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LLM as a Service Modellkatalog

Übersicht​

Cloud Temple LLMaaS bietet 46 sorgfältig ausgewählte und optimierte große Sprachmodelle, die den anspruchsvollsten Anforderungen von SecNumCloud entsprechen. Unser Katalog umfasst die gesamte Bandbreite – von ultraeffizienten Mikromodellen bis hin zu extrem umfangreichen Modellen.

Globale Statistiken​

MetrikWert
Gesamte Anzahl an Modellen46 Modelle
Minimale Kontextlänge2.048 Tokens
Maximale Kontextlänge262.144 Tokens
KonformitätSecNumCloud ✅ HDS ✅ Souveränität ✅ C5 ✅
Standort100 % Frankreich 🇫🇷

Pricing​

Usage TypePrice
Input Tokens1.9€ / million tokens
Output Tokens8€ / million tokens
Advanced Reasoning8€ / million tokens

Large Language Models​

cogito:32b​

Deep Cogito • 32B parameters • Context: 32,000 tokens

Advanced version of the Cogito model offering significantly enhanced reasoning and analytical capabilities, designed for the most demanding AI analytical applications.

Technical specifications:

Capabilities: ✅ Tools/Agent • ❌ Vision • ✅ Reasoning • ❌ Security

Tags: Agent Reasoning Comprehension Analysis

Use cases:

  • Multi-factorial scenario analysis with probabilistic evaluation of outcomes
  • Scientific problem solving with formal demonstration of steps
  • High-criticality applications requiring precision and verifiability of results
  • Expert systems in specialized domains (legal, medical, technical)
  • Multi-step reasoning analysis with full explainability of conclusions

gemma3:27b​

Google • 27B Parameters • Context: 120,000 tokens

Revolutionary model from Google offering an optimal balance between power and efficiency, with an exceptional performance-to-cost ratio for demanding professional applications.

Technical Specifications:

  • Speed: 21 tokens/second
  • Energy Consumption: 6.35 kWh per million tokens
  • License: Google Gemma Terms of Use
  • Location: FR 🇫🇷

Capabilities: ✅ Tools/Agent • ✅ Vision • ❌ Reasoning • ❌ Security

Tags: Vision Agent Large Context

Use Cases:

  • Document analysis with extended context up to 120K tokens (approximately 400 pages)
  • Semantic indexing and search in large document databases
  • Simultaneous processing of images and text thanks to multimodal capabilities
  • Structured data extraction from PDFs and scanned documents
  • Integration with external tools via function calling API

glm-4.7-flash:30b​

Zhipu AI • 30B parameters • Context: 120,000 tokens

Flash version of the GLM-4.7 model, optimized for speed and efficiency.

Technical specifications:

  • Speed: 103 tokens/second
  • Energy consumption: 1.41 kWh per million tokens
  • License: Apache 2.0
  • Location: FR 🇫🇷

Capabilities: ✅ Tools/Agent • ❌ Vision • ✅ Reasoning • ❌ Security

Tags: Agent Fast Large Context Multilingual

Use cases:

  • Fast conversational assistants
  • Long document analysis (up to 200k)
  • Reasoning tasks with low latency

glm-4.7:358b​

⚠️ WARNING: This model is deprecated. Model removed from catalog on 03/30/2026.

Zhipu AI • 358B parameters • Context: 120,000 tokens

High-performance general-purpose model developed by Zhipu AI, excelling in logical reasoning, multilingual understanding, and complex tasks.

Technical Specifications:

  • Speed : 18 tokens/second
  • Consumption : 7.41 kWh per million tokens
  • License : Apache 2.0
  • Location : FR 🇫🇷

Capabilities: ✅ Tools/Agent • ❌ Vision • ✅ Reasoning • ❌ Security

Tags: Agent Reasoning Large Context Multilingual

Use Cases:

  • Complex reasoning tasks
  • Long document analysis
  • Advanced conversational assistants

gpt-oss:120b​

OpenAI • 120B parameters • Context: 120,000 tokens

State-of-the-art open-weight language model from OpenAI, delivering strong performance with a flexible Apache 2.0 license.

Technical specifications:

  • Speed: 104 tokens/second
  • Energy consumption: 2.19 kWh per million tokens
  • License: Apache 2.0
  • Location: FR 🇫🇷

Capabilities: ✅ Tools/Agent • ❌ Vision • ✅ Reasoning • ❌ Security

Tags: MoE Agent Reasoning Open-Source Very Large

Use cases:

  • Advanced conversational agents with complex reasoning and tool integration.
  • Applications requiring full transparency in the reasoning process (chain-of-thought).
  • Commercial scenarios needing a permissive license (Apache 2.0).
  • Fine-tuning for specialized tasks requiring a powerful base model.

llama3.3:70b​

Meta • 70B parameters • Context: 132,000 tokens

State-of-the-art multilingual model developed by Meta, designed to excel in natural dialogue, complex reasoning, and nuanced instruction understanding.

