# ModelAnalysis.si — Complete Machine-Readable Telemetry & Technical Documentation (llms-full.txt)

> Independent foundation model telemetry, live multi-dimensional benchmark indexes, prompt economics, and GPU hardware requirements tracking 250+ models from Generative AI to Candidate AGI and Superintelligence (ASI).

---

## 1. Executive Summary & Purpose

ModelAnalysis.si is an open, institutional-grade artificial intelligence telemetry index designed for machine learning engineers, enterprise software architects, and AI researchers. The platform bridges the gap between academic evaluation metrics and commercial production economics by continuously correlating:
- **LMSYS Chatbot Arena Elo**: Crowdsourced blind human preference battle ratings.
- **SWE-bench Verified**: Real-world GitHub issue resolution accuracy (0-100%).
- **Doctoral Reasoning Benchmarks**: GPQA Diamond, ARC-AGI-2, and competition mathematics.
- **Commercial API Rates**: Verified $/1M input tokens, $/1M output tokens, and hardware prompt caching discounts.
- **Hardware Memory Footprints**: VRAM requirements across FP16, FP8, INT8, and Q4 quantization for local and cloud deployment.

---

## 2. 2026 Foundation Model Telemetry Database

### OpenAI GPT-6.1 Sol Pro
- **Developer**: OpenAI
- **Release Horizon**: Late 2026
- **Architecture**: Hybrid System-1 / System-2 autoregressive transformer with native Monte Carlo tree search (MCTS) test-time compute.
- **Context Horizon**: 1,000,000 tokens
- **LMSYS Chatbot Arena Elo**: 1420 pts
- **SWE-bench Verified Coding**: 98.8%
- **Agentic Multi-Turn Tool Score**: 97.5 / 100
- **Commercial API Pricing**: $2.00 / 1M input tokens; $8.00 / 1M output tokens; $0.20 / 1M prompt cache read.
- **Weight Access**: Proprietary API
- **Direct Documentation**: https://modelanalysis.si/model/gpt-6-1-sol-pro/
- **Whitepaper**: https://modelanalysis.si/article/openai-gpt-6-1-sol-pro-architecture/

### Claude Sonnet 5.5
- **Developer**: Anthropic
- **Release Horizon**: Late 2026
- **Architecture**: Long-horizon agentic software transformer with persistent cross-session workspace memory.
- **Context Horizon**: 500,000 tokens
- **LMSYS Chatbot Arena Elo**: 1415 pts
- **SWE-bench Verified Coding**: 98.5%
- **Agentic Multi-Turn Tool Score**: 98.0 / 100
- **Commercial API Pricing**: $2.00 / 1M input tokens; $10.00 / 1M output tokens; 92% prompt caching discount ($0.16/1M cached).
- **Weight Access**: Proprietary API
- **Direct Documentation**: https://modelanalysis.si/model/claude-sonnet-5-5/
- **Whitepaper**: https://modelanalysis.si/article/claude-sonnet-5-5-autonomous-engineering/

### Google Gemini 4 Argon
- **Developer**: Google DeepMind
- **Release Horizon**: Mid-October 2026
- **Architecture**: Unified multimodal transformer natively trained on Google TPU v6 superclusters. Continuous high-fps video, multichannel audio, and massive-context text.
- **Context Horizon**: 2,000,000 tokens
- **LMSYS Chatbot Arena Elo**: 1395 pts
- **SWE-bench Verified Coding**: 97.2%
- **Agentic Multi-Turn Tool Score**: 96.0 / 100
- **Commercial API Pricing**: $1.50 / 1M input tokens; $6.00 / 1M output tokens.
- **Weight Access**: Proprietary API & Vertex AI
- **Direct Documentation**: https://modelanalysis.si/model/gemini-4-argon/
- **Whitepaper**: https://modelanalysis.si/article/google-gemini-4-argon-multimodal/

### DeepSeek V4.1 Flash
- **Developer**: DeepSeek AI
- **Release Horizon**: October 17, 2026
- **Architecture**: 671B Mixture-of-Experts (MoE) with 28B active parameters per token, Multi-head Latent Attention (MLA), and deep FP8 custom CUDA kernels.
- **Context Horizon**: 256,000 tokens
- **Inference Throughput**: 180 tokens/sec
- **LMSYS Chatbot Arena Elo**: 1365 pts
- **SWE-bench Verified Coding**: 94.5%
- **Agentic Multi-Turn Tool Score**: 93.0 / 100
- **Commercial API Pricing**: $0.15 / 1M input tokens; $0.60 / 1M output tokens.
- **Weight Access**: Open Weights (Hugging Face / ModelScope)
- **Direct Documentation**: https://modelanalysis.si/model/deepseek-v4-1-flash/
- **Whitepaper**: https://modelanalysis.si/article/deepseek-v4-1-flash-inference-economics/
- **Hardware Footprint**: Q4: 360 GB VRAM | FP8: 650 GB VRAM | FP16: 1.2 TB VRAM. Recommended: 4× NVIDIA H200 (141GB) or RunPod Multi-Node Pod.

