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reacted to SeaWolf-AI's post with ❤️ about 8 hours ago
🏟️ Smol AI WorldCup: A 4B Model Just Beat 8B — Here's the Data We evaluated 18 small language models from 12 makers on 125 questions across 7 languages. The results challenge the assumption that bigger is always better. Community Article: https://huggingface.co/blog/FINAL-Bench/smol-worldcup Live Leaderboard: https://huggingface.co/spaces/ginigen-ai/smol-worldcup Dataset: https://huggingface.co/datasets/ginigen-ai/smol-worldcup What we found: → Gemma-3n-E4B (4B, 2GB RAM) outscores Qwen3-8B (8B, 5.5GB). Doubling parameters gained only 0.4 points. RAM cost: 2.75x more. → GPT-OSS-20B fits in 1.5GB yet matches Champions-league dense models requiring 8.5GB. MoE architecture is the edge AI game-changer. → Thinking models hurt structured output. DeepSeek-R1-7B scores 8.7 points below same-size Qwen3-8B and runs 2.7x slower. → A 1.3B model fabricates confident fake content 80% of the time when prompted with nonexistent entities. Qwen3 family hits 100% trap detection across all sizes. → Qwen3-1.7B (1.2GB) outscores Mistral-7B, Llama-3.1-8B, and DeepSeek-R1-14B. Latest architecture at 1.7B beats older architecture at 14B. What makes this benchmark different? Most benchmarks ask "how smart?" — we measure five axes simultaneously: Size, Honesty, Intelligence, Fast, Thrift (SHIFT). Our ranking metric WCS = sqrt(SHIFT x PIR_norm) rewards models that are both high-quality AND efficient. Smart but massive? Low rank. Tiny but poor? Also low. Top 5 by WCS: 1. GPT-OSS-20B — WCS 82.6 — 1.5GB — Raspberry Pi tier 2. Gemma-3n-E4B — WCS 81.8 — 2.0GB — Smartphone tier 3. Llama-4-Scout — WCS 79.3 — 240 tok/s — Fastest model 4. Qwen3-4B — WCS 76.6 — 2.8GB — Smartphone tier 5. Qwen3-1.7B — WCS 76.1 — 1.2GB — IoT tier Built in collaboration with the FINAL Bench research team. Interoperable with ALL Bench Leaderboard for full small-to-large model comparison. Dataset is open under Apache 2.0 (125 questions, 7 languages). We welcome new model submissions.
reacted to SeaWolf-AI's post with 👍 about 8 hours ago
🏟️ Smol AI WorldCup: A 4B Model Just Beat 8B — Here's the Data We evaluated 18 small language models from 12 makers on 125 questions across 7 languages. The results challenge the assumption that bigger is always better. Community Article: https://huggingface.co/blog/FINAL-Bench/smol-worldcup Live Leaderboard: https://huggingface.co/spaces/ginigen-ai/smol-worldcup Dataset: https://huggingface.co/datasets/ginigen-ai/smol-worldcup What we found: → Gemma-3n-E4B (4B, 2GB RAM) outscores Qwen3-8B (8B, 5.5GB). Doubling parameters gained only 0.4 points. RAM cost: 2.75x more. → GPT-OSS-20B fits in 1.5GB yet matches Champions-league dense models requiring 8.5GB. MoE architecture is the edge AI game-changer. → Thinking models hurt structured output. DeepSeek-R1-7B scores 8.7 points below same-size Qwen3-8B and runs 2.7x slower. → A 1.3B model fabricates confident fake content 80% of the time when prompted with nonexistent entities. Qwen3 family hits 100% trap detection across all sizes. → Qwen3-1.7B (1.2GB) outscores Mistral-7B, Llama-3.1-8B, and DeepSeek-R1-14B. Latest architecture at 1.7B beats older architecture at 14B. What makes this benchmark different? Most benchmarks ask "how smart?" — we measure five axes simultaneously: Size, Honesty, Intelligence, Fast, Thrift (SHIFT). Our ranking metric WCS = sqrt(SHIFT x PIR_norm) rewards models that are both high-quality AND efficient. Smart but massive? Low rank. Tiny but poor? Also low. Top 5 by WCS: 1. GPT-OSS-20B — WCS 82.6 — 1.5GB — Raspberry Pi tier 2. Gemma-3n-E4B — WCS 81.8 — 2.0GB — Smartphone tier 3. Llama-4-Scout — WCS 79.3 — 240 tok/s — Fastest model 4. Qwen3-4B — WCS 76.6 — 2.8GB — Smartphone tier 5. Qwen3-1.7B — WCS 76.1 — 1.2GB — IoT tier Built in collaboration with the FINAL Bench research team. Interoperable with ALL Bench Leaderboard for full small-to-large model comparison. Dataset is open under Apache 2.0 (125 questions, 7 languages). We welcome new model submissions.
upvoted an article about 8 hours ago
🏟️ Smol AI WorldCup: A 5-Axis Benchmark That Reveals What Small Language Models Can Really Do
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