Model Catalog•2 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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DeepSeek/Reasoning

Open Weights

DeepSeek R1

DeepSeek-R1 is an open-weights frontier reasoning model trained via large-scale reinforcement learning without supervised cold-start, featuring a 671B parameter Mixture-of-Experts architecture with 37B active parameters.

Context Window128k ctx

Pricing In / Out\$0.55 / \$2.19

AIME 202479.8 % pass@1

Llama 3.3 70B Instruct

Meta's flagship 70B parameter open-weights model delivering capability competitive with previous generation 405B models on a single-node GPU footprint with 128k context.

Context Window128k ctx

Pricing In / Out\$0.35 / \$0.4

MMLU88.6 % pass@1

Overall DNA:82/100

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.