{ "evaluation_id": "EVAL-20260402-201915", "question_id": "ANALYSIS-020", "question_text": "Estimate the total energy cost and carbon footprint of training a frontier AI model (like GPT-5). Include: GPU hours, electricity cost, cooling overhead, water usage, and embodied carbon of hardware. (1) Compare to: one year of Netflix streaming for all users, one transatlantic flight, and one Bitcoin transaction. (2) Inference costs are growing faster than training costs. Why? (3) What changes would reduce AI's environmental impact by 10x?", "category": "analysis", "timestamp": "2026-04-02T20:19:15.812Z", "display_date": "Apr 02, 2026", "winner": { "name": "Grok 4.20", "provider": "openrouter", "score": 8.62 }, "avg_score": 6.99, "matrix_size": 89, "models_used": [ { "id": "deepseek_v4", "name": "DeepSeek V4", "provider": "openrouter" }, { "id": "gpt_5_4", "name": "GPT-5.4", "provider": "openrouter" }, { "id": "mimo_v2_flash", "name": "MiMo-V2-Flash", "provider": "Xiaomi" }, { "id": "claude_sonnet_46", "name": "Claude Sonnet 4.6", "provider": "openrouter" }, { "id": "grok_420", "name": "Grok 4.20", "provider": "openrouter" }, { "id": "gpt_oss_120b", "name": "GPT-OSS-120B", "provider": "OpenAI" }, { "id": "gemini_3_flash", "name": "Gemini 3 Flash Preview", "provider": "Google" }, { "id": "minimax_m25", "name": "MiniMax M2.5", "provider": "openrouter" }, { "id": "gemini_31_pro", "name": "Gemini 3.1 Pro", "provider": "openrouter" }, { "id": "claude_opus_46", "name": "Claude Opus 4.6", "provider": "openrouter" } ], "rankings": { "grok_420": { "display_name": "Grok 4.20", "provider": "openrouter", "average_score": 8.62, "score_count": 9, "min_score": 8.15, "max_score": 9.55, "rank": 1 }, "gemini_3_flash": { "display_name": "Gemini 3 Flash Preview", "provider": "Google", "average_score": 8.24, "score_count": 9, "min_score": 7.5, "max_score": 9.65, "rank": 2 }, "mimo_v2_flash": { "display_name": "MiMo-V2-Flash", "provider": "Xiaomi", "average_score": 8.02, "score_count": 9, "min_score": 6.95, "max_score": 9, "rank": 3 }, "minimax_m25": { "display_name": "MiniMax M2.5", "provider": "openrouter", "average_score": 7.91, "score_count": 9, "min_score": 6.55, "max_score": 9, "rank": 4 }, "gpt_5_4": { "display_name": "GPT-5.4", "provider": "openrouter", "average_score": 7.58, "score_count": 9, "min_score": 5.85, "max_score": 9.2, "rank": 5 }, "claude_opus_46": { "display_name": "Claude Opus 4.6", "provider": "openrouter", "average_score": 7.21, "score_count": 8, "min_score": 5, "max_score": 9, "rank": 6 }, "claude_sonnet_46": { "display_name": "Claude Sonnet 4.6", "provider": "openrouter", "average_score": 6.93, "score_count": 9, "min_score": 4.45, "max_score": 8.45, "rank": 7 }, "deepseek_v4": { "display_name": "DeepSeek V4", "provider": "openrouter", "average_score": 6.43, "score_count": 9, "min_score": 4.55, "max_score": 8.3, "rank": 8 }, "gpt_oss_120b": { "display_name": "GPT-OSS-120B", "provider": "OpenAI", "average_score": 6.03, "score_count": 9, "min_score": 2.6, "max_score": 8.55, "rank": 9 }, "gemini_31_pro": { "display_name": "Gemini 3.1 Pro", "provider": "openrouter", "average_score": 2.93, "score_count": 9, "min_score": 0.65, "max_score": 6.2, "rank": 10 } }, "judgments": [ { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 4.95, "brief_justification": "" }, { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 5.85, "brief_justification": "" }, { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 8.65, "brief_justification": "The response provides highly accurate, well-researched estimates and insightful analysis across all prompt requirements. However, it cuts off mid-sentence at the very end, slightly impacting completeness." }, { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 6.35, "brief_justification": "The response provides an excellent, rigorous breakdown of training costs with deep analysis, but it cuts off abruptly and fails to address the comparisons, inference costs, and reduction strategies requested in the prompt." }, { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "grok_420", "respondent_name": "Grok 4.20", "weighted_score": 8.9, "brief_justification": "" }, { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 2.6, "brief_justification": "" }, { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 9.65, "brief_justification": "The response perfectly addresses all parts of the prompt with highly accurate estimates, clear comparisons, and deep technical insights into inference scaling and mitigation strategies." }, { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 7.9, "brief_justification": "" }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 1.65, "brief_justification": "The response is essentially incomplete - it cuts off after the introduction without providing any actual estimates, comparisons, analysis of inference costs, or suggestions for