Global PIQA
Models pick the correct solution to everyday commonsense prompts in over 100 languages, many about local foods and customs. Scoring is accuracy.
The license forbids training AI systems on it or on synthetic data seeded from it. The record covers the non-parallel split of over 100 languages, released in 2025-10. A parallel split of culturally agnostic questions in 131 language varieties followed in 2026-04, and its scores are a different test.
- Released
- October 2025
- Kind
- Reasoning in many languages
- Level
- Everyday
- Scored
- Checked automatically
- Answers
- Multiple choice
- Tasks
- All public
- Strongest model tested
- GPT-5.4 #27 on the capability index
- Last in a launch post
- June 2026, dropped after it saturated
- Last model added
- June 2026
- Scores refreshed
- November 12th, 2026
In the authors’ words
To date, there exist almost no culturally-specific evaluation benchmarks for large language models (LLMs) that cover a large number of languages and cultures. In this paper, we present Global PIQA, a participatory commonsense reasoning benchmark for over 100 languages, constructed by hand by over 350 researchers from over 65 countries around the world. The 141 language varieties in Global PIQA cover five continents, 19 language families, and 24 writing systems. In the non-parallel split of Global PIQA, over 50% of examples reference local foods, customs, traditions, or other culturally-specific elements. In the parallel split, we translate more "culturally agnostic" commonsense reasoning questions into 131 language varieties, for direct cross-lingual comparisons. In both splits, all examples have been verified by native speakers of the languages. We find that state-of-the-art LLMs perform well on Global PIQA in aggregate, but they exhibit weaker performance in lower-resource languages (e.g. up to a 68% accuracy gap between languages in the parallel split). Global PIQA highlights that in many languages and cultures, everyday knowledge remains an area for improvement in LLMs, alongside more widely-discussed capabilities such as complex reasoning and expert knowledge. Beyond its uses for LLM evaluation, Global PIQA provides a glimpse into the wide diversity of cultures in which human language is embedded.
Gemini 3.1 Pro leads with 93.9% among models released by April 2026
Best score per entry from the benchmark's own leaderboard, read on November 12th, 2026. Where a model ran with several settings or harnesses, we keep its best. Names in gray aren't in our model list: fine-tunes, agent systems, products and older models. They don't count toward leaders.
Gemini 3.1 Pro leads with 93.9%, claims included
Best score per model from every source, including labs’ own numbers and claims Epoch AI collected from reports and papers. Labs pick their own settings, so a claim can beat an independent run of the same model.
The newest model with a score here came out in April 2026, and nobody has tested a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:
- Claude Fable 5.2
-
Gemini 4 Carbon
- GPT-6 Astra
- Claude Opus 5.5
- and 16 more
- 93.9
Gemini 3.1 Pro
- 92.0
Gemini 3.1 Flash-Lite
- 91.3 GPT-5.4
- 90.2 Claude Sonnet 4.6
- 89.6 GPT-5.4 Mini
- 85.4 GPT-5.4 Nano
- 84.9
Gemma 4 31B IT
- 81.7
Gemma 3 27B
- 81.2 CohereLabs/c4ai-command-a-03-2025
- 79.8
Gemma 4 E4B
- 79.6 aisingapore/Llama-SEA-LION-v3-70B-IT
- 78.8 Sahabat-AI/Llama-Sahabat-AI-v2-70B-IT
- 78.5
Qwen3.5-27B
- 78.3
Qwen2.5-72B
- 78.1 google/gemma-3-12b-it
- 77.8 gpt-oss-20b
- 77.1
Qwen3-14B
- 76.2 meta-llama/Llama-3.1-70B-Instruct
- 76.0
Qwen3-32B
- 75.2 Qwen/Qwen2.5-32B-Instruct
- 74.3
Apertus 70B
- 74.2
Phi-4
- 74.0
Qwen3-8B
- 73.9 aisingapore/Gemma-SEA-LION-v3-9B-IT
- 73.5
Qwen3.5-9B
- 73.4 google/gemma-2-27b-it
- 73.4 Qwen/Qwen2.5-14B-Instruct
- 72.2
DeepSeek-R1-Distill-Qwen-32B
- 72.1
Gemma 4 E2B
- 71.0
Aya Expanse 32B
- 70.7
DeepSeek-R1-Distill-Qwen-14B
- 70.5
Qwen3-4B
- 69.5 gpt-oss-120b
- 68.9 c4ai-command-r-08-2024
- 68.6 sail/Sailor2-20B-Chat
- 68.3 google/gemma-2-9b-it
- 67.6 google/gemma-3-4b-it
- 67.3 google/gemma-3-27b-pt (base)
- 67.0
Apertus 8B Instruct
- 66.8 aisingapore/Llama-SEA-LION-v3-8B-IT
- 66.5 Qwen/Qwen2.5-7B-Instruct
