Granite Embedding 278m Multilingual vs OCR 4.1
At a Glance
| Compare |  OCR 4.1Mistral AI | |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.106IBM watsonx.ai ↗ · Aug 29, 2026 | Not reported |
| Context windowMaximum documented tokens | 1K | Not reported |
| Model facts checked | Aug 28, 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
Side-by-Side Facts
| Field | granite-embedding-278m-multilingual | OCR 4.1 |
|---|---|---|
| Developer | IBM | Mistral AI |
| Family | Granite Embedding 278m Multilingual | Mistral OCR |
| Model | granite-embedding-278m-multilingual | OCR 4.1 |
| Version | granite-embedding-278m-multilingual | OCR 4.1 |
| Lifecycle | active | active |
| Released | 2024-12-18 | 2026-07-16 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Image, Document |
| Output modalities | Embedding | Text |
| Context window | 1K | Unknown |
| Total parameters | 278M | Unknown |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | Hugging Face (Standard), IBM watsonx.ai (Pay as you go) | Mistral AI (Standard) |
| Capabilities | embeddings | bounding-box-extraction, document-ai, ocr, structured-annotations |
Granite Embedding 278m Multilingual Capabilities
OCR 4.1 Capabilities
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Primary Evidence
Sources and Freshness
Questions
Granite Embedding 278m Multilingual vs OCR 4.1 FAQs
Is Granite Embedding 278m Multilingual or OCR 4.1 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Granite Embedding 278m Multilingual and OCR 4.1, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Granite Embedding 278m Multilingual or OCR 4.1?+
Only Granite Embedding 278m Multilingual has a directly sourced input price: $0.106 per million tokens. Neither model has a directly sourced output price in this comparison.
Which has a larger context window, Granite Embedding 278m Multilingual or OCR 4.1?+
Neither model has a larger sourced context window in this comparison. Granite Embedding 278m Multilingual is 1K and OCR 4.1 is —.
Which performs better in benchmarks, Granite Embedding 278m Multilingual or OCR 4.1?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Granite Embedding 278m Multilingual or OCR 4.1 be self-hosted?+
Granite Embedding 278m Multilingual is the only model in this pair currently marked as self-hostable. Granite Embedding 278m Multilingual is open weight; OCR 4.1 is not marked open weight.
Can Granite Embedding 278m Multilingual and OCR 4.1 understand images?+
Granite Embedding 278m Multilingual is not documented with image input; OCR 4.1 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Granite Embedding 278m Multilingual or OCR 4.1?+
Neither has a larger sourced maximum output. Granite Embedding 278m Multilingual is — and OCR 4.1 is —.
Do Granite Embedding 278m Multilingual and OCR 4.1 support reasoning and tool use?+
Granite Embedding 278m Multilingual: none of these features are definitively sourced. OCR 4.1: image input. Feature support does not establish relative quality.
Which is available from more inference providers, Granite Embedding 278m Multilingual or OCR 4.1?+
Granite Embedding 278m Multilingual has 2 sourced provider routes; OCR 4.1 has 1, so Granite Embedding 278m Multilingual has broader tracked availability.
Which offers better value, Granite Embedding 278m Multilingual or OCR 4.1?+
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.