zai-org/GLM-5.1
Text Generation β’ 754B β’ Updated β’ 389 β’ β’ 460
Kosh
Kosh69
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#53 opened 15 days ago
by 
rombodawg
Please train the AIR ββmodel
#73 opened 6 days ago
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useless but fast!
ππ 8
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#21 opened 11 days ago
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ΠΠ΅ΡΡΠΈΠΊΠΈπ€‘
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#2 opened 15 days ago
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ALL Bench β Global AI Model Unified Leaderboard
FINAL-Bench/all-bench-leaderboard
If you've ever tried to compare GPT-5.2 and Claude Opus 4.6 side by side, you've probably hit the same wall: the official Hugging Face leaderboard only tracks open-source models, so the most widely used AI systems simply aren't there. ALL Bench fixes that by bringing closed-source models, open-weight models, and β uniquely β all four teams under South Korea's national sovereign AI program into a single leaderboard. Thirty-one frontier models, one consistent scoring scale.
Scoring works differently here too. Most leaderboards skip benchmarks a model hasn't submitted, which lets models game their ranking by withholding results. ALL Bench treats every missing entry as zero and divides by ten, so there's no advantage in hiding your weak spots.
The ten core benchmarks span reasoning (GPQA Diamond, AIME 2025, HLE, ARC-AGI-2), coding (SWE-bench Verified, LiveCodeBench), and instruction-following (IFEval, BFCL). The standout is FINAL Bench β the world's only benchmark measuring whether a model can catch and correct its own mistakes. It reached rank five in global dataset popularity on Hugging Face in February 2026 and has been covered by Seoul Shinmun, Asia Economy, IT Chosun, and Behind.
Nine interactive charts let you explore everything from composite score rankings and a full heatmap to an open-vs-closed scatter plot. Operational metrics like context window, output speed, and pricing are included alongside benchmark scores.
All data is sourced from Artificial Analysis Intelligence Index v4.0, arXiv technical reports, Chatbot Arena ELO ratings, and the Korean Ministry of Science and ICT's official evaluation results. Updates monthly.
FINAL-Bench/all-bench-leaderboard
If you've ever tried to compare GPT-5.2 and Claude Opus 4.6 side by side, you've probably hit the same wall: the official Hugging Face leaderboard only tracks open-source models, so the most widely used AI systems simply aren't there. ALL Bench fixes that by bringing closed-source models, open-weight models, and β uniquely β all four teams under South Korea's national sovereign AI program into a single leaderboard. Thirty-one frontier models, one consistent scoring scale.
Scoring works differently here too. Most leaderboards skip benchmarks a model hasn't submitted, which lets models game their ranking by withholding results. ALL Bench treats every missing entry as zero and divides by ten, so there's no advantage in hiding your weak spots.
The ten core benchmarks span reasoning (GPQA Diamond, AIME 2025, HLE, ARC-AGI-2), coding (SWE-bench Verified, LiveCodeBench), and instruction-following (IFEval, BFCL). The standout is FINAL Bench β the world's only benchmark measuring whether a model can catch and correct its own mistakes. It reached rank five in global dataset popularity on Hugging Face in February 2026 and has been covered by Seoul Shinmun, Asia Economy, IT Chosun, and Behind.
Nine interactive charts let you explore everything from composite score rankings and a full heatmap to an open-vs-closed scatter plot. Operational metrics like context window, output speed, and pricing are included alongside benchmark scores.
All data is sourced from Artificial Analysis Intelligence Index v4.0, arXiv technical reports, Chatbot Arena ELO ratings, and the Korean Ministry of Science and ICT's official evaluation results. Updates monthly.
liked 5
models about 1 month ago
AesSedai/Qwen3.5-122B-A10B-GGUF
122B β’ Updated β’ 12.8k β’ 40
unsloth/Qwen3.5-122B-A10B-GGUF
Image-Text-to-Text β’ 122B β’ Updated β’ 392k β’ 230
lmstudio-community/Qwen3.5-27B-GGUF
27B β’ Updated β’ 56.2k β’ 15
Qwen/Qwen3.5-27B
Image-Text-to-Text β’ 28B β’ Updated β’ 3.13M β’ β’ 883
Qwen/Qwen3.5-122B-A10B
Image-Text-to-Text β’ 125B β’ Updated β’ 958k β’ β’ 477
liked 4
models about 2 months ago
MiniMaxAI/MiniMax-M2.5
Text Generation β’ 229B β’ Updated β’ 640k β’ β’ 1.36k
unsloth/Qwen3.5-397B-A17B-GGUF
Image-Text-to-Text β’ 396B β’ Updated β’ 97.3k β’ 232
Qwen/Qwen3.5-397B-A17B
Image-Text-to-Text β’ 403B β’ Updated β’ 997k β’ β’ 1.42k
unsloth/MiniMax-M2.5-GGUF
Text Generation β’ 229B β’ Updated β’ 63.1k β’ 219