Models / Deep CogitoCogito / / Cogito v2.1 671B API
Cogito v2.1 671B API
Advanced hybrid reasoning model with self-improving capabilities

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Cogito v2.1 671B API Usage
Endpoint
How to use Cogito v2.1 671B
Model details
Architecture Overview:
• Cogito v2.1 671B employs a sophisticated Mixture-of-Experts (MoE) architecture with 671 billion total parameters, utilizing sparse routing mechanisms to activate only specialized expert subnetworks per token, enabling massive scale without proportional compute costs
• Features a 128K token context window optimized for long-form reasoning, technical documentation, and multi-turn conversations
• Implements a hybrid inference system supporting both standard mode (direct answers using internalized "intuition") and reasoning mode (step-by-step self-reflection with visible thought chains)
• Optimized for efficient serverless deployment on Together AI's infrastructure
Training Methodology - Iterated Distillation & Amplification (IDA):
• Revolutionary self-improvement approach where the model runs reasoning chains during training, then is trained on its own intermediate thoughts to develop stronger "machine intuition"
• Unlike traditional models that rely on extended inference-time reasoning, Cogito distills successful reasoning patterns directly into model parameters
• Training process explicitly rewards shorter, more efficient reasoning paths while discouraging unnecessary computational detours
• Trained on multilingual datasets spanning 30+ languages with emphasis on coding, STEM, instruction following, and general helpfulness
• Total training cost remarkably achieved at under $3.5 million for the entire Cogito family (3B to 671B), demonstrating unprecedented cost efficiency
Performance Characteristics:
• AIME 2025 (Competition Mathematics): 89.47% - outperforming models 10x larger
• MATH-500 benchmark: 98.57% accuracy
• GPQA Diamond (Scientific Reasoning): 77.72%
• SWE-Bench Verified (Coding): 42.00% solve rate
• MMLU Pro (Reasoning & Knowledge): 84.69%
• Multilingual MMLU: 86.24% across 30+ languages
• Average token efficiency: 4,894 tokens per response (lowest among frontier models)
• Performance competitive with DeepSeek v3, matching or exceeding latest 0528 model while using 60% shorter reasoning chains
• Approaches capabilities of closed models like Claude 4 Opus, O3, and GPT-5 across diverse benchmarks
• Demonstrates emergent multimodal reasoning capabilities, able to reason about images despite not being explicitly trained for visual tasks
Prompting Cogito v2.1 671B
Applications & Use Cases
High-Performance Use Cases:
• Advanced Mathematical Problem Solving: Superior performance on competition mathematics (AIME 2025: 89.47%), calculus, optimization problems, and quantitative analysis
• Software Engineering & Code Generation: 42% solve rate on SWE-Bench demonstrates strong debugging, code review, and system design capabilities
• Scientific Research & STEM: 77.72% on GPQA Diamond showcases expertise in physics, chemistry, biology, and interdisciplinary scientific reasoning
• Multilingual Applications: 86.24% on Multilingual MMLU enables global deployment across 30+ languages with native-level comprehension
• Legal & Policy Analysis: Reasoning mode excels at applying precedents, analyzing case law, and providing nuanced legal interpretations
Enterprise Applications:
• Intelligent Document Processing: 128K context window handles entire technical documents, contracts, research papers in single context
• Customer Support Automation: Hybrid mode allows fast responses for simple queries, deep reasoning for complex troubleshooting
• Financial Analysis & Risk Assessment: Strong quantitative reasoning combined with efficient token usage for cost-effective at-scale deployment
• Educational Technology: Step-by-step reasoning mode ideal for tutoring, homework help, and adaptive learning systems
• Research Assistance: Frontier performance at $1.25/1M tokens makes large-scale research analysis economically viable
Developer & Research Applications:
• Rapid Prototyping: Together AI's serverless platform enables instant deployment without infrastructure setup
• Model Experimentation: Compare standard vs reasoning modes in real-time via playground interface
• Benchmark Development: Performance approaching closed frontier models enables reproducible research
• Scalable Research: Serverless infrastructure scales automatically for large-scale experiments
Cost-Sensitive Deployments:
• High-Volume Production: Lowest token usage (4,894 avg) among frontier models translates to 20-40% cost savings vs alternatives
• Serverless Efficiency: Pay-per-use pricing on Together AI eliminates infrastructure costs and management overhead
• Startup & SMB Applications: Frontier capabilities at accessible pricing ($1.25/1M tokens) democratizes advanced AI
• Auto-scaling: Together AI's serverless infrastructure automatically handles traffic spikes without manual intervention
Unique Capabilities:
• Emergent Image Reasoning: Despite no explicit visual training, demonstrates ability to reason about images when presented in context
• Efficiency-First Design: 60% shorter reasoning chains mean faster responses and lower costs without sacrificing accuracy
• Hybrid Intelligence: Seamlessly switch between fast intuition and deep deliberation based on query complexity