- 65.8 CohereLabs/c4ai-command-r-plus-08-2024
- 65.3 swiss-ai/Apertus-70B-2509 (base)
- 64.8 google/gemma-4-E4B (base)
- 64.2 google/gemma-3-12b-pt (base)
- 64.0 meta-llama/Llama-3.1-70B (base)
- 63.9
Aya Expanse 8B
- 63.4 google/gemma-2-27b (base)
- 63.2 LumiOpen/Llama-Poro-2-8B-Instruct
- 62.8 ilsp/Llama-Krikri-8B-Instruct
- 62.6 Qwen/Qwen3.5-9B-Base (base)
- 62.5 Tower-Babel/Babel-83B-Chat
- 62.5 LumiOpen/Llama-Poro-2-70B-base (base)
- 62.4
EXAONE-3.5-32B-Instruct
- 62.1
Qwen3.5-4B
- 61.9 swiss-ai/Apertus-8B-2509 (base)
- 61.8 sail/Sailor2-8B-Chat
- 61.7 aisingapore/Gemma-SEA-LION-v3-9B (base)
- 61.0 meta-llama/Llama-3.1-8B-Instruct
- 60.8
Qwen3-1.7B
- 60.7
EXAONE-3.5-7.8B-Instruct
- 60.5 google/gemma-2-9b (base)
- 60.2 google/gemma-4-E2B (base)
- 60.2 Qwen/Qwen2.5-72B (base)
- 60.2 Qwen/Qwen2.5-3B-Instruct
- 60.1 kakaocorp/kanana-1.5-8b-instruct-2505
- 60.1 utter-project/EuroLLM-9B-Instruct
- 59.5
phi-3-medium 14B
- 59.5 Qwen/Qwen3.5-4B-Base (base)
- 59.5
Tiny Aya Global
- 59.0
Command R7B
- 58.9
Qwen3.5-2B
- 58.8 google/gemma-3-4b-pt (base)
- 58.8 SeaLLMs/SeaLLMs-v3-7B-Chat
- 58.5 microsoft/Phi-4-mini-instruct
- 58.5 Tower-Babel/Babel-9B-Chat
- 58.4
Gemma 2 2B IT
- 58.0 aisingapore/Llama-SEA-LION-v3-8B (base)
- 58.0 CohereLabs/tiny-aya-base (base)
- 57.7 Qwen/Qwen2.5-32B (base)
- 56.9 microsoft/Phi-3.5-mini-instruct
- 56.9 meta-llama/Llama-3.1-8B (base)
- 56.9 utter-project/EuroLLM-9B (base)
- 56.4 Qwen/Qwen2.5-14B (base)
- 56.4 sail/Sailor2-20B (base)
- 56.2 mistralai/Mixtral-8x7B-v0.1 (base)
- 56.0 BSC-LT/salamandra-7b (base)
- 55.5
DeepSeek-R1-Distill-Qwen-7B
- 55.2
phi-3-mini 3.8B
- 54.7 google/gemma-3-1b-pt (base)
- 54.7 Tower-Babel/Babel-9B (base)
- 54.6 google/gemma-3-1b-it
- 54.5 mistralai/Mistral-7B-Instruct-v0.3
- 54.5 mistralai/Mistral-Small-Instruct-2409
- 54.5 Qwen/Qwen3.5-2B-Base (base)
- 54.3 LumiOpen/Llama-Poro-2-8B-base (base)
- 54.0 ilsp/Llama-Krikri-8B-Base (base)
- 54.0 LumiOpen/Viking-33B (base)
- 53.9
Qwen3-0.6B
- 53.8 Qwen/Qwen2.5-1.5B-Instruct
- 53.6 meta-llama/Llama-3.2-3B (base)
- 53.6 tiiuae/falcon-40b (base)
- 53.6
Mistral-7B-Instruct-v0.1
- 53.6 Qwen/Qwen2.5-7B (base)
- 53.5 facebook/xglm-4.5B (base)
- 53.4 SeaLLMs/SeaLLMs-v3-7B (base)
- 53.4 Unbabel/TowerBase-13B-v0.1 (base)
- 53.3 mistralai/Mistral-7B-v0.1 (base)
- 53.3 bertin-project/Gromenauer-7B (base)
- 53.3 meta-llama/Llama-3.2-1B-Instruct
- 53.0 BSC-LT/salamandra-2b (base)
- 52.9 sail/Sailor2-8B (base)
- 52.6 Unbabel/TowerInstruct-13B-v0.1
- 52.6 LumiOpen/Viking-13B (base)
- 52.5
Qwen3.5-0.8B
- 52.4 bigscience/bloom-7b1 (base)
- 52.3 Unbabel/TowerInstruct-7B-v0.1
- 52.1 CraneAILabs/swahili-gemma-1b (base)
- 52.0 Unbabel/TowerBase-7B-v0.1 (base)
- 51.8 Qwen/Qwen3.5-0.8B-Base (base)
- 51.7 Qwen/Qwen2.5-3B (base)
- 51.7 CraneAILabs/ganda-gemma-1b (base)
- 51.7 ilsp/Meltemi-7B-v1.5 (base)
- 51.6 bigscience/bloom-3b (base)
- 51.6 SeaLLMs/SeaLLMs-v3-1.5B-Chat
- 51.5 LGAI-EXAONE/EXAONE-4.0-1.2B
- 51.2 meta-llama/Llama-3.2-3B-Instruct
- 51.2 LumiOpen/Viking-7B (base)
- 51.1
EXAONE 4.0 32B
- 51.0 facebook/xglm-2.9B (base)
- 50.9 bigscience/bloom-1b7 (base)
- 50.8 facebook/xglm-7.5B (base)
- 50.7 kakaocorp/kanana-1.5-8b-base (base)
- 50.3 facebook/xglm-1.7B (base)
- 50.3 Yellow-AI-NLP/komodo-7b-base (base)
- 50.0 bigscience/bloom-1b1 (base)
- 49.9 vilm/vinallama-7b-chat
- 49.9 Qwen/Qwen2.5-1.5B (base)
- 49.8 tiiuae/falcon-7b (base)
- 49.8 speakleash/Bielik-4.5B-v3 (base)
- 49.8 google/gemma-3-270m (base)
- 49.7 bigscience/bloom-560m (base)
- 49.6 Qwen/Qwen2.5-0.5B-Instruct
- 49.6 AI-Sweden-Models/gpt-sw3-6.7b-v2 (base)
- 49.6 SeaLLMs/SeaLLMs-v3-1.5B (base)
- 49.4 Gen2B/HyGPT-10b (base)
- 49.2 facebook/xglm-564M (base)
- 49.1 AI-Sweden-Models/gpt-sw3-1.3b (base)
- 49.1 vilm/vinallama-7b (base)
- 49.1 speakleash/Bielik-1.5B-v3 (base)
- 49.0 naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B
- 48.8 universitytehran/PersianMind-v1.0 (base)
- 48.8 sapienzanlp/Minerva-7B-base-v1.0 (base)
- 48.8 croissantllm/CroissantLLMBase (base)
- 48.7 sail/Sailor2-1B (base)
- 48.5 vilm/vinallama-2.7b (base)
- 48.0 TucanoBR/Tucano-2b4 (base)
- 48.0 TucanoBR/Tucano-1b1 (base)
- 47.9 sapienzanlp/Minerva-3B-base-v1.0 (base)
- 47.7 sapienzanlp/Minerva-1B-base-v1.0 (base)
- 47.5 sail/Sailor2-1B-Chat
