OCR 4.1 vs GLM 5.3

At a Glance

Compare
OCR 4.1Mistral AI
Intelligence, Cost, and Efficiency
IntelligenceHigher is better · MM Intelligence v2.5UnrankedNot in the 46-model eligible cohort#16 of 4670.4 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 46.9–80.3
CostLower is better · Published-token output estimateUnrankedNot in the 44-model eligible cohort#34 of 44$0.248 per LiveBench case
EfficiencyHigher is better · MM Efficiency v1.5UnrankedNot in the 38-model eligible cohort#26 of 3850.0 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 38.2–54.9
Pricing and Limits
Input priceFrom · USD / 1M tokensNot reported$1.20Deepinfra · Sep 22, 2026
Output priceFrom · USD / 1M tokensNot reported$4.00Deepinfra · Sep 22, 2026
Context windowMaximum documented tokensNot reported1,000K
Model facts checkedAug 29, 2026View model evidence →Aug 29, 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 4.1GLM-5.3
DeveloperMistral AIZ.ai
FamilyMistral OCRGlm 5 3
ModelOCR 4.1GLM-5.3
VersionOCR 4.1GLM-5.3
Lifecycleactiveactive
Released2026-07-16Unknown
Knowledge cutoffUnknownUnknown
Input modalitiesImage, DocumentText
Output modalitiesTextText
Context windowUnknown1,000K
Total parametersUnknownUnknown
Active parametersUnknownUnknown
LicenseUnknownUnknown
Open weightsNoNo
API availableYesYes
Self-hostableNoNo
Provider accessMistral AI (Standard)Deepinfra (Standard), Fireworks Ai (Standard), Together Ai (Standard), Z.ai (Standard)
Capabilitiesbounding-box-extraction, document-ai, ocr, structured-annotationsagents, chat, reasoning, structured_outputs, tools

OCR 4.1 Capabilities

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

GLM 5.3 Capabilities

agentschatreasoningstructured outputstools
Serving providers4
Canonical IDzai-org/glm-5.3

Primary Evidence

Sources and Freshness

Questions

OCR 4.1 vs GLM 5.3 FAQs

Is OCR 4.1 or GLM 5.3 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both OCR 4.1 and GLM 5.3, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, OCR 4.1 or GLM 5.3?+

Only GLM 5.3 has a directly sourced input price: $1.20 per million tokens. Only GLM 5.3 has a directly sourced output price: $4.00 per million tokens.

Which has a larger context window, OCR 4.1 or GLM 5.3?+

Neither model has a larger sourced context window in this comparison. OCR 4.1 is — and GLM 5.3 is 1,000K.

Which performs better in benchmarks, OCR 4.1 or GLM 5.3?+

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 GLM 5.3 be self-hosted?+

Both models have the same recorded self-hosting status: unsupported. OCR 4.1 is not marked open weight; GLM 5.3 is not marked open weight.

Can OCR 4.1 and GLM 5.3 understand images?+

OCR 4.1 is documented with image input; GLM 5.3 is not documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, OCR 4.1 or GLM 5.3?+

Neither has a larger sourced maximum output. OCR 4.1 is — and GLM 5.3 is 131K.

Do OCR 4.1 and GLM 5.3 support reasoning and tool use?+

OCR 4.1: image input. GLM 5.3: reasoning and tool calling. Feature support does not establish relative quality.

Which is available from more inference providers, OCR 4.1 or GLM 5.3?+

OCR 4.1 has 1 sourced provider route; GLM 5.3 has 4, so GLM 5.3 has broader tracked availability.

Which offers better value, OCR 4.1 or GLM 5.3?+

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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