OpenRouter LLM Research: Cheapest Effective Models for Invoice Extraction & General AI Tasks

Executive Summary

After pulling live pricing from the OpenRouter API (312+ models) and cross-referencing web benchmarks, here are the ranked recommendations for UnitCycle's two use cases.

Use Case 1: Invoice Field Extraction (Structured JSON Output)

Requirements: After OCR, extract vendor name, amounts, dates, line items, GL codes as structured JSON. Must reliably produce valid JSON every time.

Top Picks — Ranked by Value (Quality-per-Dollar)

Cost Estimate for Invoice Processing

Assuming a typical invoice = ~2,000 tokens input (OCR markdown + system prompt + schema), ~500 tokens output (extracted JSON):

At these prices, even 100,000 invoices/year costs under $40 with Gemini Flash.

Use Case 2: General AI Tasks (Matching, Scoring, Anomaly Detection, Summaries)

Requirements: Property/vendor matching, confidence scoring, anomaly detection in rent rolls, AI summaries. Light reasoning, not as format-critical.

Top Picks — Ranked by Value

RECOMMENDATION: Best Strategy for UnitCycle

Primary Model: Google Gemini 2.0 Flash (google/gemini-2.0-flash-001)

• $0.10 input / $0.40 output per million tokens
• Use for BOTH invoice extraction AND general AI tasks
• Native structured output (json_schema mode) via OpenRouter
• 1M token context window (handles any document)
• 97.1% quality on diverse benchmarks at lowest cost tier
• Multimodal (can process images too if needed later)
• Battle-tested, production-stable from Google

Fallback / Cost-Optimized: Qwen3-32B (qwen/qwen3-32b)

• $0.08 input / $0.24 output — even cheaper
• Use as fallback if Gemini has rate limits or outages
• Native structured output support
• Open-source model, multiple providers on OpenRouter

Heavy Reasoning Tasks: Qwen3-235B-A22B-2507 (qwen/qwen3-235b-a22b-2507)

• $0.071 input / $0.10 output — cheapest "frontier-class" reasoning
• Use for anomaly detection, complex pattern matching, AI summaries requiring deeper analysis
• Has reasoning/thinking mode for chain-of-thought

NOT Recommended:

• Claude 3.5 Haiku ($0.80/$4.00) — 8x more expensive than Gemini Flash for marginal quality gain on extraction tasks
• Claude Haiku 4.5 ($1.00/$5.00) — Even more expensive. Save Claude for tasks that truly need it.
• DeepSeek R1 ($0.70/$2.50) — Overkill reasoning model for these tasks. Use V3.1 instead.
• Free models — Rate-limited (20 req/min, 200 req/day). Not viable for production.

Implementation Notes

OpenRouter API Call (Invoice Extraction Example)

import requests

response = requests.post(
    "https://openrouter.ai/api/v1/chat/completions",
    headers={
        "Authorization": "Bearer YOUR_OPENROUTER_KEY",
        "Content-Type": "application/json",
    },
    json={
        "model": "google/gemini-2.0-flash-001",
        "response_format": {
            "type": "json_schema",
            "json_schema": {
                "name": "invoice_extraction",
                "strict": True,
                "schema": {
                    "type": "object",
                    "properties": {
                        "vendor_name": {"type": "string"},
                        "invoice_number": {"type": "string"},
                        "invoice_date": {"type": "string"},
                        "due_date": {"type": "string"},
                        "total_amount": {"type": "number"},
                        "line_items": {
                            "type": "array",
                            "items": {
                                "type": "object",
                                "properties": {
                                    "description": {"type": "string"},
                                    "quantity": {"type": "number"},
                                    "unit_price": {"type": "number"},
                                    "amount": {"type": "number"},
                                    "gl_code": {"type": "string"}
                                }
                            }
                        }
                    },
                    "required": ["vendor_name", "total_amount", "line_items"]
                }
            }
        },
        "messages": [
            {"role": "system", "content": "Extract invoice data from the OCR text. Return structured JSON."},
            {"role": "user", "content": "<OCR markdown text here>"}
        ]
    }
)

Key OpenRouter Features to Use:

1. response_format: json_schema — Forces valid JSON matching your exact Pydantic schema
2. Provider routing — OpenRouter auto-routes to cheapest/fastest provider for each model
3. Response healing plugin — Auto-fixes malformed JSON responses (safety net)
4. Streaming — Supported for structured outputs too

Tiered Strategy for Production:

1. Tier 1 (default): Gemini 2.0 Flash — handles 95% of invoices
2. Tier 2 (complex): If extraction confidence < 80%, retry with DeepSeek V3.1 or Qwen3-235B
3. Tier 3 (manual): If both fail, flag for human review

Sources

• OpenRouter Structured Outputs Documentation
• Claude vs GPT vs Gemini: Which AI Wins at Invoice Extraction?
• I Tested 15 LLMs: Claude, GPT, Gemini on 38 Tasks
• LLMs for Structured Data Extraction from PDFs in 2026
• DeepSeek V3.2 Prompting Techniques
• Gemini API Pricing 2026
• Top AI Models on OpenRouter March 2026
• Best Qwen Models in 2026
• OpenRouter Pricing Calculator
• Gemini 2.5 Flash Lite on OpenRouter
• Structured outputs with OpenRouter via Instructor