Technical specifications:

Capabilities: ✅ Tools/Agent • ❌ Vision • ❌ Reasoning • ❌ Security

Tags: Agent Dialogue Multilingual

Use cases:

  • Multilingual chatbots supporting 8 languages simultaneously
  • Execution of complex, chained instructions (prompt chaining)
  • Processing of 60K-token dialogue windows for conversational history
  • Analysis of large legal or technical documents (>100 pages)
  • Generation of structured text with strict adherence to stylistic guidelines

ministral-3:14b​

Mistral AI • 14B parameters • Context: 250,000 tokens

The most powerful model in the Ministral family, designed for complex tasks on local infrastructure.

Technical specifications:

  • Speed: 31 tokens/second
  • Energy consumption: 4.30 kWh per million tokens
  • License: Apache 2.0
  • Location: FR 🇫🇷

Capabilities: ✅ Tools/Agent • ❌ Vision • ✅ Reasoning • ❌ Security

Tags: High Performance Edge Reasoning Code

Use cases:

  • Solving complex problems locally
  • Coding and engineering assistants
  • In-depth document analysis with reasoning

nemotron-3-nano:30b​

NVIDIA • 30B parameters • Context: 250,000 tokens

NVIDIA-optimized model for complex reasoning and tool utilization, deployed with an extended context.

Technical Specifications:

  • Speed: 89 tokens/second
  • Energy Consumption: 1.62 kWh per million tokens
  • License: NVIDIA Community License
  • Location: FR 🇫🇷

Capabilities: ✅ Tools/Agent • ❌ Vision • ✅ Reasoning • ❌ Security

Tags: Agent Reasoning Large Context

Use Cases:

  • Complex autonomous agents with multiple tool calls
  • Logical reasoning and problem solving
  • Long document analysis with precise extraction

olmo-3:32b​

AllenAI • 32B Parameters • Context: 65,536 tokens

The first fully open reasoning model at this scale, competing with the best proprietary models.

Technical Specifications:

  • Speed: 19 tokens/second
  • Energy Consumption: 7.02 kWh per million tokens
  • License: Apache 2.0
  • Location: FR 🇫🇷

Capabilities: ❌ Tools/Agent • ❌ Vision • ✅ Reasoning • ❌ Security

Tags: Open-Source Large Context Reasoning Transparent Code High Performance

Use Cases:

  • Complex reasoning and multi-step problem solving
  • Advanced software development and code generation
  • In-depth analysis requiring transparency in decision-making processes

olmo-3:7b​

AllenAI • 7B parameters • Context: 65,536 tokens

Reference "Fully Open" model, offering complete transparency (data, code, weights) and remarkable efficiency.

Technical specifications:

  • Speed : 37 tokens/second
  • Energy consumption : 1.65 kWh per million tokens
  • License : Apache 2.0
  • Location : FR 🇫🇷

Capabilities: ❌ Tools/Agent • ❌ Vision • ❌ Reasoning • ❌ Security

Tags: Open-Source Large Context Transparent Efficient Maths Code

Use cases:

  • Academic and scientific research requiring full reproducibility
  • Programming tasks and mathematical problem solving
  • Analysis of medium-sized documents with full traceability

qwen-coder-next:80b​

Qwen Team • 80B Parameters • Context: 250,000 tokens

State-of-the-art MoE model optimized for code and complex reasoning.

Technical Specifications:

  • Speed: 98 tokens/second
  • Energy Consumption: 1.47 kWh per million tokens
  • License: Apache 2.0
  • Location: FR 🇫🇷

Capabilities: ✅ Tools/Agent • ❌ Vision • ✅ Reasoning • ❌ Security

Tags: Agent Programming MoE Large Context AWQ

Use Cases:

  • Advanced code assistant (repo-scale)
  • Complex code analysis and refactoring
  • Autonomous software engineering agents

qwen3-2507:235b​

Qwen Team • 235B Parameters • Context: 130,000 tokens

Massive MoE model with 235 billion parameters, activating only 22 billion at a time, delivering state-of-the-art performance.