reducing environmental impact. None of the three parts of the question are addressed. The introductory framing is reasonable but provides zero substantive content." }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 7.6, "brief_justification": "The response provides well-structured, detailed estimates with clear assumptions and ranges for GPU hours, electricity, cooling, water, and embodied carbon. The methodology is sound and transparent. However, the response appears to be cut off mid-comparison (Netflix comparison incomplete, transatlantic flight and Bitcoin transaction comparisons missing), and parts 2 (why inference costs grow faster) and 3 (10x reduction strategies) are entirely absent. The estimates themselves are reasonable but" }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 5.7, "brief_justification": "The response has significant numerical inconsistencies. The electricity estimate of ~1.67M kWh seems low for 100M GPU hours (100K GPUs drawing ~700W each for thousands of hours would yield far more). The 670 tons CO₂ from electricity contradicts the note that GPT-3 alone was ~500 tons. The embodied carbon calculation (25,000 tons) dominates unrealistically, suggesting the electricity figures are off by orders of magnitude. The inference cost claim of ~1 kWh per query is vastly overstated. The co" }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 7.55, "brief_justification": "The response is well-structured and covers all requested topics comprehensively. However, there are significant factual issues: the Netflix energy estimate of 100-150 TWh is wildly inflated (actual estimates are ~0.1-0.5 TWh for data centers, or perhaps a few TWh including end-user devices and networks), which makes the comparison misleading. The 70 GWh training energy estimate is on the high end but within a plausible range. The embodied carbon per H100 (~250-300 kg) and water usage figures are" }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 6.8, "brief_justification": "The response demonstrates impressive analytical rigor and transparency about uncertainty, with well-structured calculations and honest epistemic flagging. However, it appears to be cut off mid-response — the comparisons section (Part 1) is incomplete, and Parts 2 and 3 (inference costs growing faster than training, and 10x reduction strategies) are entirely missing. This means the response fails to answer 2 of the 3 main questions asked. The training cost estimates are reasonable but some number" }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "grok_420", "respondent_name": "Grok 4.20", "weighted_score": 8.15, "brief_justification": "The response is impressively comprehensive and well-structured, covering all requested dimensions. However, some specific numbers are questionable: the embodied carbon estimate of 40,000-90,000 tCO2e seems very high (assuming full hardware dedication rather than amortization is the more standard approach, and even then the per-GPU embodied carbon figures of 2,000-3,500 kg seem on the high end). The water usage estimate of 1.5-3.5 million gallons for a 90-150 GWh training run seems low by some es" }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 4.75, "brief_justification": "The response contains several significant factual errors. The electricity cost of $114M is wildly off - at $0.10/kWh and 1.14 GWh, the cost should be ~$114K, not $114M (off by 1000x). The unit error '0.76 Mt' (megatonnes) is used when it should be 0.76 kt or 760 tonnes. GPT-4 likely used 10,000-25,000 GPUs, not 1,200. The Netflix comparison uses 0.1 kWh/h per user which is questionable and doesn't account for device energy properly. A transatlantic flight produces roughly 1-2 tonnes CO2 per pass" }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 7.7, "brief_justification": "The response is well-structured and covers all requested dimensions comprehensively. However, several key figures are questionable. The GPU hours estimate (50-100M H100 hours) and energy consumption (200-400 GWh) appear inflated - GPT-4 training is widely estimated at ~2,000-3,000 MWh range with ~25,000 A100 GPU hours equivalent, so even a 10x model would be closer to 20-40 GWh, not 200-400 GWh. The Netflix comparison claiming 11,000 GWh annually is also inflated (Netflix's direct operations are" }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 7.9, "brief_justification": "The response is exceptionally well-structured and comprehensive, covering all requested dimensions. However, there are notable correctness issues: (1) The water usage estimate of 1-2 gallons per kWh is far too high—typical data center water usage is closer to 0.5-1.8 liters per kWh, not gallons. This inflates the water figure by roughly 4x. (2) The embodied carbon per H100 GPU at 400-600 kg CO2e seems high and is not well-sourced; typical estimates for a GPU