An OCR-focused benchmark for reading and extracting text from visually complex documents and images.
As of March 2026, Qwen3.6 Plus leads the CC-OCR leaderboard with 83.4% , followed by Qwen3.5 397B (82.0%) and Kimi K2.5 (79.7%).
Qwen3.6 Plus
Alibaba
Qwen3.5 397B
Alibaba
Kimi K2.5
Moonshot AI
According to BenchLM.ai, Qwen3.6 Plus leads the CC-OCR benchmark with a score of 83.4%, followed by Qwen3.5 397B (82.0%) and Kimi K2.5 (79.7%). The scores show moderate spread, with meaningful differences between the top tier and mid-tier models.
5 models have been evaluated on CC-OCR. The benchmark falls in the Multimodal & Grounded category. This category carries a 12% weight in BenchLM.ai's overall scoring system. CC-OCR is currently displayed for reference but excluded from the scoring formula, so it does not directly affect overall rankings.
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.
Qwen3.6 launch benchmarksVersion
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.
An OCR-focused benchmark for reading and extracting text from visually complex documents and images.
Qwen3.6 Plus by Alibaba currently leads with a score of 83.4% on CC-OCR.
5 AI models have been evaluated on CC-OCR on BenchLM.
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