- 47.4 lelapa/InkubaLM-0.4B (base)
- 47.3 Azurro/APT3-1B-Base (base)
- 46.5 ilsp/Meltemi-7B-Instruct-v1.5
- 27.9
DeepSeek-R1-Distill-Qwen-1.5B
- 21.0 AI-Sweden-Models/gpt-sw3-20b-instruct
- 19.4 BSC-LT/salamandra-7b-instruct
- 19.1 BSC-LT/salamandra-2b-instruct
- 15.8 google/gemma-3-270m-it
- 13.1 croissantllm/CroissantLLMChat-v0.1
- 12.3 sapienzanlp/Minerva-7B-instruct-v1.0
- 8.08 TucanoBR/Tucano-1b1-Instruct
- 5.79 AI-Sweden-Models/gpt-sw3-6.7b-v2-instruct
- 1.26 LumiOpen/Llama-Poro-2-70B-Instruct
- 0.62 AI-Sweden-Models/gpt-sw3-1.3b-instruct
- 0.13 Sarvam-M
- 0.10 TucanoBR/Tucano-2b4-Instruct
- 1
Gemini 3.1 Pro exact_match; instruct 93.9%
- 2
Gemini 3.1 Flash-Lite exact_match; instruct 92%
- 3 GPT-5.4 exact_match; instruct 91.3%
- 4 Claude Sonnet 4.6 exact_match; instruct 90.2%
- 5 GPT-5.4 Mini exact_match; instruct 89.6%
- 6 GPT-5.4 Nano exact_match; instruct 85.4%
- 7
Gemma 4 31B IT exact_match,strict_match; instruct 84.9%
- 8
Gemma 3 27B exact_match,strict_match; instruct 81.7%
- 9 CohereLabs/c4ai-command-a-03-2025 exact_match,strict_match; instruct 81.2%
- 10
Gemma 4 E4B exact_match,strict_match; instruct 79.8%
- 11 aisingapore/Llama-SEA-LION-v3-70B-IT exact_match,strict_match; instruct 79.6%
- 12 Sahabat-AI/Llama-Sahabat-AI-v2-70B-IT exact_match,strict_match; instruct 78.8%
- 13
Qwen3.5-27B exact_match,strict_match; instruct 78.5%
- 14
Qwen2.5-72B exact_match,strict_match; instruct 78.3%
- 15 google/gemma-3-12b-it exact_match,strict_match; instruct 78.1%
- 16 gpt-oss-20b exact_match,strict_match; instruct 77.8%
- 17
Qwen3-14B exact_match,strict_match; instruct 77.1%
- 18 meta-llama/Llama-3.1-70B-Instruct exact_match,strict_match; instruct 76.2%
- 19
Qwen3-32B exact_match,strict_match; instruct 76%
- 20 Qwen/Qwen2.5-32B-Instruct exact_match,strict_match; instruct 75.2%
- 21
Apertus 70B exact_match,strict_match; instruct 74.3%
- 22
Phi-4 exact_match,strict_match; instruct 74.2%
- 23
Qwen3-8B exact_match,strict_match; instruct 74%
- 24 aisingapore/Gemma-SEA-LION-v3-9B-IT exact_match,strict_match; instruct 73.9%
- 25
Qwen3.5-9B exact_match,strict_match; instruct 73.5%
- 26 google/gemma-2-27b-it exact_match,strict_match; instruct 73.4%
- 27 Qwen/Qwen2.5-14B-Instruct exact_match,strict_match; instruct 73.4%
- 28
DeepSeek-R1-Distill-Qwen-32B exact_match,strict_match; instruct 72.2%
- 29
Gemma 4 E2B exact_match,strict_match; instruct 72.1%
- 30
Aya Expanse 32B exact_match,strict_match; instruct 71%
- 31
DeepSeek-R1-Distill-Qwen-14B exact_match,strict_match; instruct 70.7%
- 32
Qwen3-4B exact_match,strict_match; instruct 70.5%
- 33 gpt-oss-120b exact_match,strict_match; instruct 69.5%
- 34 c4ai-command-r-08-2024 exact_match,strict_match; instruct 68.9%
- 35 sail/Sailor2-20B-Chat exact_match,strict_match; instruct 68.6%
- 36 google/gemma-2-9b-it exact_match,strict_match; instruct 68.3%
- 37 google/gemma-3-4b-it exact_match,strict_match; instruct 67.6%
- 38 google/gemma-3-27b-pt (base) acc,none; base 67.3%
- 39
Apertus 8B Instruct exact_match,strict_match; instruct 67%
- 40 aisingapore/Llama-SEA-LION-v3-8B-IT exact_match,strict_match; instruct 66.8%
- 41 Qwen/Qwen2.5-7B-Instruct exact_match,strict_match; instruct 66.5%
- 42 CohereLabs/c4ai-command-r-plus-08-2024 exact_match,strict_match; instruct 65.8%
- 43 swiss-ai/Apertus-70B-2509 (base) acc,none; base 65.3%
- 44 google/gemma-4-E4B (base) acc,none; base 64.8%
- 45 google/gemma-3-12b-pt (base) acc,none; base 64.2%
- 46 meta-llama/Llama-3.1-70B (base) acc,none; base 64%
- 47
Aya Expanse 8B exact_match,strict_match; instruct 63.9%
- 48 google/gemma-2-27b (base) acc,none; base 63.4%
- 49 LumiOpen/Llama-Poro-2-8B-Instruct exact_match,strict_match; instruct 63.2%
- 50 ilsp/Llama-Krikri-8B-Instruct exact_match,strict_match; instruct 62.8%
- 51 Qwen/Qwen3.5-9B-Base (base) acc,none; base 62.6%
- 52 Tower-Babel/Babel-83B-Chat exact_match,strict_match; instruct 62.5%
- 53 LumiOpen/Llama-Poro-2-70B-base (base) acc,none; base 62.5%
- 54
EXAONE-3.5-32B-Instruct exact_match,strict_match; instruct 62.4%
- 55
Qwen3.5-4B exact_match,strict_match; instruct 62.1%
- 56 swiss-ai/Apertus-8B-2509 (base) acc,none; base 61.9%
- 57 sail/Sailor2-8B-Chat exact_match,strict_match; instruct 61.8%
