--- title: "GPT-5.5 vs Claude Opus 4.7 vs Gemini 3.1 Pro for Structured data extraction (JSON outputs): A May 2026 Comparison" description: "GPT-5.5 vs Claude Opus 4.7 vs Gemini 3.1 Pro for structured data extraction (json outputs) — a May 2026 comparison grounded in current model prices, benchmarks, a..." canonical: https://callsphere.ai/blog/llm-comparison-structured-data-extraction-closed-vs-closed-may-2026 category: "Agentic AI & LLMs" tags: ["LLM Comparisons", "May 2026", "GPT-5.5 vs Claude Opus 4.7 vs Gemini 3.1 Pro", "Structured data extraction (JSON outputs)", "AI Models", "Cost Optimization", "Production AI", "CallSphere", "GPT-5.5", "Claude Opus 4.7"] author: "CallSphere Team" published: 2026-05-09T02:06:05.037Z updated: 2026-05-09T02:06:05.037Z --- # GPT-5.5 vs Claude Opus 4.7 vs Gemini 3.1 Pro for Structured data extraction (JSON outputs): A May 2026 Comparison > GPT-5.5 vs Claude Opus 4.7 vs Gemini 3.1 Pro for structured data extraction (json outputs) — a May 2026 comparison grounded in current model prices, benchmarks, a... # GPT-5.5 vs Claude Opus 4.7 vs Gemini 3.1 Pro for Structured data extraction (JSON outputs): A May 2026 Comparison This May 2026 comparison covers **structured data extraction (json outputs)** through the lens of **GPT-5.5 vs Claude Opus 4.7 vs Gemini 3.1 Pro**. Every model name, price, and benchmark below is grounded in May 2026 web research — no generalization, current as of the May 7, 2026 snapshot. ## Structured data extraction (JSON outputs): The 2026 Picture Structured data extraction is now table stakes via JSON schema mode. May 2026 leaders for schema compliance: GPT-5.5 and Claude Sonnet 4.5 hit 99%+ on simple-to-medium schemas; complex nested + many enum fields drop closer to 95%. For cost-optimized bulk extraction, Gemini 2.5 Flash ($0.15/$0.60) handles 90%+ of straightforward extraction at 30× lower cost than GPT-5.5. DeepSeek V4-Pro at $0.55/$0.87 with strict JSON mode is the open-weight winner. Always layer a deterministic JSON schema validator after the model — never trust schema compliance to the LLM alone. For ambiguous fields, ask the model to return null + a confidence score rather than guessing. ## GPT-5.5 vs Claude Opus 4.7 vs Gemini 3.1 Pro: How This Lens Plays For **structured data extraction (json outputs)**, the May 2026 closed-source leaderboard splits cleanly. **GPT-5.5** ($5/$30 per 1M, 128K standard context) leads agentic terminal work at 82.7% Terminal-Bench 2.0 and became the default ChatGPT model on May 5 with a reported 52.5% drop in high-risk hallucinations. **Claude Opus 4.7** ($5/$25, 1M context, native vision up to 3.75 MP, released Apr 16) tops multi-file code reasoning at 87.6% SWE-bench Verified and dominates long-context judgment work. **Gemini 3.1 Pro** ($2/$12 ≤200K, 1M context) leads scientific reasoning at 94.3% GPQA Diamond and is the cheapest of the three on input. The right pick for structured data extraction (json outputs) usually comes down to which of those three axes matters most. ## Reference Architecture for This Lens The reference architecture for **closed-source frontier matchup** applied to structured data extraction (json outputs): ```mermaid flowchart LR IN["Structured data extraction (JSON outputs) request"] --> ROUTE{Pick one frontier model} ROUTE -->|"agentic + tool calls"| GPT["GPT-5.5$5 / $30 per 1M82.7% Terminal-Bench 2.0"] ROUTE -->|"long-context reasoning"| CLAUDE["Claude Opus 4.7$5 / $25 per 1M1M ctx · 87.6% SWE-bench"] ROUTE -->|"science + math + cheap input"| GEM["Gemini 3.1 Pro$2 / $12 per 1M94.3% GPQA Diamond"] GPT --> RESP["Response"] CLAUDE --> RESP GEM --> RESP ``` ## Complex Multi-LLM System for Structured data extraction (JSON outputs) The production-shaped multi-LLM orchestration for structured data extraction (json outputs) — combining cheap, frontier, and self-hosted models in one system: ```mermaid flowchart LR IN["Unstructured inputemail · chat · doc"] --> EXTR["ExtractorSonnet 4.5 / Gemini 2.5 Flash"] EXTR --> JSON["JSON outputstrict schema mode"] JSON --> VAL["Pydantic / Zod validator (deterministic)"] VAL -->|"pass"| OUT["Structured record"] VAL -->|"fail"| EXTR OUT -.->|"ambiguous fields"| HUM["Human review queue"] ``` ## Cost Insight (May 2026) Frontier closed-source costs in May 2026: GPT-5.5 $5/$30, Claude Opus 4.7 $5/$25, Gemini 3.1 Pro $2/$12. Anthropic's prompt caching offers up to 90% discount on cached input — architect prompts with stable system + tool schemas at the top to maximize cache hits. ## How CallSphere Plays CallSphere uses structured outputs for every tool call across 6 production voice products. ## Frequently Asked Questions ### Which closed-source LLM should I default to in May 2026? GPT-5.5 is the safest default for general-purpose production — it became the ChatGPT default on May 5, 2026, has the best agentic terminal performance (82.7% Terminal-Bench 2.0), and ships with the strongest hallucination reductions of any May-2026 model. Pick Claude Opus 4.7 if you need 1M context or multi-file code reasoning. Pick Gemini 3.1 Pro if cost matters and you can live with $12/M output instead of $25-30. ### Why is Gemini 3.1 Pro so much cheaper than GPT-5.5 and Claude Opus 4.7? Google's pricing strategy in 2026 is to undercut on input tokens to win volume — $2/M input vs $5/M for both Anthropic and OpenAI. Output is closer ($12 vs $25-30). For RAG-heavy or long-context workflows where input dwarfs output, Gemini wins on cost by 2-3x. For generation-heavy work, the gap narrows. ### Should I be using Claude Mythos Preview yet? Only if you are one of the ~50 partner organizations Anthropic onboarded on April 7, 2026. Claude Mythos leads GPQA Diamond at 94.6% — a measurable step above Opus 4.6 — but is preview-gated through cybersecurity, reasoning, and coding partners. For everyone else, Opus 4.7 is the production-ready frontier from Anthropic. ## Get In Touch If **structured data extraction (json outputs)** is on your 2026 roadmap and you want to talk through the LLM choices in detail — book a scoping call. We will share the actual trade-offs we have seen across CallSphere's 6 production AI products. - **Live demo:** [callsphere.ai](https://callsphere.ai) - **Book a call:** [/contact](/contact) - **Read the blog:** [/blog](/blog) *#LLM #AI2026 #closedvsclosed #structureddataextraction #CallSphere #May2026* --- Source: https://callsphere.ai/blog/llm-comparison-structured-data-extraction-closed-vs-closed-may-2026