Best AI Models for JSON Extraction

How this ranking is produced

Recommendation engine phase-2.0.0, 11 benchmarks evaluated, aggregate freshness fresh. Computed from the canonical ModelRefs registry when this page was built on 2026-09-04, and recomputed on every deploy.

Each candidate below is a provisional fit signal under the stated constraints, not a guarantee, a certification, or a final ranking. Fit scores compare models against this use case's capability weights using current ModelRefs evidence; they are not accuracy rates, benchmark results, or production-readiness claims.

What we measure for Structured Extraction

Structured Output

100%

Instruction Following

80%

Weights derived from the ModelRefs capability ontology. Scores sourced from primary benchmark leaderboards where available; expansion entries carry lower confidence (0.65).

Ranked candidates

1. #1 Llama 3.1 405B — Meta

   Fit score 26 out of 100.

   • Strong reasoning (89/100).
   • Strong instruction following (89/100).
   • Caution: Overall fit moderate (26/100) — consider alternatives.
   • Caution: Limited benchmark coverage (1 scores).

   Capability evidence

   • Structured Output — evidence confidence 40% — Underperforms on structured output (0/100).
   • Instruction Following — evidence confidence 40% — Top-tier instruction following (89/100).

   Confidence: 40% · Freshness: Evaluation date not disclosed · Evidence: 0% · Benchmarks: 0% · Stability: 13%

2. #2 GPT-5 — OpenAI

   Fit score 26 out of 100.

   • Strong hallucination resistance (99/100).
   • Strong tool use (97/100).
   • Strong reasoning (90/100).
   • Caution: Overall fit moderate (26/100) — consider alternatives.

   Capability evidence

   • Structured Output — evidence confidence 40% — Underperforms on structured output (0/100).
   • Instruction Following — evidence confidence 40% — Top-tier instruction following (88/100).

   Confidence: 40% · Freshness: Evaluation date not disclosed · Evidence: 0% · Benchmarks: 0% · Stability: 88%

3. #3 GPT-4o — OpenAI

   Fit score 26 out of 100.

   • Strong multilingual (90/100).
   • Strong instruction following (87/100).
   • Caution: Weak coding (33/100).
   • Caution: Weak agentic (33/100).
   • Caution: Overall fit moderate (26/100) — consider alternatives.

   Capability evidence

   • Structured Output — evidence confidence 40% — Underperforms on structured output (0/100).
   • Instruction Following — evidence confidence 40% — Top-tier instruction following (87/100).

   Confidence: 40% · Freshness: Evaluation date not disclosed · Evidence: 0% · Benchmarks: 0% · Stability: 63%

4. #4 Mistral Large 2 — Mistral AI

   Fit score 26 out of 100.

   • Strong reasoning (86/100).
   • Strong instruction following (86/100).
   • Caution: Overall fit moderate (26/100) — consider alternatives.
   • Caution: Limited benchmark coverage (1 scores).

   Capability evidence

   • Structured Output — evidence confidence 40% — Underperforms on structured output (0/100).
   • Instruction Following — evidence confidence 40% — Top-tier instruction following (86/100). via mt-bench

   Confidence: 40% · Freshness: Evaluation date not disclosed · Evidence: 50% · Benchmarks: 50% · Stability: 13%

5. #5 Gemini 1.5 Pro — Google

   Fit score 25 out of 100.

   • Strong reasoning (86/100).
   • Strong instruction following (86/100).
   • Caution: Overall fit moderate (25/100) — consider alternatives.
   • Caution: Limited benchmark coverage (1 scores).

   Capability evidence

   • Structured Output — evidence confidence 40% — Underperforms on structured output (0/100).
   • Instruction Following — evidence confidence 40% — Top-tier instruction following (86/100).

   Confidence: 40% · Freshness: Evaluation date not disclosed · Evidence: 0% · Benchmarks: 0% · Stability: 13%

6. #6 Llama 3.1 70B — Meta

   Fit score 25 out of 100.

