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Apriel-1.5-15b-Thinker API

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Apriel-1.5-15b-Thinker API Usage
Endpoint
How to use Apriel-1.5-15b-Thinker
Model details
Architecture & Foundation:
• Built from Pixtral-12B base using depth upscaling (40 to 48 layers) for enhanced reasoning capacity
• Multimodal architecture with vision encoder, projection network, and decoder supporting both text and image inputs
• 14.9B parameters optimized for single-GPU deployment with BF16 precision
• 131K token context window with sequence packing for efficient processing
Training Methodology:
• Three-stage progressive training: depth upscaling, staged continual pretraining (CPT), and supervised fine-tuning (SFT)
• CPT Stage 1: 50% text reasoning, 20% replay data, 30% multimodal tokens covering documents, charts, OCR, and visual reasoning
• CPT Stage 2: Targeted visual reasoning via synthetic data generation for spatial structure, compositional understanding, and fine-grained perception
• Text-SFT only approach with 2M+ high-quality instruction-response pairs featuring explicit reasoning traces—no reinforcement learning or preference optimization
• Trained on 640 H100 GPUs for 7 days using Fast-LLM training stack
Performance Characteristics:
• Achieves 52 on Artificial Analysis Intelligence Index, matching DeepSeek-R1-0528 and Gemini-2.5-Flash
• Strong mathematical reasoning: 87% AIME'25, 77.3% MMLU-Pro, 71.3% GPQA Diamond
• Enterprise-focused benchmarks: 68% Tau2 Bench Telecom, 62% IFBench
• Multimodal capabilities: 70.2% MMMU, 75.5% MathVista, 88.2% CharXiv descriptive, 82.87% AI2D
• Extensive reasoning by default with explicit thinking steps before final responses
• Performs within 5 points of Gemini-2.5-Flash and Claude Sonnet-3.7 across ten vision benchmarks
• At least 1/10 the size of any model scoring >50 on AA Intelligence Index
Prompting Apriel-1.5-15b-Thinker
Applications & Use Cases
Mathematical & Scientific Reasoning:
• Competition-level mathematics: 87% on AIME'25, 80.66% on AIME'24
• Graduate-level problem solving: 71.3% on GPQA Diamond
• Scientific computing and reasoning tasks with strong performance on SciCode
• Mathematical reasoning within visual contexts (MathVision, MathVista, MathVerse)
Code Assistance & Development:
• Functional correctness in code generation via LiveCodeBench evaluation
• Coding tasks spanning multiple programming languages
• API/function invocation and complex instruction following
• Real-world Linux shell execution and system tool use (TerminalBench)
Enterprise & Domain-Specific Applications:
• Specialized telecom domain tasks: 68% on Tau2 Bench Telecom
• Instruction following and compliance: 62% on IFBench
• Document understanding, chart interpretation, and OCR-related tasks
• Long-context reasoning (AA-LCR benchmark) for extended document analysis
Multimodal Understanding:
• Image understanding and reasoning: 70.2% MMMU, 66.3% MMStar
• Document and diagram comprehension: 88.2% CharXiv descriptive, 82.87% AI2D
• Visual mathematical problem-solving: 75.5% MathVista
• Chart understanding with descriptive and reasoning capabilities
General-Purpose Capabilities:
• Multi-domain knowledge and advanced reasoning (77.3% MMLU-Pro)
• Conversational AI and question answering across diverse topics
• Logical reasoning and multi-step task execution
• Content moderation, security, and robustness applications
• On-premises deployment for privacy-sensitive and air-gapped environments