
GPT-5 mini
OpenAI · quality 84 · 280 tok/s
Monthly cost$220
Vs baseline−80%
P50 latency0.3s
Add as fallback
DeepSeek V3.5
DeepSeek · quality 81 · 95 tok/s
Monthly cost$73
Vs baseline−93%
P50 latency1.5s
Try

Baseline = GPT-5 at the same usage = $1.1K/mo.

Routing simulator

Phase 2 preview

Drag the slider to split traffic between Claude 4.6 Sonnet (primary) and GPT-5 mini (fallback). See how your monthly bill moves — without writing a line of gateway code.

Primary: Claude 4.6 SonnetFallback: GPT-5 mini
70% Claude30% GPT-5
Blended monthly cost$1.5Kat the usage assumed above
Vs all-primary−27%$2.1K → $1.5K

Phase 2 turns this routing into a real OpenAI-compatible endpoint — one key, one bill, automatic failover. Drop your email to be notified at launch.

Stored in your browser only until our email backend lands. No tracking, one click to remove.

Use this routing via API

Phase 2 preview · gateway not live yet
PHASE 2 PREVIEW · gateway not live yetThis endpoint does not exist yet. The gateway is in Phase 2 — what you see below is a design preview of the planned interface, not a live API. We will email subscribers when it launches.
Preview the planned API call
$ curl https://api.aipricly.com/v1/chat/completions \
  -H "Authorization: Bearer $AIPC_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "scenario": "data-extraction",
    "messages": [{"role": "user", "content": "..."}]
  }'
Get notified at launch

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