- 58 aisingapore/Gemma-SEA-LION-v3-9B (base) acc,none; base 61.7%
- 59 meta-llama/Llama-3.1-8B-Instruct exact_match,strict_match; instruct 61%
- 60
Qwen3-1.7B exact_match,strict_match; instruct 60.8%
- 61
EXAONE-3.5-7.8B-Instruct exact_match,strict_match; instruct 60.7%
- 62 google/gemma-2-9b (base) acc,none; base 60.5%
- 63 google/gemma-4-E2B (base) acc,none; base 60.2%
- 64 Qwen/Qwen2.5-72B (base) acc,none; base 60.2%
- 65 Qwen/Qwen2.5-3B-Instruct exact_match,strict_match; instruct 60.2%
- 66 kakaocorp/kanana-1.5-8b-instruct-2505 exact_match,strict_match; instruct 60.1%
- 67 utter-project/EuroLLM-9B-Instruct exact_match,strict_match; instruct 60.1%
- 68
phi-3-medium 14B exact_match,strict_match; instruct 59.5%
- 69 Qwen/Qwen3.5-4B-Base (base) acc,none; base 59.5%
- 70
Tiny Aya Global exact_match,strict_match; instruct 59.5%
- 71
Command R7B exact_match,strict_match; instruct 59%
- 72
Qwen3.5-2B exact_match,strict_match; instruct 58.9%
- 73 google/gemma-3-4b-pt (base) acc,none; base 58.8%
- 74 SeaLLMs/SeaLLMs-v3-7B-Chat exact_match,strict_match; instruct 58.8%
- 75 microsoft/Phi-4-mini-instruct exact_match,strict_match; instruct 58.5%
- 76 Tower-Babel/Babel-9B-Chat exact_match,strict_match; instruct 58.5%
- 77
Gemma 2 2B IT exact_match,strict_match; instruct 58.4%
- 78 aisingapore/Llama-SEA-LION-v3-8B (base) acc,none; base 58%
- 79 CohereLabs/tiny-aya-base (base) acc,none; base 58%
- 80 Qwen/Qwen2.5-32B (base) acc,none; base 57.7%
- 81 microsoft/Phi-3.5-mini-instruct exact_match,strict_match; instruct 56.9%
- 82 meta-llama/Llama-3.1-8B (base) acc,none; base 56.9%
- 83 utter-project/EuroLLM-9B (base) acc,none; base 56.9%
- 84 Qwen/Qwen2.5-14B (base) acc,none; base 56.4%
- 85 sail/Sailor2-20B (base) acc,none; base 56.4%
- 86 mistralai/Mixtral-8x7B-v0.1 (base) acc,none; base 56.2%
- 87 BSC-LT/salamandra-7b (base) acc,none; base 56%
- 88
DeepSeek-R1-Distill-Qwen-7B exact_match,strict_match; instruct 55.5%
- 89
phi-3-mini 3.8B exact_match,strict_match; instruct 55.2%
- 90 google/gemma-3-1b-pt (base) acc,none; base 54.7%
- 91 Tower-Babel/Babel-9B (base) acc,none; base 54.7%
- 92 google/gemma-3-1b-it exact_match,strict_match; instruct 54.6%
- 93 mistralai/Mistral-7B-Instruct-v0.3 exact_match,strict_match; instruct 54.5%
- 94 mistralai/Mistral-Small-Instruct-2409 exact_match,strict_match; instruct 54.5%
- 95 Qwen/Qwen3.5-2B-Base (base) acc,none; base 54.5%
- 96 LumiOpen/Llama-Poro-2-8B-base (base) acc,none; base 54.3%
- 97 ilsp/Llama-Krikri-8B-Base (base) acc,none; base 54%
- 98 LumiOpen/Viking-33B (base) acc,none; base 54%
- 99
Qwen3-0.6B exact_match,strict_match; instruct 53.9%
- 100 Qwen/Qwen2.5-1.5B-Instruct exact_match,strict_match; instruct 53.8%
- 101 meta-llama/Llama-3.2-3B (base) acc,none; base 53.6%
- 102 tiiuae/falcon-40b (base) acc,none; base 53.6%
- 103
Mistral-7B-Instruct-v0.1 exact_match,strict_match; instruct 53.6%
- 104 Qwen/Qwen2.5-7B (base) acc,none; base 53.6%
- 105 facebook/xglm-4.5B (base) acc,none; base 53.5%
- 106 SeaLLMs/SeaLLMs-v3-7B (base) acc,none; base 53.4%
- 107 Unbabel/TowerBase-13B-v0.1 (base) acc,none; base 53.4%
- 108 mistralai/Mistral-7B-v0.1 (base) acc,none; base 53.3%
- 109 bertin-project/Gromenauer-7B (base) acc,none; base 53.3%
- 110 meta-llama/Llama-3.2-1B-Instruct exact_match,strict_match; instruct 53.3%
- 111 BSC-LT/salamandra-2b (base) acc,none; base 53%
- 112 sail/Sailor2-8B (base) acc,none; base 52.9%
- 113 Unbabel/TowerInstruct-13B-v0.1 exact_match,strict_match; instruct 52.6%
- 114 LumiOpen/Viking-13B (base) acc,none; base 52.6%
- 115
Qwen3.5-0.8B exact_match,strict_match; instruct 52.5%
- 116 bigscience/bloom-7b1 (base) acc,none; base 52.4%
- 117 Unbabel/TowerInstruct-7B-v0.1 exact_match,strict_match; instruct 52.3%
- 118 CraneAILabs/swahili-gemma-1b (base) acc,none; base 52.1%
- 119 Unbabel/TowerBase-7B-v0.1 (base) acc,none; base 52%
- 120 Qwen/Qwen3.5-0.8B-Base (base) acc,none; base 51.8%
- 121 Qwen/Qwen2.5-3B (base) acc,none; base 51.7%
- 122 CraneAILabs/ganda-gemma-1b (base) acc,none; base 51.7%
- 123 ilsp/Meltemi-7B-v1.5 (base) acc,none; base 51.7%
- 124 bigscience/bloom-3b (base) acc,none; base 51.6%
- 125 SeaLLMs/SeaLLMs-v3-1.5B-Chat exact_match,strict_match; instruct 51.6%