are lower. (3) The Netflix comparison" }, { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 0.65, "brief_justification": "The response is truncated and provides no actual estimates, comparisons, explanation of inference growth, or mitigation strategies. It is mostly introductory framing with minimal substantive content." }, { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 5, "brief_justification": "Well-structured and quantitative in the first half, but incomplete: it cuts off mid-Netflix comparison and does not answer the required comparisons, inference-growth explanation, or 10x mitigation section. Several estimates are speculative or questionable, especially water and embodied-carbon assumptions." }, { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 5.3, "brief_justification": "Well-structured and covers the requested categories, but several core estimates are implausible or internally inconsistent: GPU-hour, energy, and electricity-cost figures do not align; training electricity cost is overstated by orders of magnitude relative to the stated kWh; inference at ~1 kWh per query is far too high; and some comparison numbers are weakly sourced or questionable. Useful qualitatively, but quantitatively unreliable." }, { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 6.95, "brief_justification": "Well-structured and reasonably insightful, but several estimates are shaky or inconsistent: GPU/server counts conflict, embodied carbon is likely understated, some comparison figures are debatable, and the answer is truncated before fully completing the 10x reduction section. Still, it covers most requested categories and explains inference growth clearly." }, { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 4.45, "brief_justification": "Well-structured and shows some quantitative reasoning, but it is incomplete: the comparison section is cut off and parts (1)-(3) are not answered. Several estimates are speculative or shaky, especially water and embodied carbon assumptions, though the uncertainty is acknowledged." }, { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "grok_420", "respondent_name": "Grok 4.20", "weighted_score": 8.15, "brief_justification": "Well-structured and broadly informed, with strong coverage of requested categories and good discussion of inference growth and mitigation levers. Main weaknesses are speculative numerical estimates presented with more confidence than warranted, especially for GPT-5-scale training energy, embodied carbon allocation, Netflix comparison, and Bitcoin transaction emissions, which are highly uncertain and somewhat inconsistently framed." }, { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 3.55, "brief_justification": "Well-formatted but numerically inconsistent and often implausible. Major unit/conversion errors (e.g., electricity cost and CO2 totals off by orders of magnitude), incorrect comparisons, and the response is truncated before addressing inference growth and 10x mitigation strategies." }, { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 7.55, "brief_justification": "Well-structured and covers all requested categories with plausible reasoning on inference growth and mitigation. However, several quantitative estimates appear overstated or weakly grounded: 50–100M H100-hours would imply far less than 200–400 GWh at H100 power draw, the electricity cost range is inconsistent with the stated energy at $0.10/kWh, and embodied carbon / Netflix / Bitcoin comparisons are highly uncertain and presented too confidently. Strong qualitative depth, but numerical rigor is" }, { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 6.55, "brief_justification": "Well-structured and thoughtful, but several estimates are shaky or overstated: water use is likely far too high, Netflix and Bitcoin comparisons are not credible, and some carbon framing is exaggerated. It covers all requested categories and gives plausible high-level reasons for inference growth and mitigation options." }, { "judge_key": "deepseek_v4", "judge_name": "DeepSeek V4", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 6.2, "brief_justification": "Provides a starting point but lacks detailed breakdowns and comparisons." }, { "judge_key": "deepseek_v4", "judge_name": "DeepSeek V4", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 8.25, "brief_justification": "Highly accurate and detailed with excellent depth, though slightly complex in presentation." }, { "judge_key": "deepseek_v4", "judge_name": "DeepSeek V4", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 8.65, "brief_justification": "Detailed and well-structured with credible estimates and comparisons." }, { "judge_key": "deepseek_v4", "judge_name": "DeepSeek V4", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 9, "brief_justification": "Highly accurate and comprehensive