Sold by: Bineric AI
Deployed on AWS
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A European decision model that works like TypeSafe's Jev and runs in your own AWS account. Give it a document and your questions; it picks an answer from your options and says how sure it is. 115 languages, 27 European ones trained. Your documents never leave your VPC.
Overview
Zuse is Bineric's decision model for European companies. Give it a document, such as an email, a ticket, a claim or a contract clause, and the questions you want answered. For each question it picks from the options you set and returns how sure it is, so you can act automatically on the confident answers and send the rest to a person. It is built for routing, triage, compliance checks and filling fixed fields, where a chat model is slower, costlier and harder to audit. It cannot answer outside the options you give it.
Zuse uses the same request format as TypeSafe's Jev, so teams that know Jev can start straight away, with the model running inside their own AWS account instead of a hosted API. Requests in the OpenAI Decisions format are accepted too.
Zuse 27B is the most accurate model in the family. It is built on Qwen3.8-27B and trained by Bineric on permissively licensed public data and data we generated and translated ourselves. No customer data was used, and no proprietary model's outputs. Bineric AS, Oslo, Norway.
Languages: 115 languages score 85% or more on reading and topic tests (Belebele and SIB-200, 210 languages tested).
Trained on 27 European languages: Bulgarian, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, German, Greek, Hungarian, Icelandic, Italian, Latvian, Lithuanian, Maltese, Norwegian Bokmal, Norwegian Nynorsk, Polish, Portuguese, Romanian, Slovak, Slovenian, Spanish, Swedish, Ukrainian.
22 more European languages: Albanian, Asturian, Basque, Belarusian, Bosnian, Catalan, Faroese, Galician, Irish, Ligurian, Limburgish, Lombard, Luxembourgish, Macedonian, Occitan, Russian, Sardinian, Scottish Gaelic, Serbian, Sicilian, Venetian, Welsh.
66 languages beyond Europe: Afrikaans, Arabic, Armenian, Assamese, Awadhi, Azerbaijani, Banjar, Bengali, Bhojpuri, Cantonese, Cebuano, Chhattisgarhi, Chinese (Simplified), Chinese (Traditional), Crimean Tatar, Dari, Egyptian Arabic, Esperanto, Georgian, Hebrew, Hindi, Indonesian, Japanese, Javanese, Kannada, Kashmiri, Kazakh, Khmer, Korean, Kyrgyz, Levantine Arabic, Magahi, Malay, Malayalam, Marathi, Mesopotamian Arabic, Minangkabau, Najdi Arabic, Nepali, Odia, Papiamento, Persian, Punjabi, Samoan, Sanskrit, Sinhala, South Azerbaijani, South Levantine Arabic, Sundanese, Swahili, Ta'izzi-Adeni Arabic, Tagalog, Tajik, Tamil, Tatar, Telugu, Thai, Tok Pisin, Tunisian Arabic, Turkish, Turkmen, Urdu, Uyghur, Uzbek, Vietnamese, Yiddish.
Highlights
- One pass answers many questions about a document: yes/no, a choice among up to 255 options, or a position on a scale. Each answer has a calibrated probability, so you automate the confident cases and route the rest to people.
- 115 languages at 85% or more, with 27 European languages trained on. 87.4% accuracy with these 4-bit weights on 20,044 questions from 11 task families it never trained on. Documents up to 64k tokens (measured to 32k).
- Runs inside your AWS account on one 24 GB GPU (ml.g5.2xlarge) with network isolation, so your documents never leave your VPC. About 0.2-0.5 s per request. Same request format as TypeSafe's Jev.
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Pricing
Free trial
Try this product free for 7 days according to the free trial terms set by the vendor.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g5.2xlarge Inference (Real-Time) Recommended | Model inference on the ml.g5.2xlarge instance type, real-time mode | $3.50 |
ml.g5.2xlarge Inference (Batch) Recommended | Model inference on the ml.g5.2xlarge instance type, batch mode | $3.50 |
ml.g5.4xlarge Inference (Real-Time) | Model inference on the ml.g5.4xlarge instance type, real-time mode | $3.50 |
ml.g6.2xlarge Inference (Real-Time) | Model inference on the ml.g6.2xlarge instance type, real-time mode | $3.50 |
ml.g6.4xlarge Inference (Real-Time) | Model inference on the ml.g6.4xlarge instance type, real-time mode | $3.50 |
ml.g6e.xlarge Inference (Real-Time) | Model inference on the ml.g6e.xlarge instance type, real-time mode | $5.50 |
ml.g6e.2xlarge Inference (Real-Time) | Model inference on the ml.g6e.2xlarge instance type, real-time mode | $5.50 |
ml.g6.2xlarge Inference (Batch) | Model inference on the ml.g6.2xlarge instance type, batch mode | $3.50 |
ml.g6e.xlarge Inference (Batch) | Model inference on the ml.g6e.xlarge instance type, batch mode | $5.50 |
Vendor refund policy
No refunds on hourly usage. For billing questions or problems, email support@bineric.com and we will answer within one business day (CET).
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
First release: Zuse 27B, 4-bit weights, one 24 or 48 GB GPU. Network isolated.
Additional details
Inputs
- Summary
JSON: a document ('state', text up to 64k tokens) and named typed questions ('noul', 'choice', 'score'). Batch transform: JSON Lines, one request per line.
- Limitations for input type
- Text only, 27 European languages; up to 64k tokens per document; up to 255 options per choice question; 2-10 levels per score question.
- Input MIME type
- application/json, application/jsonlines
{"state": "Hei, jeg vil avslutte kredittkortet mitt og flytte boliglånet til en annen bank. Kan dere sende meg innfrielsesbeløpet?", "questions": {"intent": {"type": "choice", "instructions": "What does the customer want?", "criteria": {"a": "Close the customer relationship or move the loan", "b": "A question about a card transaction", "c": "Something else"}}, "asks_for_document": {"type": "noul", "instructions": "Does the customer ask the bank to send them something?"}}}
https://raw.githubusercontent.com/binericai/zuse-aws-marketplace/main/samples/input.jsonl
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
state | The document to decide on (string or JSON). | - | Yes |
questions | Map of question name to {type, instructions, criteria}. | - | Yes |
Resources
Vendor resources
Support
Vendor support
Email support@bineric.com . We answer within one business day (CET). Usage guide, sample requests and a notebook:
AWS infrastructure support
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