Best AI Models for OCR

How this ranking is produced

Recommendation engine phase-2.0.0, 13 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 OCR / Document AI

OCR

100%

Multimodal

70%

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 Qwen2.5-VL 72B — Alibaba

   Fit score 84 out of 100.

   • Strong cost efficiency (100/100).
   • Strong ocr (88/100).
   • Strong multimodal (78/100).

   Capability evidence

   • OCR — evidence confidence 40% — Top-tier ocr (88/100). via docvqa, ocrbench
   • Multimodal — evidence confidence 40% — Top-tier multimodal (78/100). via mathvista, mmmu

   Confidence: 40% · Freshness: Evaluation date not disclosed · Evidence: 100% · Benchmarks: 100% · Stability: 50%

2. #2 Llama 4 Scout — Meta

   Fit score 84 out of 100.

   • Strong cost efficiency (94/100).
   • Strong ocr (89/100).
   • Strong multimodal (76/100).
   • Caution: Weak coding (33/100).

   Capability evidence

   • OCR — evidence confidence 40% — Top-tier ocr (89/100). via docvqa, chartqa
   • Multimodal — evidence confidence 40% — Top-tier multimodal (76/100). via mathvista, mmmu

   Confidence: 40% · Freshness: Evaluation date not disclosed · Evidence: 100% · Benchmarks: 100% · Stability: 100%

3. #3 Pixtral 12B — Mistral AI

   Fit score 77 out of 100.

   • Strong cost efficiency (96/100).
   • Strong ocr (80/100).

   Capability evidence

   • OCR — evidence confidence 40% — Top-tier ocr (80/100). via docvqa, chartqa

   Confidence: 40% · Freshness: Evaluation date not disclosed · Evidence: 100% · Benchmarks: 100% · Stability: 38%

4. #4 Claude Opus 4 — Anthropic

   Fit score 77 out of 100.

   • Strong reasoning (78/100).
   • Strong multimodal (77/100).
   • Strong ocr (77/100).

   Capability evidence

   • OCR — evidence confidence 40% — Top-tier ocr (77/100).
   • Multimodal — evidence confidence 40% — Top-tier multimodal (77/100). via mmmu

   Confidence: 40% · Freshness: Evaluation date not disclosed · Evidence: 50% · Benchmarks: 50% · Stability: 88%

5. #5 Claude Sonnet 4 — Anthropic

   Fit score 74 out of 100.

   Confidence: 40% · Freshness: Evaluation date not disclosed · Evidence: 50% · Benchmarks: 50% · Stability: 88%

6. #6 InternVL2.5 78B — OpenGVLab

   Fit score 70 out of 100.

   • Strong cost efficiency (100/100).
   • Caution: Limited benchmark coverage (1 scores).

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

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 Qwen2.5-VL 72B — evidence confidence 80%
2. #2 Llama 4 Scout — evidence confidence 80%
3. #3 Pixtral 12B — evidence confidence 80%
4. #4 Claude Opus 4 — evidence confidence 80%
5. #5 Claude Sonnet 4 — evidence confidence 80%
6. #6 InternVL2.5 78B — evidence confidence 80%

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

Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to Best AI Models for OCR.

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