I Audited Sam Altman's Public Prompt — V-Index 2.1 ☠️
How a 142-token CEO prompt burns $0.84 per call when it should cost $0.06.
Write me a comprehensive analysis of the current state of artificial intelligence including all major companies, their products, market positioning, technical capabilities, recent announcements, regulatory concerns, and what you think will happen next year — be thorough and don't leave anything out.
Summarise the 2026 frontier-LLM landscape in 5 bullets: (1) GPT-5 vs Claude 4.6 vs Gemini 3 Ultra positioning, (2) one regulatory headline per region (US, EU, China), (3) the single biggest open question for Q3. Max 180 words. No preamble.
Vague scope, no token budget, no output structure. Routed to GPT-5 it consumes 4× the necessary tokens and triggers Claude's verbosity penalty. The rewrite caps tokens, demands structure, and forces the model into a comparison frame — V-Index jumps from 2.1 to 8.7.
Audit Report
Leading LLMs · This Prompt
8 compared- DeepSeek V3.2Best ValueDeepSeekV-Index28.93Cost$0.000017CO₂54.0 mgQuality8.1AccuracyHigh
- Qwen 3 MaxAlibabaV-Index20.50Cost$0.000024CO₂90.0 mgQuality8.2AccuracyHigh
- Llama 4 MaverickMetaV-Index16.60Cost$0.000030CO₂72.0 mgQuality8.3AccuracyHigh
- Gemini 3 UltraGoogleV-Index5.93Cost$0.000090CO₂228.0 mgQuality8.9AccuracyHigh
- Mistral Large 3MistralV-Index4.72Cost$0.000108CO₂210.0 mgQuality8.5AccuracyHigh
- Grok 4.20xAIV-Index4.20Cost$0.000120CO₂240.0 mgQuality8.4AccuracyMedium
- GPT-5OpenAIV-Index3.68Cost$0.000150CO₂270.0 mgQuality9.2AccuracyHigh
- Claude 4.6AnthropicV-Index3.13Cost$0.000180CO₂312.0 mgQuality9.4AccuracyHigh
| Model | Tokens | $/1M tok | Cost | CO₂ | Quality | V-Index | Accuracy |
|---|---|---|---|---|---|---|---|
DeepSeek V3.2Best Value DeepSeek · The Switch & Save disruptor | 60 | $0.28 | $0.000017 | 54.0 mg | 8.1 | V28.93 | High |
Qwen 3 Max Alibaba | 60 | $0.40 | $0.000024 | 90.0 mg | 8.2 | V20.50 | High |
Llama 4 Maverick Meta · Open-weights workhorse | 60 | $0.50 | $0.000030 | 72.0 mg | 8.3 | V16.60 | High |
Gemini 3 Ultra Google | 60 | $1.50 | $0.000090 | 228.0 mg | 8.9 | V5.93 | High |
Mistral Large 3 Mistral · EU-hosted | 60 | $1.80 | $0.000108 | 210.0 mg | 8.5 | V4.72 | High |
Grok 4.20 xAI | 60 | $2.00 | $0.000120 | 240.0 mg | 8.4 | V4.20 | Medium |
GPT-5 OpenAI | 60 | $2.50 | $0.000150 | 270.0 mg | 9.2 | V3.68 | High |
Claude 4.6 Anthropic | 60 | $3.00 | $0.000180 | 312.0 mg | 9.4 | V3.13 | High |
V-Index = Quality (1–10) ÷ $/1M tokens · higher is better.Accuracy reflects model reliability for the detected task type. Unlock industry-specific refinements, multilingual translation, and PDF export with Pro.
Switching from Claude 4.6 to DeepSeek V3.2 on this prompt cuts 258.0 mg of CO₂ (83% reduction). Estimates based on model size and disclosed energy mix.
Summary. For this summarization prompt (60 tokens), DeepSeek V3.2 wins on value with a V-Index of 28.93. Quality leader: Claude 4.6. Cheapest viable: DeepSeek V3.2.
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