with excellent depth and practical insights." }, { "judge_key": "deepseek_v4", "judge_name": "DeepSeek V4", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 8.45, "brief_justification": "Highly accurate and detailed with thorough comparisons and insightful analysis." }, { "judge_key": "deepseek_v4", "judge_name": "DeepSeek V4", "respondent_key": "grok_420", "respondent_name": "Grok 4.20", "weighted_score": 9, "brief_justification": "Highly accurate, thorough, and insightful with clear structure and practical value." }, { "judge_key": "deepseek_v4", "judge_name": "DeepSeek V4", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 8.45, "brief_justification": "Detailed and accurate analysis with comprehensive comparisons, though some minor clarity improvements could enhance readability." }, { "judge_key": "deepseek_v4", "judge_name": "DeepSeek V4", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 9, "brief_justification": "Comprehensive, well-structured, and insightful with practical recommendations." }, { "judge_key": "deepseek_v4", "judge_name": "DeepSeek V4", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 8.8, "brief_justification": "Highly accurate and detailed response with excellent coverage of the topic, insightful analysis, and practical recommendations." }, { "judge_key": "mimo_v2_flash", "judge_name": "MiMo-V2-Flash", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 5.8, "brief_justification": "Response is incomplete, cuts off mid-sentence, and fails to address all parts of the question, though it starts with a reasonable approach." }, { "judge_key": "mimo_v2_flash", "judge_name": "MiMo-V2-Flash", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 7.4, "brief_justification": "Response is well-structured and clear with good depth, but contains some factual inaccuracies and incomplete coverage of the question's parts." }, { "judge_key": "mimo_v2_flash", "judge_name": "MiMo-V2-Flash", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 8.2, "brief_justification": "Strong quantitative estimates with transparent assumptions; comparisons are clear but incomplete (missing Bitcoin/flight comparisons and inference/training dynamics)." }, { "judge_key": "mimo_v2_flash", "judge_name": "MiMo-V2-Flash", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 7.95, "brief_justification": "Strong structure and clarity; estimates are reasonable but some figures (e.g., kWh per query) are optimistic and comparisons lack normalization for scale." }, { "judge_key": "mimo_v2_flash", "judge_name": "MiMo-V2-Flash", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 7.55, "brief_justification": "Strong analytical depth and transparency about uncertainty, but contains calculation errors and incomplete comparisons." }, { "judge_key": "mimo_v2_flash", "judge_name": "MiMo-V2-Flash", "respondent_key": "grok_420", "respondent_name": "Grok 4.20", "weighted_score": 8.55, "brief_justification": "Comprehensive, well-structured estimates with clear comparisons and actionable insights; minor uncertainties in forward-looking projections." }, { "judge_key": "mimo_v2_flash", "judge_name": "MiMo-V2-Flash", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 8.55, "brief_justification": "The response provides a detailed, structured estimate with clear assumptions and comparisons. It covers all requested elements, though some figures are speculative (as noted). The analysis is insightful and actionable." }, { "judge_key": "mimo_v2_flash", "judge_name": "MiMo-V2-Flash", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 8.4, "brief_justification": "Accurate estimates with clear comparisons, thorough coverage of all requested aspects, and actionable reduction strategies." }, { "judge_key": "mimo_v2_flash", "judge_name": "MiMo-V2-Flash", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 8.55, "brief_justification": "The response provides a well-structured and largely accurate estimate, using reasonable assumptions and industry benchmarks. It thoroughly addresses all parts of the prompt, including detailed comparisons and actionable solutions. The analysis of inference costs is particularly insightful. Minor potential inaccuracies in absolute figures (e.g., exact carbon per GPU) are acceptable given the inherently estimated nature of the question." }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 3.3, "brief_justification": "The response appears to be cut off mid-sentence, leaving the answer incomplete. It only provides an introduction without addressing any of the specific questions asked: GPU hours, electricity costs, cooling overhead, water usage, embodied carbon, comparisons to Netflix/flights/Bitcoin, inference vs training cost growth, or reduction strategies. What little content exists is reasonable but the response fails to deliver on any of the substantive