Our Large Language Model as a Service (LLMaaS) offering gives you access to cutting-edge language models, inferred using SecNumCloud-qualified infrastructure, HDS-certified for healthcare data hosting, and therefore sovereign, calculated in France. Benefit from high performance and optimal security for your AI applications. Your data remains strictly confidential, and is neither exploited nor stored after processing.
Simple, transparent pricing
1,8
€
per million input tokens
8
€
per million tokens issued
8
€
per million reasoning tokens
4
€
per million reranking tokens
0,9
€
per million batch tokens received
4
€
per million batch tokens output
0,01
€
per minute of transcribed audio *
Calculated on an infrastructure based in France, SecNumcloud qualified and HDS certified.
Note on the "Reasoning" price:
This price applies specifically to models classified as "reasoners" or "hybrids" (models with the "Reasoning" capability activated) when reasoning is active and only on tokens linked to this activity.
* any minute started is counted
Chat & Reasoning
Our large models offer state-of-the-art performance for the most demanding tasks. They are particularly well-suited to applications requiring a deep understanding of language, complex reasoning or the processing of long documents.
80 tokens/second
qwen3.6:27b
Generalist reference model with a native context of 1M tokens. Excels at reasoning, following instructions and multilingualism.
Significant improvements in following instructions, reasoning, reading comprehension, mathematics, coding and tool use. Its context of 1M tokens enables the analysis of entire documents without truncation.
94 tokens/second
gpt-oss:120b
OpenAI's state-of-the-art open-weight model with configurable reasoning and transparent chain of thought.
Mixture-of-Experts model with 120 billion parameters offering configurable reasoning and full access to the chain of thought. Ideal for scenarios requiring a permissive licence (Apache 2.0).
10 tokens/second
llama3.3:70b
Multilingual Meta model, excellent in natural dialogue and nuanced understanding in 8 languages.
Supports English, French, German, Spanish, Italian, Portuguese, Hindi and Thai. Its 132k tokens window enables analysis of complex documents and long conversations.
72 tokens/second
nemotron-3-super:120b
NVIDIA model optimised for collaborative agents, long reasoning and high-volume workloads. 1M tokens context.
Ideal for agentic workflows, long-context reasoning, high-volume automation (support tickets, mass analyses), the use of tools and RAG.
56 tokens/second
qwen3-2507:235b
The most powerful model in the catalogue (235B parameters, 22B active). Excels in maths, coding and logical reasoning.
Ultra-sparse Mixture-of-Experts architecture combining the power of a very large model with the efficiency of a smaller model.
100 tokens/second
mistral-small4:119b
High-performance Mistral model (119B) with vision, integrated security and context of 262K tokens. Fast (100 t/s).
Large version of the Mistral Small family. Combines power, speed and reliability with an extended context. Native security filters.
55 tokens/second
qwen3-2507-think:4b
Compact model optimised for deep reasoning (logic, maths, science, code). Context of 250K tokens.
Thinking" version with enhanced reasoning capability. Combines compactness, speed and advanced reasoning.
Programming & Agents
Our programming and agent models are specially optimised for agentic software engineering, large-scale code generation and development workflow automation.
121 tokens/second
qwen3.6:35b
Leader in agentic software engineering (SWE-bench 73.4%). Context of 1M tokens, integrated vision and tool calling.
Includes entire code repositories thanks to its 1M token context. Supports multi-step reasoning and vision (screenshots, diagrams). Optimised for IDEs and CI/CD pipelines.
97 tokens/second
qwen-coder-next:80b
State-of-the-art model for complex code and reasoning. Context of 250K tokens.
Excels at large-scale code generation and analysis. Designed for advanced software engineering tasks.
67 tokens/second
qwen3-next:80b
Versatile 80B model optimised for large contexts, function calling and structured reasoning.
Context of 250K tokens with support for function calling and guided decoding.
33 tokens/second
devstral-small-2:24b
State-of-the-art agentic model for software engineering. Performance close to >100B models for code. Integrated vision.
Optimised for codebase exploration, multi-file editing and the use of tools. Native vision support. Context of 200K tokens.
40 tokens/second
functiongemma:270m
Micro-model specialising in function call detection. Ideal as a router in an agentic architecture.
Ultra-compact, optimised for identifying and formatting function calls quickly.
Vision & Multimodal
Our Vision & Multimodal models can analyse images, videos and visual documents. They excel in OCR, object detection, structured extraction and spatio-temporal reasoning.
24 tokens/second
qwen3-vl:235b
The most powerful multimodal model in the catalogue. Advanced visual understanding and exceptional reasoning.
Excels in complex document analysis, multilingual OCR, 3D spatial reasoning and video understanding.
39 tokens/second
qwen3-vl:30b
High-performance multimodal model for OCR, object detection, video analysis and spatio-temporal reasoning.
Incorporates innovations in image and video analysis. Excels in complex OCR, graphics and structured extraction (JSON).
57 tokens/second
qwen3-vl:4b
Compact, fast vision model for document analysis and video comprehension.