- 126 LGAI-EXAONE/EXAONE-4.0-1.2B exact_match,strict_match; instruct 51.5%
- 127 meta-llama/Llama-3.2-3B-Instruct exact_match,strict_match; instruct 51.2%
- 128 LumiOpen/Viking-7B (base) acc,none; base 51.2%
- 129
EXAONE 4.0 32B exact_match,strict_match; instruct 51.1%
- 130 facebook/xglm-2.9B (base) acc,none; base 51%
- 131 bigscience/bloom-1b7 (base) acc,none; base 50.9%
- 132 facebook/xglm-7.5B (base) acc,none; base 50.8%
- 133 kakaocorp/kanana-1.5-8b-base (base) acc,none; base 50.7%
- 134 facebook/xglm-1.7B (base) acc,none; base 50.3%
- 135 Yellow-AI-NLP/komodo-7b-base (base) acc,none; base 50.3%
- 136 bigscience/bloom-1b1 (base) acc,none; base 50%
- 137 vilm/vinallama-7b-chat exact_match,strict_match; instruct 49.9%
- 138 Qwen/Qwen2.5-1.5B (base) acc,none; base 49.9%
- 139 tiiuae/falcon-7b (base) acc,none; base 49.8%
- 140 speakleash/Bielik-4.5B-v3 (base) acc,none; base 49.8%
- 141 google/gemma-3-270m (base) acc,none; base 49.8%
- 142 bigscience/bloom-560m (base) acc,none; base 49.7%
- 143 Qwen/Qwen2.5-0.5B-Instruct exact_match,strict_match; instruct 49.6%
- 144 AI-Sweden-Models/gpt-sw3-6.7b-v2 (base) acc,none; base 49.6%
- 145 SeaLLMs/SeaLLMs-v3-1.5B (base) acc,none; base 49.6%
- 146 Gen2B/HyGPT-10b (base) acc,none; base 49.4%
- 147 facebook/xglm-564M (base) acc,none; base 49.2%
- 148 AI-Sweden-Models/gpt-sw3-1.3b (base) acc,none; base 49.1%
- 149 vilm/vinallama-7b (base) acc,none; base 49.1%
- 150 speakleash/Bielik-1.5B-v3 (base) acc,none; base 49.1%
- 151 naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B exact_match,strict_match; instruct 49%
- 152 universitytehran/PersianMind-v1.0 (base) acc,none; base 48.8%
- 153 sapienzanlp/Minerva-7B-base-v1.0 (base) acc,none; base 48.8%
- 154 croissantllm/CroissantLLMBase (base) acc,none; base 48.8%
- 155 sail/Sailor2-1B (base) acc,none; base 48.7%
- 156 vilm/vinallama-2.7b (base) acc,none; base 48.5%
- 157 TucanoBR/Tucano-2b4 (base) acc,none; base 48%
- 158 TucanoBR/Tucano-1b1 (base) acc,none; base 48%
- 159 sapienzanlp/Minerva-3B-base-v1.0 (base) acc,none; base 47.9%
- 160 sapienzanlp/Minerva-1B-base-v1.0 (base) acc,none; base 47.7%
- 161 sail/Sailor2-1B-Chat exact_match,strict_match; instruct 47.5%
- 162 lelapa/InkubaLM-0.4B (base) acc,none; base 47.4%
- 163 Azurro/APT3-1B-Base (base) acc,none; base 47.3%
- 164 ilsp/Meltemi-7B-Instruct-v1.5 exact_match,strict_match; instruct 46.5%
- 165
DeepSeek-R1-Distill-Qwen-1.5B exact_match,strict_match; instruct 27.9%
- 166 AI-Sweden-Models/gpt-sw3-20b-instruct exact_match,strict_match; instruct 21%
- 167 BSC-LT/salamandra-7b-instruct exact_match,strict_match; instruct 19.4%
- 168 BSC-LT/salamandra-2b-instruct exact_match,strict_match; instruct 19.1%
- 169 google/gemma-3-270m-it exact_match,strict_match; instruct 15.8%
- 170 croissantllm/CroissantLLMChat-v0.1 exact_match,strict_match; instruct 13.1%
- 171 sapienzanlp/Minerva-7B-instruct-v1.0 exact_match,strict_match; instruct 12.3%
- 172 TucanoBR/Tucano-1b1-Instruct exact_match,strict_match; instruct 8.08%
- 173 AI-Sweden-Models/gpt-sw3-6.7b-v2-instruct exact_match,strict_match; instruct 5.79%
- 174 LumiOpen/Llama-Poro-2-70B-Instruct exact_match,strict_match; instruct 1.26%
- 175 AI-Sweden-Models/gpt-sw3-1.3b-instruct exact_match,strict_match; instruct 0.62%
- 176 Sarvam-M exact_match,strict_match; instruct 0.13%
- 177 TucanoBR/Tucano-2b4-Instruct exact_match,strict_match; instruct 0.10%
Source: Official Global PIQA leaderboard
- 93.9
Gemini 3.1 Pro
- 93.4
Gemini 3 Pro
- 92.8
Gemini 3 Flash
- 92.3
Seed2.0 Pro
- 92.0
Gemini 3.1 Flash-Lite
- 91.4
Qwen3.7-Max
- 91.3 GPT-5.4
- 90.3
Qwen3.7-Plus
- 90.2 Claude Sonnet 4.6
- 89.8
Qwen3.5 397B-A17B
- 89.8
Qwen 3.6 Plus
- 89.6 GPT-5.4 Mini
- 88.4
Qwen3.5-122B-A10B
- 87.5
Qwen3.5-27B
- 86.6
Qwen3.5-35B-A3B
- 85.4 GPT-5.4 Nano
- 84.9
Gemma 4 31B IT
- 83.2
Qwen3.5-9B
- 81.7
Gemma 3 27B
- 79.8
Gemma 4 E4B
- 78.9
Qwen3.5-4B
- 78.3
Qwen2.5-72B
- 77.8 gpt-oss-20b
- 77.1
Qwen3-14B
- 76.0
Qwen3-32B
- 74.3
Apertus 70B
- 74.2
Phi-4
- 74.0
Qwen3-8B
- 72.2
DeepSeek-R1-Distill-Qwen-32B
- 72.1
Gemma 4 E2B
- 71.0
Aya Expanse 32B
- 70.7
DeepSeek-R1-Distill-Qwen-14B
- 70.5
Qwen3-4B
- 69.5 gpt-oss-120b
- 69.3
Qwen3.5-2B
- 68.9 c4ai-command-r-08-2024
- 67.0
Apertus 8B Instruct
- 63.9
Aya Expanse 8B
