multimodal benchmark
CC-OCR Leaderboard
A comprehensive OCR benchmark for evaluating Large Multimodal Models (LMMs) in literacy. Comprises four OCR-centric tracks: multi-scene text reading, multilingual text reading, document parsing, and key information extraction. Contains 39 subsets with 7,058 fully annotated images, 41% sourced from real applications. Tests capabilities including text grounding, multi-orientation text recognition, and detecting hallucination/repetition across diverse visual challenges.
Updated Aug 17, 2026
Models18
Model coverage18
MetricScore
EvidenceB
CC-OCR Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelAC | Score83.4% | Percentile100.0% | Participants18 | EvidenceC | Evaluated |
| Rank02 | ModelAC | Score82.2% | Percentile94.1% | Participants18 | EvidenceC | Evaluated |
| Rank03 | ModelAC | Score81.9% | Percentile88.2% | Participants18 | EvidenceC | Evaluated |
| Rank04 | ModelAC | Score81.8% | Percentile82.3% | Participants18 | EvidenceC | Evaluated |
| Rank05 | ModelAC | Score81.5% | Percentile76.5% | Participants18 | EvidenceC | Evaluated |
| Rank06 | ModelAC | Score81.2% | Percentile70.6% | Participants18 | EvidenceC | Evaluated |
| Rank07 | ModelAC | Score81.0% | Percentile64.7% | Participants18 | EvidenceC | Evaluated |
| Rank08 | ModelAC | Score80.7% | Percentile58.8% | Participants18 | EvidenceC | Evaluated |
| Rank09 | ModelAC | Score80.7% | Percentile52.9% | Participants18 | EvidenceC | Evaluated |
| Rank10 | ModelAC | Score80.3% | Percentile47.1% | Participants18 | EvidenceC | Evaluated |
| Rank11 | ModelAC | Score79.9% | Percentile41.2% | Participants18 | EvidenceC | Evaluated |
| Rank12 | ModelAC | Score79.8% | Percentile35.3% | Participants18 | EvidenceC | Evaluated |
| Rank13 | ModelAC | Score77.8% | Percentile29.4% | Participants18 | EvidenceC | Evaluated |
| Rank14 | ModelAC | Score77.8% | Percentile23.5% | Participants18 | EvidenceC | Evaluated |
| Rank15 | ModelAC | Score77.1% | Percentile17.6% | Participants18 | EvidenceC | Evaluated |
| Rank16 | ModelAC | Score76.3% | Percentile11.8% | Participants18 | EvidenceC | Evaluated |
| Rank17 | ModelAC | Score76.2% | Percentile5.9% | Participants18 | EvidenceC | Evaluated |
| Rank18 | ModelAC | Score73.8% | Percentile0.0% | Participants18 | EvidenceC | Evaluated |
CC-OCR Highlights
The leading models and scores on this benchmark.
CC-OCR Score Distribution
A closer view of the leading scores on this benchmark.
The Top AI Models for CC-OCR
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis cc-ocr AI model leaderboard uses descending score in the benchmark's original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
- 01ACQwen3.6 PlusAlibaba Cloud / Qwen Team
- Score
- 83.4%
- Price
- $0.50 input / $3.0 output per 1M tokens
- Speed
- Up to 16 tok/s via Together
Strengths
- Ranks #1 of 18 compared models
- 100th percentile on this benchmark
- C evidence result
Considerations
- This result measures CC-OCR, not total model capability
- 02ACQwen3 VL 235B A22B InstructAlibaba Cloud / Qwen Team
- Score
- 82.2%
Strengths
- Ranks #2 of 18 compared models
- 94th percentile on this benchmark
- C evidence result
Considerations
- This result measures CC-OCR, not total model capability
- 03ACQwen3.6-35B-A3BAlibaba Cloud / Qwen Team
- Score
- 81.9%
- Price
- $0.25 input / $1.5 output per 1M tokens
Strengths
- Ranks #3 of 18 compared models
- 88th percentile on this benchmark
- C evidence result
Considerations
- This result measures CC-OCR, not total model capability
- 04ACQwen3.5-122B-A10BAlibaba Cloud / Qwen Team
- Score
- 81.8%
- Price
- $0.40 input / $3.2 output per 1M tokens
Strengths
- Ranks #4 of 18 compared models
- 82th percentile on this benchmark
- C evidence result
Considerations
- This result measures CC-OCR, not total model capability
- 05ACQwen3 VL 235B A22B ThinkingAlibaba Cloud / Qwen Team
- Score
- 81.5%
Strengths
- Ranks #5 of 18 compared models
- 76th percentile on this benchmark
- C evidence result
Considerations
- This result measures CC-OCR, not total model capability
Selection summary
Best AI Models for CC-OCR
Qwen3.6 Plus currently leads CC-OCR with 83.4%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.
What is CC-OCR?
What CC-OCR measures and how its scores work.
A comprehensive OCR benchmark for evaluating Large Multimodal Models (LMMs) in literacy. Comprises four OCR-centric tracks: multi-scene text reading, multilingual text reading, document parsing, and key information extraction. Contains 39 subsets with 7,058 fully annotated images, 41% sourced from real applications. Tests capabilities including text grounding, multi-orientation text recognition, and detecting hallucination/repetition across diverse visual challenges.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- CC-OCR
- Modality
- multimodal
- Primary category
- multimodal
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- cc-ocr|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about CC-OCR.
Which model scores highest on CC-OCR?
Qwen3.6 Plus is currently ranked first with 83.4%.
What does CC-OCR measure?
A comprehensive OCR benchmark for evaluating Large Multimodal Models (LMMs) in literacy. Comprises four OCR-centric tracks: multi-scene text reading, multilingual text reading, document parsing, and key information extraction. Contains 39 subsets with 7,058 fully annotated images, 41% sourced from real applications. Tests capabilities including text grounding, multi-orientation text recognition, and detecting hallucination/repetition across diverse visual challenges.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
18 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.