Technical Specifications:

  • Speed: 58 tokens/second
  • Energy Consumption: 3.93 kWh per million tokens
  • License: Apache 2.0
  • Location: FR 🇫🇷

Capabilities: ✅ Tools/Agent • ❌ Vision • ✅ Reasoning • ❌ Security

Tags: MoE Agent Reasoning Very Large

Use Cases:

  • Solving complex mathematical and logical problems
  • Tasks requiring extensive knowledge base
  • Advanced coding assistant
  • In-depth document analysis

qwen3-2507:30b-a3b​

Qwen Team • 30B Parameters • Context: 250,000 tokens

Enhanced version of the non-thinking mode of Qwen3-30B, featuring improved general capabilities, broader knowledge coverage, and better user alignment.

Technical Specifications:

  • Speed: 104 tokens/second
  • Energy Consumption: 1.39 kWh per million tokens
  • License: Apache 2.0
  • Location: FR 🇫🇷

Capabilities: ✅ Tools/Agent • ❌ Vision • ❌ Reasoning • ❌ Security

Tags: Agent Large Context MoE Multilingual

Use Cases:

  • Complex tasks requiring precise instruction following and logical reasoning.
  • Multilingual applications with extensive knowledge coverage.
  • High-quality text generation for open-ended and subjective tasks.
  • Analysis of very large documents thanks to the 250k-token context.

qwen3-coder:30b​

⚠️ WARNING: This model is deprecated. Recommendation to migrate to qwen-coder-next:80b.

Qwen Team • 30B parameters • Context: 250,000 tokens

MoE-optimized model for software engineering tasks, featuring an extremely long context.

Technical Specifications:

  • Speed: 104 tokens/second
  • Energy Consumption: 1.39 kWh per million tokens
  • License: Apache 2.0
  • Location: FR 🇫🇷

Capabilities: ✅ Tools/Agent • ❌ Vision • ✅ Reasoning • ❌ Security

Tags: Agent Programming Large Context MoE

Use Cases:

  • Software engineering agents for exploring and modifying codebases
  • Generation of complex code with repository-scale understanding
  • Reasoning tasks over extended contexts
  • Code improvement via reinforcement learning

qwen3-next:80b​

Qwen Team • 80B Parameters • Context: 250,000 tokens

Next 80B model from Qwen, optimized for large contexts and reasoning.

Technical Specifications:

  • Speed : 98 tokens/second
  • Consumption : 1.47 kWh per million tokens
  • License : Apache 2.0
  • Location : FR 🇫🇷

Capabilities: ✅ Tools/Agent • ❌ Vision • ❌ Reasoning • ❌ Security

Tags: Agent Reasoning Large Context MoE

Use Cases:

  • Advanced conversational agents with tool integration
  • Analysis of very large documents (up to 260k tokens)
  • Code generation and complex tasks requiring structured reasoning

qwen3-omni:30b​

Qwen Team • 30B Parameters • Context: 32,768 tokens

Qwen3-Omni 30B is a native multimodal model capable of understanding text, images, video, and audio within a single stream.

Technical Specifications:

  • Speed: 86 tokens/second
  • Energy Consumption: 2.65 kWh per million tokens
  • License: Apache 2.0
  • Location: FR 🇫🇷

Capabilities: ✅ Tools/Agent • ✅ Vision • ✅ Reasoning • ❌ Security

Tags: Omni Audio Vision Agent Multimodal BF16

Use Cases:

  • Seamless multimodal interactions (speaks, sees, listens)
  • Combined video and audio analysis
  • Next-generation intelligent assistants

qwen3-vl:235b​

Qwen Team • 235B Parameters • Context: 200,000 tokens

The most powerful multimodal model in the catalog, combining state-of-the-art visual understanding with exceptional reasoning capabilities.

Technical Specifications:

  • Speed: 31 tokens/second
  • Energy Consumption: 7.35 kWh per million tokens
  • License: Apache 2.0
  • Location: FR 🇫🇷

Capabilities: ✅ Tools/Agent • ✅ Vision • ✅ Reasoning • ❌ Security

Tags: Agent Reasoning Large Context NVFP4 Blackwell Vision

Use Cases:

  • Automation of complex document workflows (multilingual OCR, structured extraction)
  • Intelligent visual agents for software interaction and GUI automation
  • Advanced scientific and technical analysis (STEM, 3D spatial reasoning)
  • Multimodal RAG on large documents (>200k tokens) and videos

qwen3-vl:30b​

Qwen Team • 30B Parameters • Context: 250,000 tokens

State-of-the-art multimodal model (Qwen3-VL) offering exceptional visual understanding and precise temporal reasoning.