- 62.4
EXAONE-3.5-32B-Instruct
- 60.8
Qwen3-1.7B
- 60.7
EXAONE-3.5-7.8B-Instruct
- 59.5
phi-3-medium 14B
- 59.5
Tiny Aya Global
- 59.4
Qwen3.5-0.8B
- 59.0
Command R7B
- 58.4
Gemma 2 2B IT
- 55.5
DeepSeek-R1-Distill-Qwen-7B
- 55.2
phi-3-mini 3.8B
- 53.9
Qwen3-0.6B
- 53.6
Mistral-7B-Instruct-v0.1
- 51.1
EXAONE 4.0 32B
- 27.9
DeepSeek-R1-Distill-Qwen-1.5B
- 0.13 Sarvam-M
- 1
Gemini 3.1 Pro exact_match; instruct 93.9%
- 2
Gemini 3 Pro 93.4%
- 3
Gemini 3 Flash 92.8%
- 4
Seed2.0 Pro 92.3%
- 5
Gemini 3.1 Flash-Lite exact_match; instruct 92%
- 6
Qwen3.7-Max 91.4%
- 7 GPT-5.4 exact_match; instruct 91.3%
- 8
Qwen3.7-Plus 90.3%
- 9 Claude Sonnet 4.6 exact_match; instruct 90.2%
- 10
Qwen3.5 397B-A17B 89.8%
- 11
Qwen 3.6 Plus 89.8%
- 12 GPT-5.4 Mini exact_match; instruct 89.6%
- 13
Qwen3.5-122B-A10B 88.4%
- 14
Qwen3.5-27B 87.5%
- 15
Qwen3.5-35B-A3B 86.6%
- 16 GPT-5.4 Nano exact_match; instruct 85.4%
- 17
Gemma 4 31B IT exact_match,strict_match; instruct 84.9%
- 18
Qwen3.5-9B 83.2%
- 19
Gemma 3 27B exact_match,strict_match; instruct 81.7%
- 20
Gemma 4 E4B exact_match,strict_match; instruct 79.8%
- 21
Qwen3.5-4B 78.9%
- 22
Qwen2.5-72B exact_match,strict_match; instruct 78.3%
- 23 gpt-oss-20b exact_match,strict_match; instruct 77.8%
- 24
Qwen3-14B exact_match,strict_match; instruct 77.1%
- 25
Qwen3-32B exact_match,strict_match; instruct 76%
- 26
Apertus 70B exact_match,strict_match; instruct 74.3%
- 27
Phi-4 exact_match,strict_match; instruct 74.2%
- 28
Qwen3-8B exact_match,strict_match; instruct 74%
- 29
DeepSeek-R1-Distill-Qwen-32B exact_match,strict_match; instruct 72.2%
- 30
Gemma 4 E2B exact_match,strict_match; instruct 72.1%
- 31
Aya Expanse 32B exact_match,strict_match; instruct 71%
- 32
DeepSeek-R1-Distill-Qwen-14B exact_match,strict_match; instruct 70.7%
- 33
Qwen3-4B exact_match,strict_match; instruct 70.5%
- 34 gpt-oss-120b exact_match,strict_match; instruct 69.5%
- 35
Qwen3.5-2B 69.3%
- 36 c4ai-command-r-08-2024 exact_match,strict_match; instruct 68.9%
- 37
Apertus 8B Instruct exact_match,strict_match; instruct 67%
- 38
Aya Expanse 8B exact_match,strict_match; instruct 63.9%
- 39
EXAONE-3.5-32B-Instruct exact_match,strict_match; instruct 62.4%
- 40
Qwen3-1.7B exact_match,strict_match; instruct 60.8%
- 41
EXAONE-3.5-7.8B-Instruct exact_match,strict_match; instruct 60.7%
- 42
phi-3-medium 14B exact_match,strict_match; instruct 59.5%
- 43
Tiny Aya Global exact_match,strict_match; instruct 59.5%
- 44
Qwen3.5-0.8B 59.4%
- 45
Command R7B exact_match,strict_match; instruct 59%
- 46
Gemma 2 2B IT exact_match,strict_match; instruct 58.4%
- 47
DeepSeek-R1-Distill-Qwen-7B exact_match,strict_match; instruct 55.5%
- 48
phi-3-mini 3.8B exact_match,strict_match; instruct 55.2%
- 49
Qwen3-0.6B exact_match,strict_match; instruct 53.9%
- 50
Mistral-7B-Instruct-v0.1 exact_match,strict_match; instruct 53.6%
- 51
EXAONE 4.0 32B exact_match,strict_match; instruct 51.1%
- 52
DeepSeek-R1-Distill-Qwen-1.5B exact_match,strict_match; instruct 27.9%
- 53 Sarvam-M exact_match,strict_match; instruct 0.13%
Sources: Official Global PIQA leaderboard and labs’ model cards
| # | Entry | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 93.9% | exact_match; instruct | Feb 2026 | Official leaderboard |
| 2 | | 92% | exact_match; instruct | Mar 2026 | Official leaderboard |
| 3 | GPT-5.4 | 91.3% | exact_match; instruct | Mar 2026 | Official leaderboard |
| 4 | Claude Sonnet 4.6 | 90.2% | exact_match; instruct | Feb 2026 | Official leaderboard |
| 5 | GPT-5.4 Mini | 89.6% | exact_match; instruct | Mar 2026 | Official leaderboard |
| 6 | GPT-5.4 Nano | 85.4% | exact_match; instruct | Mar 2026 | Official leaderboard |
| 7 | | 84.9% | exact_match,strict_match; instruct | Apr 2026 | Official leaderboard |
| 8 | | 81.7% | exact_match,strict_match; instruct | Mar 2025 | Official leaderboard |
| 9 | CohereLabs/c4ai-command-a-03-2025 | 81.2% | exact_match,strict_match; instruct | Official leaderboard | |
| 10 | | 79.8% | exact_match,strict_match; instruct | Mar 2026 | Official leaderboard |
| 11 | aisingapore/Llama-SEA-LION-v3-70B-IT | 79.6% | exact_match,strict_match; instruct | Official leaderboard | |
| 12 | Sahabat-AI/Llama-Sahabat-AI-v2-70B-IT | 78.8% | exact_match,strict_match; instruct | Official leaderboard | |
