OCR 4.1 vs GPT-5.2

At a Glance

Compare
OCR 4.1Mistral AI
GPT-5.2OpenAI
Intelligence, Cost, and Efficiency
IntelligenceHigher is better · MM Intelligence v2.5UnrankedNot in the 46-model eligible cohort#33 of 4649.3 score · 3/3 sources · complete
CostLower is better · Published-token output estimateUnrankedNot in the 44-model eligible cohort#28 of 44$0.161 per LiveBench case
EfficiencyHigher is better · MM Efficiency v1.5UnrankedNot in the 38-model eligible cohort#34 of 3844.0 score · 3/3 sources · complete
Pricing and Limits
Input priceFrom · USD / 1M tokensNot reported$1.75Openai · Sep 3, 2026
Output priceFrom · USD / 1M tokensNot reported$14.00Openai · Sep 3, 2026
Context windowMaximum documented tokensNot reported400K
Model facts checkedAug 29, 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

FieldOCR 4.1GPT-5.2
DeveloperMistral AIOpenAI
FamilyMistral OCRGpt 5 2
ModelOCR 4.1GPT-5.2
VersionOCR 4.1GPT-5.2
Lifecycleactiveactive
Released2026-07-162025-12-11
Knowledge cutoffUnknown2025-08-31
Input modalitiesImage, DocumentText, Image
Output modalitiesTextText
Context windowUnknown400K
Total parametersUnknownUnknown
Active parametersUnknownUnknown
LicenseUnknownUnknown
Open weightsNoNo
API availableYesYes
Self-hostableNoNo
Provider accessMistral AI (Standard)Openai (Standard), Openrouter (Standard)
Capabilitiesbounding-box-extraction, document-ai, ocr, structured-annotationschat, generation, reasoning, structured_outputs, tools

OCR 4.1 Capabilities

bounding-box-extractiondocument-aiocrstructured-annotations
Serving providers1
Canonical IDmistralai/mistral-ocr-4-1

GPT-5.2 Capabilities

chatgenerationreasoningstructured outputstools
Serving providers2
Canonical IDopenai/gpt-5.2

Primary Evidence

Sources and Freshness

Questions

OCR 4.1 vs GPT-5.2 FAQs

Is OCR 4.1 or GPT-5.2 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both OCR 4.1 and GPT-5.2, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, OCR 4.1 or GPT-5.2?+

Only GPT-5.2 has a directly sourced input price: $1.75 per million tokens. Only GPT-5.2 has a directly sourced output price: $14.00 per million tokens.

Which has a larger context window, OCR 4.1 or GPT-5.2?+

Neither model has a larger sourced context window in this comparison. OCR 4.1 is — and GPT-5.2 is 400K.

Which performs better in benchmarks, OCR 4.1 or GPT-5.2?+

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

Can OCR 4.1 or GPT-5.2 be self-hosted?+

Both models have the same recorded self-hosting status: unsupported. OCR 4.1 is not marked open weight; GPT-5.2 is not marked open weight.

Can OCR 4.1 and GPT-5.2 understand images?+

OCR 4.1 is documented with image input; GPT-5.2 is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, OCR 4.1 or GPT-5.2?+

Neither has a larger sourced maximum output. OCR 4.1 is — and GPT-5.2 is 128K.

Do OCR 4.1 and GPT-5.2 support reasoning and tool use?+

OCR 4.1: image input. GPT-5.2: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, OCR 4.1 or GPT-5.2?+

OCR 4.1 has 1 sourced provider route; GPT-5.2 has 2, so GPT-5.2 has broader tracked availability.

Which offers better value, OCR 4.1 or GPT-5.2?+

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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