   • Strong cost efficiency (86/100).
   • Strong reasoning (84/100).
   • Strong instruction following (84/100).
   • Caution: Overall fit moderate (25/100) — consider alternatives.
   • Caution: Limited benchmark coverage (1 scores).

   Capability evidence

   • Structured Output — evidence confidence 40% — Underperforms on structured output (0/100).
   • Instruction Following — evidence confidence 40% — Top-tier instruction following (84/100).

   Confidence: 40% · Freshness: Evaluation date not disclosed · Evidence: 0% · Benchmarks: 0% · Stability: 13%

7. #7 Mixtral 8x7B — Mistral AI

   Fit score 25 out of 100.

   • Strong cost efficiency (91/100).
   • Strong reasoning (83/100).
   • Strong instruction following (83/100).
   • Caution: Overall fit moderate (25/100) — consider alternatives.
   • Caution: Limited benchmark coverage (1 scores).

   Capability evidence

   • Structured Output — evidence confidence 40% — Underperforms on structured output (0/100).
   • Instruction Following — evidence confidence 40% — Top-tier instruction following (83/100). via mt-bench

   Confidence: 40% · Freshness: Evaluation date not disclosed · Evidence: 50% · Benchmarks: 50% · Stability: 13%

8. #8 Nemotron-4 340B — NVIDIA

   Fit score 23 out of 100.

   • Strong cost efficiency (80/100).
   • Strong reasoning (79/100).
   • Strong instruction following (79/100).
   • Caution: Overall fit moderate (23/100) — consider alternatives.
   • Caution: Limited benchmark coverage (1 scores).

   Capability evidence

   • Structured Output — evidence confidence 40% — Underperforms on structured output (0/100).
   • Instruction Following — evidence confidence 40% — Top-tier instruction following (79/100).

   Confidence: 40% · Freshness: Evaluation date not disclosed · Evidence: 0% · Benchmarks: 0% · Stability: 13%

9. #9 Command R+ — Cohere

   Fit score 22 out of 100.

   • Strong reasoning (76/100).
   • Strong instruction following (76/100).
   • Caution: Overall fit moderate (22/100) — consider alternatives.
   • Caution: Limited benchmark coverage (1 scores).

   Capability evidence

   • Structured Output — evidence confidence 40% — Underperforms on structured output (0/100).
   • Instruction Following — evidence confidence 40% — Top-tier instruction following (76/100).

   Confidence: 40% · Freshness: Evaluation date not disclosed · Evidence: 0% · Benchmarks: 0% · Stability: 13%

10. #10 GPT-5 Mini — OpenAI

    Fit score 21 out of 100.

    • Strong hallucination resistance (99/100).
    • Strong long context (89/100).
    • Strong rag suitability (89/100).
    • Caution: Overall fit moderate (21/100) — consider alternatives.

    Capability evidence

    • Structured Output — evidence confidence 40% — Underperforms on structured output (0/100).

    Confidence: 40% · Freshness: Evaluation date not disclosed · Evidence: 0% · Benchmarks: 0% · Stability: 88%

Knowledge Graph signal

Independent cross-validation from the ModelRefs semantic graph. Models below were identified via graph traversal of benchmark to capability to use-case edges — a separate signal from the recommendation engine above.

Capability fit describes how strongly a model's measured capabilities match this use case. It is not an accuracy rate or production-readiness guarantee. Evidence confidence describes how complete and well-supported the evidence behind that fit is; missing or stack-level requirements lower confidence.

1. #1 Llama 3.1 405B — evidence confidence 70%
2. #2 GPT-5 — evidence confidence 70%
3. #3 GPT-4o — evidence confidence 70%
4. #4 Mistral Large 2 — evidence confidence 70%
5. #5 Gemini 1.5 Pro — evidence confidence 70%
6. #6 Llama 3.1 70B — evidence confidence 70%

Limits of this ranking

Coverage is uneven. A model ranks only where ModelRefs holds benchmark-eligible evidence for the capabilities this use case requires, so a strong model with thin evidence can rank low or be absent entirely. Confidence, evidence, and benchmark-coverage figures beside each candidate say how well supported its position is — read them before acting on the order.

Continue your research

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