| 13 | | 78.5% | exact_match,strict_match; instruct | Feb 2026 | Official leaderboard |
| 14 | | 78.3% | exact_match,strict_match; instruct | Sep 2024 | Official leaderboard |
| 15 | google/gemma-3-12b-it | 78.1% | exact_match,strict_match; instruct | Official leaderboard | |
| 16 | gpt-oss-20b | 77.8% | exact_match,strict_match; instruct | Aug 2025 | Official leaderboard |
| 17 | | 77.1% | exact_match,strict_match; instruct | Apr 2025 | Official leaderboard |
| 18 | meta-llama/Llama-3.1-70B-Instruct | 76.2% | exact_match,strict_match; instruct | Official leaderboard | |
| 19 | | 76% | exact_match,strict_match; instruct | Apr 2025 | Official leaderboard |
| 20 | Qwen/Qwen2.5-32B-Instruct | 75.2% | exact_match,strict_match; instruct | Official leaderboard | |
| 21 | | 74.3% | exact_match,strict_match; instruct | Sep 2025 | Official leaderboard |
| 22 | | 74.2% | exact_match,strict_match; instruct | Dec 2024 | Official leaderboard |
| 23 | | 74% | exact_match,strict_match; instruct | Apr 2025 | Official leaderboard |
| 24 | aisingapore/Gemma-SEA-LION-v3-9B-IT | 73.9% | exact_match,strict_match; instruct | Official leaderboard | |
| 25 | | 73.5% | exact_match,strict_match; instruct | Feb 2026 | Official leaderboard |
| 26 | google/gemma-2-27b-it | 73.4% | exact_match,strict_match; instruct | Official leaderboard | |
| 27 | Qwen/Qwen2.5-14B-Instruct | 73.4% | exact_match,strict_match; instruct | Official leaderboard | |
| 28 | | 72.2% | exact_match,strict_match; instruct | Jan 2025 | Official leaderboard |
| 29 | | 72.1% | exact_match,strict_match; instruct | Mar 2026 | Official leaderboard |
| 30 | | 71% | exact_match,strict_match; instruct | Oct 2024 | Official leaderboard |
| 31 | | 70.7% | exact_match,strict_match; instruct | Jan 2025 | Official leaderboard |
| 32 | | 70.5% | exact_match,strict_match; instruct | Apr 2025 | Official leaderboard |
| 33 | gpt-oss-120b | 69.5% | exact_match,strict_match; instruct | Aug 2025 | Official leaderboard |
| 34 | c4ai-command-r-08-2024 | 68.9% | exact_match,strict_match; instruct | Aug 2024 | Official leaderboard |
| 35 | sail/Sailor2-20B-Chat | 68.6% | exact_match,strict_match; instruct | Official leaderboard | |
| 36 | google/gemma-2-9b-it | 68.3% | exact_match,strict_match; instruct | Official leaderboard | |
| 37 | google/gemma-3-4b-it | 67.6% | exact_match,strict_match; instruct | Official leaderboard | |
| 38 | google/gemma-3-27b-pt (base) | 67.3% | acc,none; base | Official leaderboard | |
| 39 | | 67% | exact_match,strict_match; instruct | Sep 2025 | Official leaderboard |
| 40 | aisingapore/Llama-SEA-LION-v3-8B-IT | 66.8% | exact_match,strict_match; instruct | Official leaderboard | |
| 41 | Qwen/Qwen2.5-7B-Instruct | 66.5% | exact_match,strict_match; instruct | Official leaderboard | |
| 42 | CohereLabs/c4ai-command-r-plus-08-2024 | 65.8% | exact_match,strict_match; instruct | Official leaderboard | |
| 43 | swiss-ai/Apertus-70B-2509 (base) | 65.3% | acc,none; base | Official leaderboard | |
| 44 | google/gemma-4-E4B (base) | 64.8% | acc,none; base | Official leaderboard | |
| 45 | google/gemma-3-12b-pt (base) | 64.2% | acc,none; base | Official leaderboard | |
| 46 | meta-llama/Llama-3.1-70B (base) | 64% | acc,none; base | Official leaderboard | |
| 47 | | 63.9% | exact_match,strict_match; instruct | Oct 2024 | Official leaderboard |
| 48 | google/gemma-2-27b (base) | 63.4% | acc,none; base | Official leaderboard | |
| 49 | LumiOpen/Llama-Poro-2-8B-Instruct | 63.2% | exact_match,strict_match; instruct | Official leaderboard | |
| 50 | ilsp/Llama-Krikri-8B-Instruct | 62.8% | exact_match,strict_match; instruct | Official leaderboard | |
| 51 | Qwen/Qwen3.5-9B-Base (base) | 62.6% | acc,none; base | Official leaderboard | |
| 52 | Tower-Babel/Babel-83B-Chat | 62.5% | exact_match,strict_match; instruct | Official leaderboard | |
| 53 | LumiOpen/Llama-Poro-2-70B-base (base) | 62.5% | acc,none; base | Official leaderboard | |
| 54 | | 62.4% | exact_match,strict_match; instruct | Dec 2024 | Official leaderboard |
| 55 | | 62.1% | exact_match,strict_match; instruct | Feb 2026 | Official leaderboard |
| 56 | swiss-ai/Apertus-8B-2509 (base) | 61.9% | acc,none; base | Official leaderboard | |
