Top picks for JSON Extraction (2026)
Pulling structured fields out of unstructured text. Ranked from 452 live models on the OpenRouter catalog, weighted for structured output, low latency, low cost.
Updated 2026-10-13 · prices checked at this morning's rebuild
Which should you use? OpenAI: GPT-5 (batch) tops this ranking on blended score. If cost drives the decision, OpenAI: GPT-4.1 Mini (batch) is the cheapest of the leaders at $0.20/M input. To prototype without spending, Google: Gemma 4 26B A4B (free) is the best free option ranked here.
| # | Model | Score | In / 1M | Out / 1M | Context | |
|---|---|---|---|---|---|---|
| 1 | OpenAI: GPT-5 (batch)openai/gpt-5:batch | 138 | $0.62 | $5.00 | 400,000 | Details → |
| 2 | Google: Gemini 2.5 Pro (batch)google/gemini-2.5-pro:batch | 138 | $0.62 | $5.00 | 1,048,576 | Details → |
| 3 | OpenAI: o4 Mini (batch)openai/o4-mini:batch | 137 | $0.55 | $2.20 | 200,000 | Details → |
| 4 | Google: Gemini 2.5 Flash (batch)google/gemini-2.5-flash:batch | 136 | $0.15 | $1.25 | 1,048,576 | Details → |
| 5 | Google: Gemini 2.5 Flashgoogle/gemini-2.5-flash | 136 | $0.30 | $2.50 | 1,048,576 | Details → |
| 6 | OpenAI: o3 Mini (batch)openai/o3-mini:batch | 136 | $0.55 | $2.20 | 200,000 | Details → |
| 7 | OpenAI: GPT-4.1 Mini (batch)openai/gpt-4.1-mini:batch | 134 | $0.20 | $0.80 | 1,047,576 | Details → |
| 8 | OpenAI: GPT-4.1 Miniopenai/gpt-4.1-mini | 134 | $0.40 | $1.60 | 1,047,576 | Details → |
| 9 | OpenAI: GPT-4.1 Nano (batch)openai/gpt-4.1-nano:batch | 132 | $0.05 | $0.20 | 1,047,576 | Details → |
| 10 | Meta: Llama 4 Maverickmeta-llama/llama-4-maverick | 132 | $0.19 | $0.65 | 1,048,576 | Details → |
| 11 | OpenAI: GPT-4.1 Nanoopenai/gpt-4.1-nano | 132 | $0.10 | $0.40 | 1,047,576 | Details → |
| 12 | inclusionAI: Ling 3.0 Flash VLinclusionai/ling-3.0-flash-vl | 132 | $0.02 | $0.06 | 262,144 | Details → |
| 13 | Qwen: Qwen3.7 Flashqwen/qwen3.7-flash | 132 | $0.03 | $0.13 | 1,000,000 | Details → |
| 14 | inclusionAI: Ling 3.0 Flashinclusionai/ling-3.0-flash | 132 | $0.02 | $0.06 | 262,144 | Details → |
| 15 | Google: Gemma 4 26B A4B (free)google/gemma-4-26b-a4b-it:free | 132 | Free | Free | 262,144 | Details → |
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How we ranked these
For JSON Extraction, we weight models on structured output, low latency, low cost. Scores combine each model's public specs with independent benchmark results (Aider Polyglot coding scores, Artificial Analysis intelligence/coding/agentic indices) and live pricing. See full methodology →
About JSON Extraction
JSON extraction is the automated process of converting unstructured text, documents, or web content into structured, machine-readable JSON fields. You need this when you're ingesting invoices, emails, customer reviews, or any semi-formatted source and require consistent, queryable output. Good models maintain field accuracy under schema constraints, handle missing values gracefully, and don't hallucinate fields that don't exist in the source. Poor models either miss fields entirely or invent plausible-sounding data when uncertain. The main cost tradeoff: cheaper models process faster but require careful prompt engineering and validation loops, while frontier models like GPT-4 handle ambiguity better upfront but cost more per extraction.
When to use: Use this when you have piles of unstructured information (emails, PDFs, chat logs, documents) that you need organized into consistent, database-ready fields that downstream systems can actually use.
Common questions
What is the difference between JSON extraction and traditional data parsing?
Traditional parsing works with rigid, predictable formats like CSV or XML where delimiters are fixed. JSON extraction handles messy, variable text where fields are scattered or described in natural language, requiring the model to understand context and intent. Claude or GPT-4 can infer that "Sent on Tuesday" belongs in a date field even if the format varies wildly across documents.
How much does it cost to extract JSON from thousands of documents?
Costs range from under $0.01 per extraction using open-source models to $0.05-$0.10 per extraction with GPT-4, depending on document length and accuracy requirements. For 10,000 documents, budget $100-$1,000; cheaper models need more validation overhead, so total cost depends on error tolerance and whether you're hiring humans to audit.