Technical Specifications:

  • Speed: 43 tokens/second
  • Energy Consumption: 3.10 kWh per million tokens
  • License: Apache 2.0
  • Location: FR 🇫🇷

Capabilities: ✅ Tools/Agent • ✅ Vision • ❌ Reasoning • ❌ Security

Tags: Vision Agent Large Context Multimodal Video OCR

Use Cases:

  • Deep analysis of long videos and intelligent surveillance
  • Extraction of complex structured data (documents, tables, charts)
  • Advanced visual assistants with spatial understanding
  • Multimodal reasoning over sequences of events

qwen3-vl:32b​

Qwen Team • 32B parameters • Context: 250,000 tokens

High-performance variant of Qwen3-VL, optimized for the most demanding vision tasks.

Technical Specifications:

  • Speed: 17 tokens/second
  • Energy Consumption: 7.84 kWh per million tokens
  • License: Apache 2.0
  • Location: FR 🇫🇷

Capabilities: ✅ Tools/Agent • ✅ Vision • ❌ Reasoning • ❌ Security

Tags: Vision Agent Large Context Multimodal Video OCR

Use Cases:

  • Scientific and technical analysis of high-resolution images
  • Automation of complex visual processes
  • Detailed understanding of dynamic scenes

qwen3:14b​

Qwen Team • 14B parameters • Context: 131,072 tokens

Balanced Qwen3 14B model, delivering strong general performance with good inference speed.

Technical Specifications:

  • Speed: 68.2 tokens/second
  • Energy Consumption: 0.90 kWh per million tokens
  • License: Apache 2.0
  • Location: FR 🇫🇷

Capabilities: ✅ Tools/Agent • ❌ Vision • ❌ Reasoning • ❌ Security

Tags: Agent Versatile Multilingual

Use Cases:

  • High-performance virtual assistants
  • High-quality content generation
  • Classification and extraction tasks

Specialized Models​

bge-m3:567m​

BAAI • 567M parameters • Context: 8,192 tokens

State-of-the-art multilingual embedding model (BGE-M3), delivering exceptional semantic search capabilities across more than 100 languages.

Technical Specifications:

  • Speed: 171 tokens/second
  • Energy Consumption: 0.36 kWh per million tokens
  • License: MIT
  • Location: FR 🇫🇷

Capabilities: ❌ Tools/Agent • ❌ Vision • ❌ Reasoning • ❌ Security

Tags: Embedding Multilingual Efficient

Use Cases:

  • Multilingual semantic search
  • Retrieval-Augmented Generation (RAG)
  • Document clustering and classification

deepseek-ocr​

DeepSeek AI • 3B parameters • Context: 8,192 tokens

Specialized OCR model from DeepSeek, designed for high-precision text extraction with formatting preservation.

Technical specifications:

  • Speed: 79 tokens/second
  • Consumption: 1.01 kWh per million tokens
  • License: MIT License
  • Location: FR 🇫🇷

Capabilities: ❌ Tools/Agent • ✅ Vision • ❌ Reasoning • ❌ Security

Tags: Vision OCR Efficient

Use cases:

  • Extraction of structured text (Markdown/LaTeX) from images/PDFs
  • Document digitization with complex tables and formulas

devstral-small-2:24b​

Mistral AI & All Hands AI • 24B parameters • Context: 200,000 tokens

Second iteration of Devstral (Small 2), state-of-the-art agent model for software engineering, deployed on high-performance GPU server.

Technical specifications:

  • Speed : 38 tokens/second
  • Consumption : 3.80 kWh per million tokens
  • License : Apache 2.0
  • Location : FR 🇫🇷

Capabilities: ✅ Tools/Agent • ✅ Vision • ❌ Reasoning • ✅ Security

Tags: Agent Programming Vision Open-Source Large Context FP8 Fast

Use cases:

  • Autonomous coding agents requiring low latency
  • Rapid code refactoring
  • Iterative engineering tasks

devstral:24b​

⚠️ WARNING: This model is deprecated. Recommendation to migrate to devstral-small-2:24b.

Mistral AI & All Hands AI • 24B parameters • Context: 120,000 tokens

Devstral 24b is an agent-based LLM specialized in software engineering, co-developed by Mistral AI and All Hands AI.