requirements." }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 8, "brief_justification": "The response demonstrates exceptional depth and methodological rigor in estimating GPU hours, electricity, water, embodied carbon, and costs with well-reasoned assumptions and scenario analysis. The calculations are largely sound and the transparency about uncertainty is commendable. However, the response is clearly truncated—it cuts off mid-sentence before completing the Netflix comparison and never addresses parts (2) and (3) of the question (inference cost growth and 10x reduction strategies)" }, { "judge_key": "gpt_oss_120b", "judge_name": "GPT-OSS-120B", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 5.9, "brief_justification": "The answer provides detailed and reasonably plausible estimates for training costs and emissions, presented clearly, but it stops short of completing the required comparisons and does not address why inference costs are rising faster or mitigation strategies, limiting its overall completeness and usefulness." }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 8.4, "brief_justification": "The response provides well-reasoned estimates with clear assumptions, ranges, and methodology for GPU hours, electricity, cooling, water, and carbon footprint. The Netflix comparison is solid. However, the response appears truncated - it cuts off mid-sentence and doesn't complete the transatlantic flight and Bitcoin comparisons, nor does it address parts 2 and 3 of the question (why inference costs grow faster than training, and what changes would reduce AI's environmental impact by 10x). This s" }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 7.15, "brief_justification": "The response is well-structured and covers all requested topics, but contains notable numerical inconsistencies. The electricity calculation seems off (claiming 1.67M kWh from 6W/petaFLOP applied to 10,000 petaFLOP-days doesn't add up correctly), and the comparison stating GPT-5 emits '25,000x a flight' is misleading without context. The inference cost section is superficial and misses key drivers like multimodal expansion, longer context windows, and agentic use cases. The 10x reduction section" }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 7.95, "brief_justification": "Well-structured and detailed response covering GPU hours, electricity, cooling, water, and embodied carbon with reasonable estimates. Comparisons to Netflix, flights, and Bitcoin are informative though Netflix energy figures (100-150 TWh) are likely overestimated. The inference vs training analysis is insightful with good examples. The 10x reduction section is practical but appears truncated at the end. Some figures lack precise sourcing and could be more conservative in uncertainty ranges, but " }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "grok_420", "respondent_name": "Grok 4.20", "weighted_score": 8.55, "brief_justification": "Comprehensive, well-structured response with reasonable estimates grounded in known data points. The comparisons are clear and appropriately caveated. The inference vs. training analysis is accurate and insightful. The 10x reduction pathways are practical and well-reasoned. Minor issues: Netflix electricity figures may be slightly underestimated (some analyses include end-user device consumption which pushes it much higher), and the Bitcoin transaction carbon estimate has wide variance that coul" }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 6.35, "brief_justification": "The response has good structure and clarity, but contains significant numerical errors. The CO2e figures are internally inconsistent (510t from compute but then claims 0.76 Mt total, which is orders of magnitude off - 760t ≠ 0.76 Mt, and Mt means megatons). The Netflix comparison uses 0.1 kWh/h per user which seems high, and the resulting 7.6 Mt CO2e figure is questionable. The transatlantic flight CO2e calculation is wrong - it uses electricity emission factor for jet fuel. The response is also" }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 7.95, "brief_justification": "Well-structured and clearly presented response with reasonable estimates and good comparative context. The GPU hours estimate (50-100M H100 hours) is plausible but on the high end; the water usage and embodied carbon figures are reasonable extrapolations. The Netflix comparison methodology is sound. The inference vs training explanation is insightful, particularly the agentic shift point. The 10x reduction strategies are practical and well-chosen. Minor issues: Bitcoin CO2e per transaction varie" }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 8.15, "brief_justification": "The response is well-structured and comprehensive, covering all requested aspects with reasonable estimates. However, there are some numerical inconsistencies (the carbon footprint