The LLM benchmark aggregator.

TeleOCR

Other

Strongest in Document parsing (#47 of 76), weakest in Knowledge (#121 of 190). Above par in 0 of 2 scopes. Among the models it meets almost everywhere, it finishes behind Claude Opus 5.5 and Claude Fable 5.1 and ahead of Claude Haiku 4.5 and GPT-5 Mini.

§ 1 · Profile

What it is good at

Bars run from 50 — the average of the models each source lists — so right of the line is above par. The middle column is the gap to whoever leads that scope.

Knowledge49.2−13.6#121/1901/3

Document parsing49.2−14.5#47/761/1

§ 2 · Head to head

What it beats, and what beats it

The same models turn up scope after scope. Counted once: where both were placed, who finished higher. Bars run right for TeleOCR, left for the other.

GLM OCRwon 2 · lost 0

Qwen3.5 0.8Bwon 2 · lost 0

WeVisDoc 4Bwon 2 · lost 0

Qwen3.5 2Bwon 2 · lost 0

Granite Vision 4.1 4Bwon 2 · lost 0

WeVisDoc 2Bwon 2 · lost 0

Qwen3.5 9Bwon 2 · lost 0

Nova 2 Litewon 2 · lost 0

MinerU Diffusion V1 2.5Bwon 2 · lost 0

Gemma 4 E4Bwon 2 · lost 0

DeepSeek OCR 2won 2 · lost 0

PaddleOCR VLwon 2 · lost 0

Qwen3.5 4Bwon 2 · lost 0

GPT-5.4 Nanowon 2 · lost 0

OvisOCR2won 2 · lost 0

MinerU2.5 1.2Bwon 2 · lost 0

Qianfan OCRwon 2 · lost 0

Docling Modelswon 2 · lost 0

Nemotron 3 Nano Omni 30B A3Bwon 2 · lost 0

Unlimited OCRwon 2 · lost 0

Jina OCR V1won 2 · lost 0

Qwen3.6 35B A3Bwon 2 · lost 0

Falcon OCRwon 2 · lost 0

LightOnOCR 2 1Bwon 2 · lost 0

HPD Parsingwon 2 · lost 0

GPT-5 Miniwon 2 · lost 0

Claude Haiku 4.5won 2 · lost 0

Gemini 3.1 Flash Litewon 1 · lost 1

Claude Opus 4.6won 1 · lost 1

Gemma 4 26B A4Bwon 0 · lost 2

Dots.mocrwon 0 · lost 2

Claude Haiku 5.5won 0 · lost 2

Gemini 3.5 Flash Litewon 0 · lost 2

Qwen3 VL 8B Instructwon 0 · lost 2

HunyuanOCR 1.5won 0 · lost 2

PaddleOCR VL 1.5won 0 · lost 2

GPT-5.4won 0 · lost 2

PaddleOCR VL 1.6won 0 · lost 2

Gemma 4 31Bwon 0 · lost 2

Claude Sonnet 5won 0 · lost 2

Surya OCR 2won 0 · lost 2

GPT-6 Lunawon 0 · lost 2

DeepSeek V4.1 Flashwon 0 · lost 2

Claude Opus 4.8won 0 · lost 2

Claude Opus 4.7won 0 · lost 2

Chandra Ocr 2won 0 · lost 2

GPT-5.6 Lunawon 0 · lost 2

GPT-5.5won 0 · lost 2

GPT-5.6 Terrawon 0 · lost 2

Qwen3.8 27Bwon 0 · lost 2

GPT-6 Solwon 0 · lost 2

KDL Frontier Parser Nanowon 0 · lost 2

LightOnOCR 3 1Bwon 0 · lost 2

Gemini 3.1 Prowon 0 · lost 2

GPT-6.1 Solwon 0 · lost 2

Gemini 3.6 Flashwon 0 · lost 2

MinerU2.5 Pro 1.2Bwon 0 · lost 2

GLM-5.3 Flashwon 0 · lost 2

GPT-6 Astrawon 0 · lost 2

Claude Sonnet 5.5won 0 · lost 2

Infinity Parser2 Flashwon 0 · lost 2

Gemini 3.7 Flashwon 0 · lost 2

Claude Fable 5won 0 · lost 2

Infinity Parser2 Prowon 0 · lost 2

Gemini 3.8 Flashwon 0 · lost 2

LightOnOCR 3 0.8Bwon 0 · lost 2

Gemini 3.5 Flashwon 0 · lost 2

LightOnOCR 3 4Bwon 0 · lost 2

Florin Parser Nanowon 0 · lost 2

aeddix-alpine-ocr (KDL pipeline)won 0 · lost 2

GPT-5.6 Solwon 0 · lost 2

Gemini 3 Flashwon 0 · lost 2

Rakedoc Nanowon 0 · lost 2

Claude Fable 5.1won 0 · lost 2

Claude Opus 5.5won 0 · lost 2

§ 3 · Sources

Where the numbers come from

1 publication, 6 figures. Every one links to the page it was read from.

ParseBench ↗6 measuresread 2026-10-19

ParseBench · Overall 58.67%ParseBench · Tables 72.74%ParseBench · Charts 9.97%ParseBench · Content faithfulness 85.39%ParseBench · Semantic formatting 45.13%ParseBench · Visual grounding 80.14%

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