Technical specifications:

  • Speed: 44 tokens/second
  • Energy consumption: 3.28 kWh per million tokens
  • License: Apache 2.0
  • Location: FR 🇫🇷

Capabilities: ✅ Tools/Agent • ❌ Vision • ❌ Reasoning • ✅ Security

Tags: Agent Programming Open-Source Large Context FP8

Use cases:

  • Exploration and modification of codebases
  • Autonomous software engineering agents
  • Complex code refactoring and generation

embeddinggemma:300m​

Google • 300M parameters • Context: 2,048 tokens

State-of-the-art embedding model from Google, optimized for its size, ideal for search and semantic retrieval tasks.

Technical specifications:

  • Speed : 175 tokens/second
  • Energy consumption : 0.35 kWh per million tokens
  • License : Google Gemma Terms of Use
  • Location : FR 🇫🇷

Capabilities: ❌ Tools/Agent • ❌ Vision • ❌ Reasoning • ❌ Security

Tags: Embedding Compact Semantic Efficient Multilingual

Use cases:

  • Information retrieval and search
  • Document classification and clustering
  • Semantic similarity search
  • Deployment on resource-constrained devices (mobile, laptop)

gemma3:1b​

Google • 1B parameters • Context: 120,000 tokens

Ultra-fast and efficient micro-model Gemma 3.

Technical specifications:

  • Speed : 53 tokens/second
  • Energy consumption : 1.15 kWh per million tokens
  • License : Google Gemma Terms of Use
  • Location : FR 🇫🇷

Capabilities: ❌ Tools/Agent • ❌ Vision • ❌ Reasoning • ❌ Security

Tags: Compact Efficient Edge

Use cases:

  • Fast text classification
  • Simple chatbots
  • Rapid prototyping

gemma3:4b​

Google • 4B Parameter • Kontext: 120.000 Tokens

Kompakter Gemma 3-Modell mit 4B Parametern, bietet ein hervorragendes Leistungs-/Größen-Verhältnis.

Technische Spezifikationen:

Funktionen: ❌ Werkzeuge/Agent • ❌ Vision • ❌ Schlussfolgerung • ❌ Sicherheit

Tags: Kompakt Effizient Edge

Anwendungsfälle:

  • Persönliche Assistenten auf Laptop
  • Textzusammenfassung
  • Leichte Übersetzungen

gpt-oss:20b​

OpenAI • 20B Parameters • Context: 120,000 tokens

Open-weight language model from OpenAI, optimized for efficiency and deployment on consumer-grade hardware.

Technical Specifications:

  • Speed: 9 tokens/second
  • Energy Consumption: 14.81 kWh per million tokens
  • License: Apache 2.0
  • Location: FR 🇫🇷

Capabilities: ✅ Tools/Agent • ❌ Vision • ✅ Reasoning • ❌ Security

Tags: MoE Agent Reasoning Open-Source Compact Fast

Use Cases:

  • Deployments on resource-constrained devices (edge devices) or low-cost servers.
  • Applications requiring fast inference with strong reasoning capabilities.
  • Agent-based use cases involving function calls, web navigation, and code execution.
  • Fine-tuning for specialized tasks on consumer-grade hardware.

granite-embedding:278m​

IBM • 278M parameters • Context: 8,192 tokens

Ultra-compact IBM Granite embedding model, designed for maximum efficiency.

Technical specifications:

  • Speed : 196.3 tokens/second
  • Energy consumption : 0.31 kWh per million tokens
  • License : Apache 2.0
  • Location : FR 🇫🇷

Capabilities: ❌ Tools/Agent • ❌ Vision • ❌ Reasoning • ❌ Security

Tags: Embedding Compact Efficient

Use cases:

  • Semantic search
  • Document clustering

granite4-small-h:32b​

IBM • 32B (9B active) Parameters • Context: 128,000 tokens

IBM's MoE (Mixture-of-Experts) model, designed as a "workhorse" for daily enterprise tasks, featuring excellent efficiency for long contexts.

Technical Specifications:

  • Speed: 49 tokens/second
  • Energy Consumption: 2.95 kWh per million tokens
  • License: Apache 2.0
  • Location: FR 🇫🇷

Capabilities: ✅ Tools/Agent • ❌ Vision • ✅ Reasoning • ✅ Security

Tags: Agent Reasoning Security MoE Large Context Efficient

Use Cases:

  • Conversational agents for customer support with access to extensive knowledge bases.
  • Enterprise workflow automation requiring the use of multiple tools.
  • Analysis of long documents with optimized resource consumption.
  • Deployment on medium-sized infrastructures thanks to its efficiency.

granite4-tiny-h:7b​

IBM • 7B (1B active) parameters • Context: 128,000 tokens

Ultra-efficient hybrid MoE model from IBM, designed for low latency, edge and local applications, and as a foundational component for agent workflows.