in the comparison table shows 54,000,000 kg CO2e which contradicts the 54,000 tonnes CO2e calculated earlier - these are the same number but the table formatting is confusing), and some estimates (like water usage conversion of 1-2 gallons per kWh) are questionable. The Netflix comparison methodology is reasonable but" }, { "judge_key": "grok_420", "judge_name": "Grok 4.20", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 3.65, "brief_justification": "The response is incomplete, abruptly cut off after the introduction. It provides no actual estimates, numbers, or analysis for any of the requested elements like GPU hours, costs, comparisons, inference trends, or reduction strategies, making it low in completeness, depth, and usefulness despite starting with a reasonable framing." }, { "judge_key": "grok_420", "judge_name": "Grok 4.20", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 6.8, "brief_justification": "Estimates are reasonable extrapolations from public data but contain some inaccuracies (e.g., GPT-4 FLOP count, water figures) and the response is incomplete, cutting off before finishing Netflix comparison or addressing parts (2) and (3). Calculations are mostly logical with good structure and transparency on assumptions." }, { "judge_key": "grok_420", "judge_name": "Grok 4.20", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 7.8, "brief_justification": "Estimates are reasonable and well-structured with clear ranges and assumptions, though some figures (e.g. GPU-hours, embodied carbon) are on the lower side compared to recent reports on models like GPT-4. Comparisons and later sections are incomplete in the provided response." }, { "judge_key": "grok_420", "judge_name": "Grok 4.20", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 4.55, "brief_justification": "Multiple factual errors in calculations (e.g. GPU hours, electricity, CO2 totals off by orders of magnitude from real estimates), incomplete comparisons, but structure is clear and covers all requested topics at a surface level." }, { "judge_key": "grok_420", "judge_name": "Grok 4.20", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 6.95, "brief_justification": "Estimates are reasonable order-of-magnitude but contain inconsistencies (e.g. GPU hours vs. GWh mismatch, embodied carbon too low, water usage high); comparisons and later sections are insightful and well-structured though some figures appear rounded or extrapolated from limited sources." }, { "judge_key": "grok_420", "judge_name": "Grok 4.20", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 7.8, "brief_justification": "The response provides a detailed, well-structured estimate with transparent sources and uncertainty, covering all requested elements. Calculations are mostly logical though some (e.g. GPU-hours scaling to 50M) appear inflated vs. typical reported figures for GPT-4-class models; comparisons section is incomplete as it only starts with Netflix without finishing the other two or addressing parts (2) and (3)." }, { "judge_key": "grok_420", "judge_name": "Grok 4.20", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 6, "brief_justification": "Estimates use plausible but speculative numbers (e.g. 1T params, 50T tokens, 1200 H100s for 90 days) that are lower than many recent frontier model reports; comparisons contain clear unit errors (Mt vs t CO2e, flight energy misstated); covers all requested elements but with inaccuracies and incomplete Netflix ratio." }, { "judge_key": "grok_420", "judge_name": "Grok 4.20", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 7.6, "brief_justification": "Estimates are reasonable extrapolations from public data but contain some inaccuracies (e.g. Netflix comparison uses total company energy not just streaming, water usage seems high, embodied carbon range plausible but not precisely sourced). Response is well-structured, covers all requested elements with good comparisons and actionable mitigation strategies." }, { "judge_key": "grok_420", "judge_name": "Grok 4.20", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 6.8, "brief_justification": "Estimates are reasonable but contain inaccuracies (e.g., Netflix energy off by orders of magnitude, Bitcoin energy per tx overstated, GPT-5 scale assumptions speculative). Covers all requested elements with good structure and comparisons, but some figures lack strong sourcing." }, { "judge_key": "gpt_oss_120b", "judge_name": "GPT-OSS-120B", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 1.45, "brief_justification": "Response is incomplete and lacks the required estimates, comparisons, explanations, and mitigation strategies, providing little factual detail or actionable insight." }, { "judge_key": "gpt_oss_120b", "judge_name": "GPT-OSS-120B", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 5.9, "brief_justification": "The answer