Excellent compromise between performance and footprint. Supports structured extraction and visual reasoning.
59 tokens/second
gemma4:31b
Google's dense multimodal model, ranked 3rd in the world on Arena AI. Advanced vision, reasoning and coding. Context 250K tokens.
Google's most powerful open-source model. Native function calling, advanced visual understanding (OCR, graphics, documents, UI). Multilingual (35+ languages).
42 tokens/second
gemma4:12b-it-qat
An intermediate multimodal model from Google, with integrated vision, reasoning and native function calling. Very large context of 250K tokens.
Variant 12B of the Gemma 4 family, offering a good balance between multimodal capabilities and footprint. Advanced reasoning, visual understanding (OCR, graphics, documents, UI) and multilingual support (35+ languages).
Embedding
Our embedding models transform text into vector representations for semantic search, clustering and RAG (Retrieval-Augmented Generation) pipelines.
171 tokens/second
bge-m3:567m
State-of-the-art multilingual embedding (100+ languages). Supports dense, sparse and multi-vector searches.
Context of 8192 tokens with three complementary search methods.
qwen3-embedding:4b
High-performance embedding with deep semantic understanding and extended context (40K tokens).
Ideal for processing large documents in RAG pipelines.
qwen3-embedding:8b
High-capacity embedding with the best semantic understanding of the Qwen3 family. Extended context (40K tokens).
The most powerful version of the Qwen3 embedding family. Ideal for tasks requiring contextual understanding.
qwen3-embedding:0.6b
Ultra-light and fast embedding for low-latency semantic search.
Excellent compromise between semantic performance and speed of execution.
196.3 tokens/second
granite-embedding:278m
Ultra-compact IBM embedding for semantic search with minimal latency.
The fastest embedding model in the catalogue. Ideal for clustering and high-frequency searching.
175 tokens/second
embeddinggemma:300m
Multilingual Google embedding (100+ languages), optimised for search and semantic retrieval.
Produces vector representations of text for classification, clustering and similarity search.
Reranking
Our reranking models reorder search results by relevance to refine the quality of RAG pipelines. Compatible with the Cohere API.
nvidia/llama-nemotron-rerank-vl-1b-v2
Cohere API-compatible reranking model (/v1/rerank and /v2/rerank). Orders documents by relevance to a query.
Cohere v1/v2 SDK compatible. The relevance score is a raw logit (relative order is guaranteed). Ideal as a complement to the RAG stack (embedding + retrieval + rerank).
qwen3-reranker:4b
Powerful reranking model with a high level of contextual understanding.
Excellent rescheduling quality thanks to its 4B parameters. Ideal for demanding RAG pipelines.
qwen3-reranker:0.6b
Compact and efficient reranking model for rapid rescheduling.
Lightweight version for use cases requiring low reranking latency.
bge-reranker-large
High-performance multilingual reranking model from the BGE family.
Complementary to the BGE-M3 embedding model for complete RAG pipelines.
Security
Our security models specialise in detecting problematic content, preventing jailbreaks and ensuring regulatory compliance (RGPD, HDS). They can be used as pre-filters or post-filters in your workflows.
45 tokens/second
granite3-guardian:8b
Granite Guardian 4.1 (v3 upgrade) — detection of problematic content, jailbreak, BYOC and hybrid thinking.
Version 4.1 (April 2026). Designed to filter sensitive content and ensure GDPR/HDS compliance. Can be used as a pre-filter or post-filter in your workflows. Hybrid thinking (reasoning) enabled.
60 tokens/second
granite3-guardian:2b
Granite Guardian 4.1 compact (upgrade v3:2b) — also known as version 8B with hybrid thinking.
Same filtering capabilities as the 8B version, but with a smaller footprint. Ideal for high-frequency workflows. Hybrid thinking (reasoning) enabled.
Translation
Our translation models offer high fidelity in 55 languages, respecting the grammar, cultural nuances and technical specificities of the documents.
17 tokens/second
translategemma:27b
High-performance translation for 55 languages. Superior quality for complex and technical content.
Captures literary and cultural nuances with exceptional fidelity.
Audio & Image
Our Audio & Image models enable real-time voice transcription (ASR streaming) and image generation from text descriptions, compatible with the OpenAI API.
voxtral
Real-time audio transcription via WebSocket. Low-latency streaming speech recognition.
Operates in Realtime mode via the /v1/realtime endpoint (WebSocket). Transcribes streaming audio.
z-image:16b
Image generation from text prompts, OpenAI API compatible /v1/images/generations.
Supports image size and number of images. Compatible with the OpenAI ecosystem.