Technical Specifications:

  • Speed: 58 tokens/second
  • Energy Consumption: 2.30 kWh per million tokens
  • License: Apache 2.0
  • Location: FR 🇫🇷

Capabilities: ✅ Tools/Agent • ❌ Vision • ✅ Reasoning • ✅ Security

Tags: Agent Reasoning Security MoE Large Context Efficient Fast Compact

Use Cases:

  • Embedded and edge applications requiring low latency.
  • Fast tasks within larger agent workflows (e.g., function calling).
  • Document analysis on consumer-grade hardware.
  • Deployments requiring minimal memory footprint.

medgemma:27b​

Google • 27B Parameters • Context: 128,000 tokens

MedGemma is one of Google's most advanced open models for understanding medical text and images, based on Gemma 3.

Technical Specifications:

  • Speed: 22 tokens/second
  • Energy Consumption: 6.56 kWh per million tokens
  • License: Google Gemma Terms of Use
  • Location: FR 🇫🇷

Capabilities:
✅ Tools/Agent • ✅ Vision • ❌ Reasoning • ❌ Security

Tags: Medical Vision Specialized Large Context

Use Cases:

  • Medical image interpretation (Report generation and VQA)
  • Medical text understanding and clinical reasoning (Decision support)
  • Patient interaction (Interviews and medical triage)
  • Medical record synthesis and literature search

ministral-3:3b​

Mistral AI • 3B parameters • Context: 250,000 tokens

High-performance compact model from Mistral AI, designed for efficiency in local and edge deployments.

Technical Specifications:

  • Speed : 50 tokens/second
  • Energy Consumption : 1.22 kWh per million tokens
  • License : Apache 2.0
  • Location : FR 🇫🇷

Capabilities: ✅ Tools/Agent • ❌ Vision • ❌ Reasoning • ❌ Security

Tags: Compact Efficient Edge

Use Cases:

  • Local inference on mobile devices or edge devices
  • Responsive personal assistants
  • Fast routing and classification tasks

ministral-3:8b​

Mistral AI • 8B parameters • Context: 250,000 tokens

Intermediate-sized model from the Ministral family, offering an optimal balance between performance and resource usage.

Technical specifications:

  • Speed: 55 tokens/second
  • Energy consumption: 2.42 kWh per million tokens
  • License: Apache 2.0
  • Location: FR 🇫🇷

Capabilities: ✅ Tools/Agent • ❌ Vision • ✅ Reasoning • ❌ Security

Tags: Efficient Edge Reasoning

Use cases:

  • Advanced local conversational assistants
  • Document analysis and information extraction
  • Tasks requiring a good trade-off between speed and quality

mistral-small3.2:24b​

Mistral AI • 24B parameters • Context: 128,000 tokens

Minor update to Mistral Small 3.1, improving instruction following, function calling robustness, and reducing repetition errors.

Technical specifications:

  • Speed : 27 tokens/second
  • Energy consumption : 5.35 kWh per million tokens
  • License : Apache 2.0
  • Location : FR 🇫🇷

Capabilities: ✅ Tools/Agent • ✅ Vision • ❌ Reasoning • ✅ Security

Tags: Vision Agent Security Instruction Following

Use cases:

  • Conversational agents with enhanced instruction following
  • Robust integration with external tools via function calling
  • Applications requiring high reliability to avoid repetitions
  • Use cases identical to Mistral Small 3.1, with improved performance

qwen3-2507-think:4b​

Qwen Team • 4B parameters • Context: 250,000 tokens

Qwen3-4B model optimized for reasoning, with improved performance on logical tasks, mathematics, science, and code, featuring an extended context of 250K tokens.

Technical Specifications:

  • Speed: 52 tokens/second
  • Energy Consumption: 2.56 kWh per million tokens
  • License: Apache 2.0
  • Location: FR 🇫🇷

Capabilities: ✅ Tools/Agent • ❌ Vision • ✅ Reasoning • ❌ Security

Tags: Agent Reasoning Large Context Compact Fast

Use Cases:

  • Highly complex reasoning tasks (logic, math, science, code).
  • Conversational agents with extremely long conversation histories (256k tokens).
  • Deep reasoning analysis of very large documents.
  • Integration with external tools via function calling on extremely large contexts.

qwen3-2507:4b​

⚠️ WARNING: This model is deprecated. Deprecated.

Qwen Team • 4B parameters • Context: 250,000 tokens

Updated version of the non-thinking mode of Qwen3-4B, featuring significant improvements in general capabilities, expanded knowledge coverage, and better alignment with user preferences.