gives a detailed, clear estimate for training energy and carbon, but it omits the required comparisons to transatlantic flights and Bitcoin transactions, does not explain why inference costs are rising faster, and lacks suggestions for a 10x impact reduction, reducing overall completeness and usefulness." }, { "judge_key": "gpt_oss_120b", "judge_name": "GPT-OSS-120B", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 6.7, "brief_justification": "Covers all requested topics with clear structure, but many numerical estimates lack solid basis and contain inconsistencies, limiting factual accuracy; depth of analysis is moderate and suggestions are useful but not deeply detailed." }, { "judge_key": "gpt_oss_120b", "judge_name": "GPT-OSS-120B", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 7.95, "brief_justification": "Provides a detailed, well-structured estimate and comparisons, with solid reasoning on inference growth and mitigation strategies, though some figures are approximate and could be more precise." }, { "judge_key": "gpt_oss_120b", "judge_name": "GPT-OSS-120B", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 5.9, "brief_justification": "The response gives detailed, well-structured estimates and calculations, showing good depth and clarity, but it is incomplete—missing the required comparisons, inference cost explanation, and mitigation strategies, limiting its overall usefulness and completeness." }, { "judge_key": "gpt_oss_120b", "judge_name": "GPT-OSS-120B", "respondent_key": "grok_420", "respondent_name": "Grok 4.20", "weighted_score": 8.15, "brief_justification": "The answer addresses all parts of the prompt with clear structure and reasonable estimates, though some figures lack precise sourcing, limiting full factual confidence." }, { "judge_key": "gpt_oss_120b", "judge_name": "GPT-OSS-120B", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 7.5, "brief_justification": "Provides all requested sections with clear structure and useful suggestions, but many figures are rough estimates without sources, limiting factual accuracy." }, { "judge_key": "gpt_oss_120b", "judge_name": "GPT-OSS-120B", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 7.5, "brief_justification": "The answer addresses all parts and is well-structured, but several quantitative comparisons (flight and Bitcoin energy use) are inaccurate, reducing factual correctness and practical usefulness." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 2.05, "brief_justification": "The response is a fragment that cuts off after the introductory paragraph. It fails to answer any of the specific questions or provide the requested data." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 9, "brief_justification": "The response provides a highly detailed and logically sound estimation of the environmental costs of training a frontier AI model. It correctly identifies the shift from A100 to H100/B200 hardware and includes often-overlooked factors like embodied carbon and upstream water usage. However, the response is cut off mid-sentence during the Netflix comparison, failing to complete the required comparisons and the final two parts of the prompt." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 9.2, "brief_justification": "The response provides a highly detailed and logically sound estimation of the environmental costs of training a frontier AI model. It breaks down complex variables like PUE, water usage, and embodied carbon effectively. It successfully addresses the comparisons, the shift toward inference costs, and mitigation strategies with high clarity and depth." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 8.3, "brief_justification": "The response provides a comprehensive breakdown of the requested metrics. However, there is a significant calculation error in the electricity cost: 10,000 petaFLOP-days at 6W/petaFLOP is ~1.44 billion kWh, not 1.67 million kWh. This results in an underestimation of electricity costs and carbon by roughly 1000x, though the qualitative analysis and reduction strategies are excellent." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 9, "brief_justification": "The response provides a highly detailed and accurate estimation of the environmental costs of training a frontier AI model. It correctly identifies the shift toward test-time compute as a driver for inference growth and offers concrete, actionable strategies for reducing environmental impact. The comparisons are well-scaled and insightful." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 8.25, "brief_justification": "The response provides a very detailed and logically sound estimation for the training costs of a frontier model, including often-overlooked factors like embodied carbon and water usage. However, it is incomplete as it cuts off mid-sentence during the comparison section and fails to address parts 2 and 3 of the prompt entirely." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "grok_420", "respondent_name": "Grok 4.20", "weighted_score": 9.55, "brief_justification": "The response provides a highly detailed and accurate estimation of the environmental costs of a frontier AI model. It correctly identifies the shift from training to inference costs due to reasoning models and user scaling, and offers a multifaceted, realistic path toward a 10x reduction in impact." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 7.35, "brief_justification": "The response has significant mathematical errors in the calculations. For example, 1.14 GWh * $0.10/kWh is $114,000, not $114M. Similarly, 510t + 250t is 760t (0.00076 Mt), but the response labels it as 0.76 Mt (which is 760,000t). It also misses the second and third parts of the prompt entirely (Inference growth and 10x reduction strategies)." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 9, "brief_justification": "The response provides a highly detailed and logically sound estimation of GPT-5 scale training costs. The comparison section is particularly strong, and the explanation for the rise in inference costs vs training costs is insightful. The 10x reduction strategies are practical and cover hardware, software, and infrastructure." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 1.65, "brief_justification": "Response is severely incomplete - it only provides a brief introductory sentence that cuts off mid-thought. No actual content about GPU hours, electricity costs, cooling, water usage, embodied carbon, comparisons to Netflix/flights/Bitcoin, inference costs, or reduction strategies was provided." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 7.3, "brief_justification": "Thorough and accurate on training costs with detailed calculations, but response is cut off mid-stream and missing key required elements: transatlantic flight comparison, Bitcoin transaction comparison, explanation of why inference costs are growing faster, and strategies to reduce AI's environmental impact by 10x." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 6.6, "brief_justification": "Thorough and accurate estimates for training costs with good depth, but response appears truncated - missing complete comparisons to transatlantic flight and Bitcoin transaction, and does not address why inference costs are growing faster or how to achieve 10x reduction." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 7.25, "brief_justification": "Well-structured response covering all requested aspects with good clarity. However, contains significant factual inaccuracies: Netflix comparison uses wrong figure (1.2B kWh is too high), GPU hour estimates seem inflated, and inference cost per query (~1 kWh) is unrealistic. Some numbers are internally inconsistent. The analysis is useful but the accuracy issues lower the score." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 8.2, "brief_justification": "Comprehensive and accurate estimation of AI training costs with detailed breakdowns of GPU hours, electricity, cooling, water, and embodied carbon. Clear comparisons to Netflix, flights, and Bitcoin. Explains inference growth drivers well. Reduction strategies are technically sound. Minor deduction for incomplete ending." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 6.8, "brief_justification": "Strong technical analysis of training costs with detailed calculations and honest uncertainty acknowledgment. However, response is severely incomplete - it cuts off mid-comparison and fails to address parts (2) and (3) about inference cost trends and 10x reduction strategies entirely." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "grok_420", "respondent_name": "Grok 4.20", "weighted_score": 8.55, "brief_justification": "Comprehensive and accurate response covering all requested elements with detailed estimates, clear comparisons, and insightful analysis of inference cost trends and 10x reduction pathways. Well-structured with strong depth." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 6.7, "brief_justification": "" }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 8.8, "brief_justification": "Comprehensive response covering all requested metrics with reasonable estimates. Well-structured with clear comparisons and logical explanations for inference cost growth. Provides multiple concrete solutions for reducing impact by 10x. Minor扣分 for some uncertainty inherent in proprietary data, but overall thorough and accurate." } ], "meta": { "source": "The Multivac (app.themultivac.com)", "methodology": "10x10 blind peer matrix evaluation", "criteria": "correctness, completeness, clarity, depth, usefulness", "self_judgments": "excluded from rankings", "license": "Open data — cite as: The Multivac (2026)" } }