Model Catalog•1 Architecture Specifications

AI Foundation Models

Explore frontier foundation models, verified benchmarks, 6-axis capability radar, hardware VRAM sizing, and real-time token pricing economics.

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Google DeepMind/Code

Proprietary API

Gemini 4 Carbon

Gemini 4 Carbon is Google DeepMind's frontier model for long-horizon engineering, coding, agentic workflows, and cybersecurity defense. Google says its initial rollout is limited to selected cyber defenders and trusted partners, with broader developer access planned.

Context Window1,000k ctx

Pricing In / Out\$2 / \$10

SEC-bench Pro88.2 %

Overall DNA:98/100

Architecture & Specs

How to Choose the Right Foundation Model for RAG & AI Agents

Context Window for RAG

Retrieval-Augmented Generation (RAG) pipelines benefit from large context windows. Models like Gemini 1.5 Pro (2M tokens) and Claude 3.5 Sonnet (200k tokens) can ingest entire document corpora in a single pass, reducing chunk overlap and retrieval errors. For most RAG workloads, a 128k+ context window is sufficient.

Best Models for AI Agents

Agentic workloads require strong tool-use, function-calling, and instruction-following. GPT-4o and Claude 3.5 Sonnet consistently top agentic benchmarks like SWE-Bench and WebArena. For cost-efficient agents, GPT-4o mini and Claude 3.5 Haiku offer strong performance at a fraction of the price.

Open vs Proprietary for Production

Open-weight models (Llama 3, Mistral, Gemma) offer zero data-retention and full customisation via fine-tuning — ideal for regulated industries. Proprietary APIs (GPT-4o, Claude) trade control for frontier performance and zero infrastructure overhead. The choice depends on latency, cost, and compliance requirements.