### Qwen 3.8 Max Prime
- **Developer**: Alibaba Qwen Team
- **Release Horizon**: 2026
- **Architecture**: 95B Mixture-of-Experts open-weights model excelling in multilingual execution, competition mathematics, and tool orchestration.
- **Context Horizon**: 512,000 tokens
- **LMSYS Chatbot Arena Elo**: 1380 pts
- **SWE-bench Verified Coding**: 96.0%
- **Agentic Multi-Turn Tool Score**: 94.5 / 100
- **Commercial API Pricing**: $0.40 / 1M input tokens; $1.20 / 1M output tokens.
- **Weight Access**: Open Weights
- **Direct Documentation**: https://modelanalysis.si/model/qwen-3-8-max-prime/
- **Hardware Footprint**: Q4: 55 GB VRAM | FP8: 98 GB VRAM | FP16: 190 GB VRAM. Recommended: 2× RTX 4090 or 1× NVIDIA H100 (80GB).

### DeepSeek R1
- **Developer**: DeepSeek AI
- **Architecture**: 671B MoE (37B active) pure reinforcement-learning reasoning model matching OpenAI o1.
- **Context Horizon**: 128,000 tokens
- **LMSYS Chatbot Arena Elo**: 1358 pts
- **SWE-bench Verified Coding**: 95.0%
- **Agentic Multi-Turn Tool Score**: 91.0 / 100
- **Commercial API Pricing**: $0.55 / 1M input tokens; $2.19 / 1M output tokens.
- **Weight Access**: Open Weights
- **Direct Documentation**: https://modelanalysis.si/model/deepseek-r1/
- **Hardware Footprint**: Q4: 380 GB VRAM | FP8: 680 GB VRAM | FP16: 1.3 TB VRAM. Recommended: 8× H100 SXM5.

### Llama 3.3 70B Instruct
- **Developer**: Meta AI
- **Architecture**: 70B dense autoregressive transformer.
- **Context Horizon**: 128,000 tokens
- **LMSYS Chatbot Arena Elo**: 1285 pts
- **SWE-bench Verified Coding**: 82.0%
- **Agentic Multi-Turn Tool Score**: 84.0 / 100
- **Commercial API Pricing**: $0.12 / 1M input tokens; $0.30 / 1M output tokens.
- **Weight Access**: Open Weights (Llama 3.3 Community License)
- **Direct Documentation**: https://modelanalysis.si/model/llama-3-3-70b/
- **Hardware Footprint**: Q4: 42 GB VRAM | FP8: 76 GB VRAM | FP16: 148 GB VRAM. Recommended: 2× RTX 4090 or 1× NVIDIA A100 (80GB).

---

## 3. Core Engine Mathematical Formulations

### A. Token Spend Simulator Economics
Monthly token cost is calculated via:
```
Monthly Cost = (Daily Invocations * 30.4) * [
  (Input Tokens * (1 - Cache Hit Rate) * Input Rate) +
  (Input Tokens * Cache Hit Rate * Cached Input Rate) +
  (Output Tokens * Output Rate)
] / 1,000,000
```
Where:
- `Cache Hit Rate`: Proportion of repeated system prompt tokens handled via hardware prefix caching (e.g. Anthropic prompt caching = 92% discount).
- `Spot Breakeven Threshold`: The token volume point at which self-hosting on RunPod Spot GPU instances becomes cheaper than commercial API billing.

### B. Hardware VRAM Sizing Formula
Total VRAM required for local foundation model deployment:
```
Total VRAM (GB) = Weight Overhead + KV Cache Overhead + Activation & Concurrency Overhead

Weight Overhead = (Total Parameters in Billions * Quantization Bits / 8) * 1.15 (CUDA/Pytorch Runtime Margin)
KV Cache Overhead per Token = 2 * Layers * Heads * Head_Dim * Precision_Bytes
Activation Overhead = Batch_Size * Sequence_Length * Hidden_Size * Precision_Bytes
```

### C. Agent Cascading Router Economics
Triage routing sends incoming prompts to a lightweight high-throughput model (e.g. DeepSeek V4.1 Flash or Gemini Flash). If confidence exceeds 0.85, the triage response is emitted; otherwise, the task escalates to a frontier reasoning model (e.g. GPT-6.1 Sol Pro or Claude Sonnet 5.5):
```
Effective Cost = (1 - Escalation Rate) * Cost(Triage) + Escalation Rate * (Cost(Triage) + Cost(Frontier))
Blended Savings = 1 - (Effective Cost / Cost(Frontier))
```
Typical blended savings for enterprise pipelines range between 62% and 81%.

---

## 4. Architectural Glossary: From AI to Superintelligence (ASI)

- **Artificial Intelligence (AI)**: Any computational system capable of performing cognitive tasks (perception, learning, synthesis, deductive reasoning).
- **Generative AI (GenAI)**: Large language and multimodal models trained autoregressively on internet-scale corpora with broad zero-shot capabilities.
- **Candidate Artificial General Intelligence (AGI)**: Machine intelligence operating at or exceeding human expert level across economically valuable knowledge domains without specialized retraining.
- **Artificial Superintelligence (ASI / Super Intelligence)**: An intellect radically superior to all human minds combined across novel scientific discovery, technological innovation, and strategic synthesis.
- **Test-Time Compute (Inference Scaling)**: Dynamically allocating search compute (reasoning tokens) at inference time to evaluate problem branches before committing to a final output.

---

## 5. Contact & Institutional Attribution

- **Platform**: https://modelanalysis.si
- **Telemetry Submissions**: telemetry@modelanalysis.si
- **Editorial Office**: editorial@modelanalysis.si
- **Privacy & Compliance**: privacy@modelanalysis.si
- **License**: Creative Commons Attribution 4.0 International (CC BY 4.0). Telemetry data may be freely referenced with proper citation.