Benchmark profile
CC-OCR
An OCR-focused benchmark for reading and extracting text from visually complex documents and images.
Data verifiedBenchmark score on CC-OCR — July 23, 2026
BenchLM mirrors the published score view for CC-OCR. Qwen3.6-35B-A3B leads the public snapshot at 81.9% , followed by Qwen3.6-27B (81.2%). BenchLM does not use these results to rank models overall.
Qwen3.6-35B-A3B
Alibaba
qwen3-6-35b-a3b
Qwen3.6-27B
Alibaba
qwen3-6-27b
Benchmark score table (2 models)
ScoreAbout CC-OCR
Year
2026
Tasks
Optical character recognition
Format
Text extraction from images and documents
Difficulty
Document reading
CC-OCR is useful as a direct check on raw reading ability before higher-level reasoning. It highlights whether failures come from extraction quality or from later reasoning over the extracted content.
BenchLM freshness & provenance
Version
CC-OCR 2026
Refresh cadence
Quarterly
Staleness state
Current
Question availability
Public benchmark set
BenchLM uses freshness metadata to decide whether a benchmark should still be treated as a strong differentiator, a benchmark to watch, or a display-only reference. For the full scoring policy, see the BenchLM methodology page.
FAQ
What does CC-OCR measure?
An OCR-focused benchmark for reading and extracting text from visually complex documents and images.
Which model scores highest on CC-OCR?
Qwen3.6-35B-A3B by Alibaba currently leads with a score of 81.9% on CC-OCR.
How many models are evaluated on CC-OCR?
2 AI models have been evaluated on CC-OCR on BenchLM.
Compare Top Models on CC-OCR
Choose a model with this week’s evidence
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