Model comparison
This comparison table will help you choose the model best suited to your needs, based on various criteria such as context size, performance and specific use cases.
| Model | Publisher | Parameters | Context (tokens) | Vision | Agent | Reasoning | Security | Quick * | Energy efficiency * |
|---|---|---|---|---|---|---|---|---|---|
| Chat & Reasoning | |||||||||
| qwen3.6:27b | Qwen Team | 27B | 1 000 000 | ||||||
| gpt-oss:120b | OpenAI | 120B | 120 000 | ||||||
| llama3.3:70b | Meta | 70B | 132 000 | ||||||
| nemotron-3-super:120b | NVIDIA | 120B | 1 000 000 | ||||||
| qwen3-2507:235b | Qwen Team | 235B | 200 000 | ||||||
| mistral-small4:119b | Mistral AI | 119B | 262 144 | ||||||
| qwen3-2507-think:4b | Qwen Team | 4B | 250 000 | ||||||
| Programming & Agents | |||||||||
| qwen3.6:35b | Qwen Team | 35B | 1 000 000 | ||||||
| qwen-coder-next:80b | Qwen Team | 80B | 250 000 | ||||||
| qwen3-next:80b | Qwen Team | 80B | 250 000 | ||||||
| devstral-small-2:24b | Mistral AI & All Hands AI | 24B | 200 000 | ||||||
| functiongemma:270m | 270M | 32 768 | |||||||
| Vision & Multimodal | |||||||||
| qwen3-vl:235b | Qwen Team | 235B | 200 000 | ||||||
| qwen3-vl:30b | Qwen Team | 30B | 250 000 | ||||||
| qwen3-vl:4b | Qwen Team | 4B | 250 000 | ||||||
| gemma4:31b | 31B | 250 000 | |||||||
| gemma4:12b-it-qat | 12B | 250 000 | |||||||
| Embedding | |||||||||
| bge-m3:567m | BAAI | 567M | 8 192 | ||||||
| qwen3-embedding:4b | Qwen Team | 4B | 40 000 | ||||||
| qwen3-embedding:8b | Qwen Team | 8B | 40 000 | ||||||
| qwen3-embedding:0.6b | Qwen Team | 0.6B | 32 768 | ||||||
| granite-embedding:278m | IBM | 278M | 512 | ||||||
| embeddinggemma:300m | 300M | 2 048 | |||||||
| Reranking | |||||||||
| nvidia/llama-nemotron-rerank-vl-1b-v2 | NVIDIA | 1B | 4 096 | N.C. | |||||
| qwen3-reranker:4b | Qwen Team | 4B | 4 096 | N.C. | |||||
| qwen3-reranker:0.6b | Qwen Team | 0.6B | 4 096 | N.C. | |||||
| bge-reranker-large | BAAI | 335M | 512 | N.C. | |||||
| Security | |||||||||
| granite3-guardian:8b | IBM | 8B | 8 192 | ||||||
| granite3-guardian:2b | IBM | 2B | 8 192 | ||||||
| Translation | |||||||||
| translategemma:27b | 27B | 120 000 | |||||||
| Audio & Image | |||||||||
| voxtral | Mistral AI | 4B | 32 768 | N.C. | |||||
| z-image:16b | Community | 16B | N.C. | N.C. | |||||
Legend and explanation
Functionality or capacity supported by the model
Functionality or capability not supported by the model
* Energy efficiency
Indicates particularly low energy consumption (< 2.0 kWh/Mtoken)
* Quick
Model capable of generating more than 50 tokens per second
Note on performance measures
The speed values (tokens/s) represent performance targets in real-life conditions. Energy consumption (kWh/Mtoken) is calculated by dividing the estimated power of the inference server (in Watts) by the measured speed of the model (in tokens/second), then converted into kilowatt-hours per million tokens (division by 3.6). This method offers a practical comparison of the energy efficiency of different models, to be used as a relative indicator rather than an absolute measure of power consumption.
Recommended use cases
Here are some common use cases and the most suitable models for each. These recommendations are based on the specific performance and capabilities of each model.
Multilingual dialogue
Chatbots and assistants able to communicate in several languages with automatic detection and context maintenance
Recommended models
- nemotron-3-super:120b
- qwen3.6:27b
- gpt-oss:120b
Analysis of long documents
Processing of large documents (>100 pages) with extraction of key information, summaries and answers to questions
Recommended models
- nemotron-3-super:120b
- qwen3.6:27b
- qwen3-2507:235b
Programming and development
Code generation, optimisation and debugging in multiple languages, refactoring and test creation
Recommended models
- qwen3.6:35b
- qwen-coder-next:80b
- devstral-small-2:24b
- nemotron-3-super:120b
Visual analysis
Image and visual document processing, OCR, interpretation of graphs and tables
Recommended models
- qwen3-vl:235b
- gemma4:31b
- qwen3-vl:30b
Safety and compliance
Sensitive content filtering, jailbreak detection, RGPD/HDS compliance
Recommended models
- granite4.1-guardian:8b
- granite3-guardian:8b
- granite3-guardian:2b
- mistral-small4:119b
Light deployments
Applications requiring a minimal footprint, low latency and low power consumption
RAG (Retrieval-Augmented Generation)
Complete semantic search, reordering and retrieval-enhanced generation pipelines
Recommended models
- bge-m3:567m
- nvidia/llama-nemotron-rerank-vl-1b-v2
- qwen3.6:27b