PaddleOCR VL 1.5 vs GPT-5.5

At a Glance

Compare
GPT-5.5OpenAI
Intelligence, Cost, and Efficiency
IntelligenceHigher is better · MM Intelligence v2.5UnrankedNot in the 46-model eligible cohort#7 of 4683.9 score · 3/3 sources · complete
CostLower is better · Published-token output estimateUnrankedNot in the 44-model eligible cohort#38 of 44$0.341 per LiveBench case
EfficiencyHigher is better · MM Efficiency v1.5UnrankedNot in the 38-model eligible cohort#18 of 3853.4 score · 3/3 sources · complete
Pricing and Limits
Input priceFrom · USD / 1M tokensNot reported$5.00Openai · Sep 3, 2026
Output priceFrom · USD / 1M tokensNot reported$30.00Openai · Sep 3, 2026
Context windowMaximum documented tokens131K1,050K
Model facts checkedAug 28, 2026View model evidence →Sep 3, 2026View model evidence →

Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →

Available Benchmarks

All benchmark results →
No Protocol-Matched Benchmark Yet.Results appear here only when both models share the same benchmark version, metric, evaluation protocol, and evidence class.

Side-by-Side Facts

FieldPaddleOCR-VL-1.5GPT-5.5
DeveloperBaiduOpenAI
FamilyPaddleocr VL 1 5Gpt 5 5
ModelPaddleOCR-VL-1.5GPT-5.5
VersionPaddleOCR-VL-1.5GPT-5.5
Lifecycleactiveactive
Released2026-01-292026-04-23
Knowledge cutoffUnknown2025-12-01
Input modalitiesText, ImageText, Image
Output modalitiesTextText
Context window131K1,050K
Total parameters958.6MUnknown
Active parametersUnknownUnknown
Licenseapache-2.0Unknown
Open weightsYesNo
API availableUnknownYes
Self-hostableYesNo
Provider accessUnknownOpenai (Standard), Openrouter (Standard)
Capabilitieschat, generationchat, generation, reasoning, structured_outputs, tools

PaddleOCR VL 1.5 Capabilities

chatgeneration
Serving providers0
Canonical IDPaddlePaddle/PaddleOCR-VL-1.5

GPT-5.5 Capabilities

chatgenerationreasoningstructured outputstools
Serving providers2
Canonical IDopenai/gpt-5.5

Primary Evidence

Sources and Freshness

Questions

PaddleOCR VL 1.5 vs GPT-5.5 FAQs

Is PaddleOCR VL 1.5 or GPT-5.5 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both PaddleOCR VL 1.5 and GPT-5.5, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, PaddleOCR VL 1.5 or GPT-5.5?+

Only GPT-5.5 has a directly sourced input price: $5.00 per million tokens. Only GPT-5.5 has a directly sourced output price: $30.00 per million tokens.

Which has a larger context window, PaddleOCR VL 1.5 or GPT-5.5?+

GPT-5.5 has the larger sourced context window. PaddleOCR VL 1.5 supports 131K and GPT-5.5 supports 1,050K.

Which performs better in benchmarks, PaddleOCR VL 1.5 or GPT-5.5?+

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

Can PaddleOCR VL 1.5 or GPT-5.5 be self-hosted?+

PaddleOCR VL 1.5 is the only model in this pair currently marked as self-hostable. PaddleOCR VL 1.5 is open weight; GPT-5.5 is not marked open weight.

Can PaddleOCR VL 1.5 and GPT-5.5 understand images?+

PaddleOCR VL 1.5 is documented with image input; GPT-5.5 is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, PaddleOCR VL 1.5 or GPT-5.5?+

Neither has a larger sourced maximum output. PaddleOCR VL 1.5 is — and GPT-5.5 is 128K.

Do PaddleOCR VL 1.5 and GPT-5.5 support reasoning and tool use?+

PaddleOCR VL 1.5: image input. GPT-5.5: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, PaddleOCR VL 1.5 or GPT-5.5?+

PaddleOCR VL 1.5 has 0 sourced provider routes; GPT-5.5 has 2, so GPT-5.5 has broader tracked availability.

Which offers better value, PaddleOCR VL 1.5 or GPT-5.5?+

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.

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