Technical Specifications:

  • Speed: 30 tokens/second
  • Energy Consumption: 4.44 kWh per million tokens
  • License: Apache 2.0
  • Location: FR 🇫🇷

Capabilities: ✅ Tools/Agent • ❌ Vision • ❌ Reasoning • ❌ Security

Tags: Agent Large Context Compact Fast Multilingual

Use Cases:

  • General tasks requiring precise instruction following and logical reasoning.
  • Multilingual applications with broad knowledge coverage.
  • High-quality text generation for open-ended and subjective tasks.
  • Analysis of very large documents thanks to a 256k-token context.

qwen3-embedding:0.6b​

Qwen Team • 0.6B parameters • Context: 32,768 tokens

Ultra-lightweight Qwen3 embedding model, optimized for speed and efficiency on resource-constrained infrastructure.

Technical Specifications:

  • Speed: N/A
  • Energy Consumption: 0.57 kWh per million tokens
  • License: Apache 2.0
  • Localization: FR 🇫🇷

Capabilities: ❌ Tools/Agent • ❌ Vision • ❌ Reasoning • ❌ Security

Tags: Embedding Compact Efficient

Use Cases:

  • Fast semantic search
  • Real-time text classification

qwen3-embedding:4b​

Qwen Team • 4B parameters • Context: 40,000 tokens

Ultra-performant embedding model Qwen3-4B, offering deep semantic understanding and an extended context window.

Technical Specifications:

  • Speed : N/A
  • Energy Consumption : 0.57 kWh per million tokens
  • License : Apache 2.0
  • Location : FR 🇫🇷

Capabilities: ❌ Tools/Agent • ❌ Vision • ❌ Reasoning • ❌ Security

Tags: Embedding Large Context Efficient

Use Cases:

  • Semantic search on long documents
  • RAG with extended context windows
  • High-precision semantic analysis

qwen3-vl:2b​

Qwen Team • 2B Parameters • Context: 250,000 tokens

Ultra-compact multimodal model Qwen3-VL, bringing advanced vision capabilities to edge devices.

Technical Specifications:

  • Speed: 64 tokens/second
  • Power Consumption: 0.95 kWh per million tokens
  • License: Apache 2.0
  • Location: FR 🇫🇷

Capabilities: ✅ Tools/Agent • ✅ Vision • ❌ Reasoning • ❌ Security

Tags: Vision Compact Efficient Multimodal Edge OCR

Use Cases:

  • Real-time image analysis on mobile devices
  • Lightweight OCR and document reading
  • Fast visual sorting and classification

qwen3-vl:4b​

Qwen Team • 4B Parameter • Kontext: 250.000 Tokens

Ausgewogener multimodaler Qwen3-VL-Modell mit solider Bildverarbeitungsleistung bei geringem Ressourcenverbrauch.

Technische Spezifikationen:

  • Geschwindigkeit : 57 Tokens/Sekunde
  • Energieverbrauch : 2,34 kWh pro Million Tokens
  • Lizenz : Apache 2.0
  • Standort : FR 🇫🇷

Funktionen: ✅ Werkzeuge/Agent • ✅ Bildverarbeitung • ❌ Schlussfolgerung • ❌ Sicherheit

Tags: Bildverarbeitung Kompakt Multimodal Effizient Video OCR

Anwendungsfälle:

  • Automatisierte Dokumentenanalyse (Rechnungen, Formulare)
  • Verständnis von Videoinhalten
  • Interaktive visuelle Assistenten

qwen3-vl:8b​

Qwen Team • 8B Parameters • Context: 250,000 tokens

Multimodal model Qwen3-VL (8B), delivering advanced vision capabilities with a reasonable footprint.

Technical Specifications:

  • Speed: 44 tokens/second
  • Energy Consumption: 3.03 kWh per million tokens
  • License: Apache 2.0
  • Location: FR 🇫🇷

Capabilities: ✅ Tools/Agent • ✅ Vision • ❌ Reasoning • ❌ Security

Tags: Vision Compact Multimodal Efficient Video OCR

Use Cases:

  • Automated document analysis
  • Video content understanding
  • Interactive visual assistants

qwen3:0.6b​

Qwen Team • 0.6B parameters • Context: 40,000 tokens

Ultra-light Qwen3 model with 0.6 billion parameters, delivering exceptional inference speed for simple and fast tasks.

