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CC-OCR

An OCR-focused benchmark for reading and extracting text from visually complex documents and images.

How BenchLM shows CC-OCR right now

BenchLM is tracking CC-OCR in the local dataset, but exact-source verification records for these rows are still being attached. To avoid a blank benchmark page, BenchLM shows the current tracked rows below as a display-only reference table.

These tracked rows are useful for inspection and spot-checking, but until exact-source attachments are completed they should not be treated as fully verified public benchmark rows.

5 tracked modelsLocal tracked rowsAwaiting exact-source attachmentsDisplay only

Tracked score on CC-OCR — April 10, 2026

BenchLM mirrors the published tracked score view for CC-OCR. Qwen3.6 Plus leads the public snapshot at 83.4% , followed by Qwen3.5 397B (82.0%) and Kimi K2.5 (79.7%). BenchLM does not use these results to rank models overall.

5 modelsMultimodal & GroundedCurrentDisplay onlyUpdated April 10, 2026

The published CC-OCR snapshot is tightly clustered at the top: Qwen3.6 Plus sits at 83.4%, while the third row is only 3.7 points behind. The broader top-10 spread is 13.1 points, so the benchmark still separates strong models even when the leaders cluster.

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.

About 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

CurrentDisplay only

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.

Tracked score table (5 models)

1
Qwen3.6 Plusqwen3-6-plus
83.4%
2
Qwen3.5 397Bqwen3-5-397b
82.0%
3
Kimi K2.5kimi-k2-5
79.7%
4
Gemini 3 Progemini-3-pro
79.0%
5
GPT-5.2gpt-5-2
70.3%

FAQ

What does CC-OCR measure?

An OCR-focused benchmark for reading and extracting text from visually complex documents and images.

Which model leads the published CC-OCR snapshot?

Qwen3.6 Plus currently leads the published CC-OCR snapshot with a tracked score of 83.4%. BenchLM shows this benchmark for display only and does not use it in overall rankings.

How many models are evaluated on CC-OCR?

5 AI models are included in BenchLM's mirrored CC-OCR snapshot, based on the public leaderboard captured on April 10, 2026.

Last updated: April 10, 2026 · mirrored from the public benchmark leaderboard

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