PaddleOCR VL 1.5 vs Gemini Deep Research

This comparison does not currently contain a protocol-matched coding benchmark for both PaddleOCR VL 1.5 and Gemini Deep Research, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Neither model has a directly sourced input price in this comparison. Neither model has a directly sourced output price in this comparison.

Gemini Deep Research has the larger sourced context window. PaddleOCR VL 1.5 supports 131K and Gemini Deep Research supports 1,049K.

There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.

PaddleOCR VL 1.5 is the only model in this pair currently marked as self-hostable. PaddleOCR VL 1.5 is open weight; Gemini Deep Research is not marked open weight.

PaddleOCR VL 1.5 is documented with image input; Gemini Deep Research is documented with image input. This reflects supported input modalities, not vision quality.

Neither has a larger sourced maximum output. PaddleOCR VL 1.5 is — and Gemini Deep Research is 66K.

PaddleOCR VL 1.5: image input. Gemini Deep Research: reasoning, tool calling, and image input. Feature support does not establish relative quality.

PaddleOCR VL 1.5 has 0 sourced provider routes; Gemini Deep Research has 2, so Gemini Deep Research has broader tracked availability.

There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.