Technical Specifications:

  • Speed: 46 tokens/second
  • Energy Consumption: 1.33 kWh per million tokens
  • License: Apache 2.0
  • Location: FR 🇫🇷

Capabilities: ✅ Tools/Agent • ❌ Vision • ❌ Reasoning • ❌ Security

Tags: Compact Fast Efficient Multilingual

Use Cases:

  • Simple text processing tasks
  • Fast classification and sorting
  • Lightweight assistants with low latency

rnj-1:8b​

Essential AI • 8B parameters • Context: 32,000 tokens

8B "Open Weight" model specialized in code, mathematics, and sciences (STEM).

Technical specifications:

  • Speed : 31 tokens/second
  • Consumption : 1.97 kWh per million tokens
  • License : Open Weights
  • Location : FR 🇫🇷

Capabilities: ❌ Tools/Agent • ❌ Vision • ✅ Reasoning • ❌ Security

Tags: Code Maths STEM Reasoning Efficient

Use cases:

  • Advanced programming assistant and code generation
  • Solving complex mathematical problems
  • Scientific and technical tasks (STEM)

translategemma:12b​

Google • 12B parameters • Context: 128,000 tokens

State-of-the-art open translation model based on Gemma 3, supporting 55 languages.

Technical specifications:

  • Speed : 30 tokens/second
  • Energy consumption : 4.44 kWh per million tokens
  • License : Gemma Terms of Use
  • Location : FR 🇫🇷

Capabilities:
❌ Tools/Agent • ❌ Vision • ❌ Reasoning • ❌ Security

Tags: Translation Multilingual Specialized

Use cases:

  • Translation of long documents
  • Cross-language communication
  • Content localization

translategemma:27b​

Google • 27B Parameters • Context: 120,000 tokens

High-performance translation model based on Gemma 3 27B.

Technical specifications:

  • Speed: 44 tokens/second
  • Energy consumption: 6.35 kWh per million tokens
  • License: Gemma Terms of Use
  • Localization: FR 🇫🇷

Capabilities:
❌ Tools/Agent • ❌ Vision • ❌ Reasoning • ❌ Security

Tags: Translation Multilingual Specialized High Performance

Use cases:

  • High-precision translation
  • Technical document translation
  • Literary and cultural nuances

translategemma:4b​

Google • 4B Parameters • Context: 128,000 tokens

Compact version of the TranslateGemma translation model, optimized for speed.

Technical Specifications:

  • Speed: 38 tokens/second
  • Energy Consumption: 1.27 kWh per million tokens
  • License: Gemma Terms of Use
  • Localization: FR 🇫🇷

Capabilities: ❌ Tools/Agent • ❌ Vision • ❌ Reasoning • ❌ Security

Tags: Translation Multilingual Specialized Efficient

Use Cases:

  • Fast text translation
  • Translation on devices with limited resources
  • Real-time localization

Multilingual Dialogue​

Chatbots and assistants capable of communicating in multiple languages with automatic language detection, context preservation throughout the entire conversation, and understanding of linguistic nuances.

Recommended Models:

  • Llama 3.3
  • Mistral Small 3.2
  • Qwen 3
  • Openai OSS
  • Granite 4

Long Document Analysis​

Processing large documents (>100 pages) while preserving context across the entire text, extracting key information, generating relevant summaries, and answering specific questions about the content.

Recommended Models:

  • Gemma 3
  • Qwen next
  • Qwen 3
  • Granite 4

Programming and Development​

Generation and optimization of code in multiple languages, debugging, refactoring, development of complete features, understanding of complex algorithmic implementations, and creation of unit tests

Recommended models:

  • DeepCoder
  • Qwen3 coder
  • Granite 4
  • Devstral

Visual Analysis​

Direct processing of images and visual documents without prior OCR preprocessing, interpretation of technical diagrams, charts, tables, drawings, and photos with detailed textual explanations of the visual content.

Recommended Models:

  • deepseek-OCR
  • Mistral Small 3.2
  • Gemma 3
  • Qwen 3 VL

Security and Compliance​

Applications requiring specific security capabilities; sensitive content filtering, reasoning traceability, GDPR/HDS compliance verification, risk minimization, vulnerability analysis, and adherence to industry-specific regulations

Recommended models:

  • Granite Guardian
  • Granite 4
  • Devstral
  • Mistral Small 3.2
  • Magistral small

Lightweight and Embedded Deployments​

Applications requiring minimal resource footprint, deployment on devices with limited capacity, real-time inference on standard CPUs, and integration into embedded systems or IoT devices

Recommended models:

  • Gemma 3n
  • Granite 4 tiny
  • Qwen 3 VL (2B)