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MODULE 02 · MODEL EVALUATION & ARCHITECTURES

Frontier Model Benchmarks & Gen 4 Architectures

Empirical evaluation matrix of Gen 4 frontier LLMs alongside an interactive slide deck inspector analyzing on-policy distillation, agentic benchmarks, and test-time compute.

💡 EXECUTIVE EVALUATION SUMMARY
Frontier AI selection is no longer about buying raw parameter size. Gemini 3.1 Pro dominates autonomous software engineering (95% SWE-bench) and corporate knowledge synthesis (1890 Elo). Claude Opus 5 leads in novel spatial reasoning and agentic tool planning. Deploying mid-tier distilled models for daily workflows delivers 80% cost savings compared to top-tier parent orchestrators.
🏷️ Plain-English Analogy (Mixture-of-Experts (MoE)): An MoE model is like employing 896 specialized consultants, but only activating the 16 exact experts needed for each question. You get 2.8 trillion parameters of knowledge at 1/16th the operational server cost.
DYNAMIC VISUAL COMPARISON

Frontier Capability Radar Chart

Selecting models in the comparator dynamically updates the spider graph below.

Coding (SWE-bench) Elo (GDPval) Spatial (ARC-AGI) Context Size Speed (TPS) Cost Efficiency
Gemini 3.1 Pro (Model A) Claude Opus 5 (Model B)
HEAD-TO-HEAD SELECTOR

Model Selector & Comparator

Select any two models to update both table metrics and the spider graph in real time.

VS
SWE-bench (Coding)
95.0% 83.0%
GDPval-AA Elo
1890 Elo 1861 Elo
ARC-AGI-3 (Spatial)
32.0% 30.2%
Context Window
2.0M tokens 1.0M tokens
CLICK ANY BENCHMARK FOR EXPLAINER

Gen 4 Frontier Model Benchmark Matrix

Click on any column header (, , ) to view methodology details.

Model Name Developer Architecture Context Window SWE-bench ℹ️ GDPval Elo ℹ️ ARC-AGI-3 ℹ️ Status
Claude Opus 5 Anthropic Dense / Hybrid MoE 1.0M tokens 83% 1861 Elo 30.2% Validated
Claude Fable 5 Anthropic High-Efficiency MoE 500K tokens 84% 1820 Elo 28.5% Validated
Gemini 3.1 Pro Google Multimodal Sparse MoE 2.0M tokens 95% 1890 Elo 32% Frontier Peak
GPT-5.6 Sol OpenAI Reasoning Hybrid 1.0M tokens 86.5% 1845 Elo 29.1% Validated
Kimi K3 MoE Moonshot AI 2.8T MoE (896 experts, 16 active) 2.0M tokens 81.5% 1790 Elo 26.8% New Addition
Claude 3.7 Sonnet Anthropic Hybrid MoE 1.0M tokens 85.2% 1850 Elo N/A Validated
SLIDE INSPECTOR (15 SLIDES)

Frontier Model Architecture & Agentic Economics Deck Inspector

Click any slide to open the high-resolution lightbox inspector with slide details and OCR text breakdown.

Frontier Model Architecture & Agentic Economics
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Frontier Model Architecture & Agentic Economics

Agentic Evaluation Matrix (SWE-bench & ARC-AGI)
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Agentic Evaluation Matrix (SWE-bench & ARC-AGI)

The Token-Efficiency Paradox
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The Token-Efficiency Paradox

Adaptive Test-Time Compute & Speed Toggles
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Adaptive Test-Time Compute & Speed Toggles

Intrinsic Alignment vs Outer Safety Cascade
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Intrinsic Alignment vs Outer Safety Cascade

Model Personalities & Operational Styles
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Model Personalities & Operational Styles

Evolution of Frontier Architectures (Gen 1 to Gen 4)
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Evolution of Frontier Architectures (Gen 1 to Gen 4)

Gen 4 Model Bifurcation (Distilled vs Parent)
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Gen 4 Model Bifurcation (Distilled vs Parent)

Zero-Data-Retention (ZDR) Enterprise Compliance
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Zero-Data-Retention (ZDR) Enterprise Compliance

Four Strategic Pillars for Distilled Deployment
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Four Strategic Pillars for Distilled Deployment

Macroeconomic Pressures & On-Policy Distillation
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Macroeconomic Pressures & On-Policy Distillation

Agentic Benchmark Dominance (OSWorld & SWE-bench)
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Agentic Benchmark Dominance (OSWorld & SWE-bench)

Self-Verification Loop Expansion Mechanics
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Self-Verification Loop Expansion Mechanics

Enterprise Model Deployment Strategy Matrix
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Enterprise Model Deployment Strategy Matrix

Conclusion: Active Structural AI Engineering
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Conclusion: Active Structural AI Engineering

ARCHITECTURAL SIMULATOR

Sparse MoE & KV Cache Topology Calculator

Adjust MoE expert count and attention type to simulate FLOP savings and VRAM footprint reductions.

ACTIVE PARAMETER COMPUTE 50.0B Params Compute cost per generated token
PER-TOKEN FLOP SAVINGS 75.0% FLOP Reduction Versus dense equivalent training