| 57 | sail/Sailor2-8B-Chat | 61.8% | exact_match,strict_match; instruct | Official leaderboard | |
| 58 | aisingapore/Gemma-SEA-LION-v3-9B (base) | 61.7% | acc,none; base | Official leaderboard | |
| 59 | meta-llama/Llama-3.1-8B-Instruct | 61% | exact_match,strict_match; instruct | Official leaderboard | |
| 60 | | 60.8% | exact_match,strict_match; instruct | Apr 2025 | Official leaderboard |
| 61 | | 60.7% | exact_match,strict_match; instruct | Dec 2024 | Official leaderboard |
| 62 | google/gemma-2-9b (base) | 60.5% | acc,none; base | Official leaderboard | |
| 63 | google/gemma-4-E2B (base) | 60.2% | acc,none; base | Official leaderboard | |
| 64 | Qwen/Qwen2.5-72B (base) | 60.2% | acc,none; base | Official leaderboard | |
| 65 | Qwen/Qwen2.5-3B-Instruct | 60.2% | exact_match,strict_match; instruct | Official leaderboard | |
| 66 | kakaocorp/kanana-1.5-8b-instruct-2505 | 60.1% | exact_match,strict_match; instruct | Official leaderboard | |
| 67 | utter-project/EuroLLM-9B-Instruct | 60.1% | exact_match,strict_match; instruct | Official leaderboard | |
| 68 | | 59.5% | exact_match,strict_match; instruct | Apr 2024 | Official leaderboard |
| 69 | Qwen/Qwen3.5-4B-Base (base) | 59.5% | acc,none; base | Official leaderboard | |
| 70 | | 59.5% | exact_match,strict_match; instruct | Feb 2026 | Official leaderboard |
| 71 | | 59% | exact_match,strict_match; instruct | Dec 2024 | Official leaderboard |
| 72 | | 58.9% | exact_match,strict_match; instruct | Feb 2026 | Official leaderboard |
| 73 | google/gemma-3-4b-pt (base) | 58.8% | acc,none; base | Official leaderboard | |
| 74 | SeaLLMs/SeaLLMs-v3-7B-Chat | 58.8% | exact_match,strict_match; instruct | Official leaderboard | |
| 75 | microsoft/Phi-4-mini-instruct | 58.5% | exact_match,strict_match; instruct | Official leaderboard | |
| 76 | Tower-Babel/Babel-9B-Chat | 58.5% | exact_match,strict_match; instruct | Official leaderboard | |
| 77 | | 58.4% | exact_match,strict_match; instruct | Jul 2024 | Official leaderboard |
| 78 | aisingapore/Llama-SEA-LION-v3-8B (base) | 58% | acc,none; base | Official leaderboard | |
| 79 | CohereLabs/tiny-aya-base (base) | 58% | acc,none; base | Official leaderboard | |
| 80 | Qwen/Qwen2.5-32B (base) | 57.7% | acc,none; base | Official leaderboard | |
| 81 | microsoft/Phi-3.5-mini-instruct | 56.9% | exact_match,strict_match; instruct | Official leaderboard | |
| 82 | meta-llama/Llama-3.1-8B (base) | 56.9% | acc,none; base | Official leaderboard | |
| 83 | utter-project/EuroLLM-9B (base) | 56.9% | acc,none; base | Official leaderboard | |
| 84 | Qwen/Qwen2.5-14B (base) | 56.4% | acc,none; base | Official leaderboard | |
| 85 | sail/Sailor2-20B (base) | 56.4% | acc,none; base | Official leaderboard | |
| 86 | mistralai/Mixtral-8x7B-v0.1 (base) | 56.2% | acc,none; base | Official leaderboard | |
| 87 | BSC-LT/salamandra-7b (base) | 56% | acc,none; base | Official leaderboard | |
| 88 | | 55.5% | exact_match,strict_match; instruct | Jan 2025 | Official leaderboard |
| 89 | | 55.2% | exact_match,strict_match; instruct | Apr 2024 | Official leaderboard |
| 90 | google/gemma-3-1b-pt (base) | 54.7% | acc,none; base | Official leaderboard | |
| 91 | Tower-Babel/Babel-9B (base) | 54.7% | acc,none; base | Official leaderboard | |
| 92 | google/gemma-3-1b-it | 54.6% | exact_match,strict_match; instruct | Official leaderboard | |
| 93 | mistralai/Mistral-7B-Instruct-v0.3 | 54.5% | exact_match,strict_match; instruct | Official leaderboard | |
| 94 | mistralai/Mistral-Small-Instruct-2409 | 54.5% | exact_match,strict_match; instruct | Official leaderboard | |
| 95 | Qwen/Qwen3.5-2B-Base (base) | 54.5% | acc,none; base | Official leaderboard | |
| 96 | LumiOpen/Llama-Poro-2-8B-base (base) | 54.3% | acc,none; base | Official leaderboard | |
| 97 | ilsp/Llama-Krikri-8B-Base (base) | 54% | acc,none; base | Official leaderboard | |
| 98 | LumiOpen/Viking-33B (base) | 54% | acc,none; base | Official leaderboard | |
| 99 | | 53.9% | exact_match,strict_match; instruct | Apr 2025 | Official leaderboard |
| 100 | Qwen/Qwen2.5-1.5B-Instruct | 53.8% | exact_match,strict_match; instruct | Official leaderboard | |
| 101 | meta-llama/Llama-3.2-3B (base) | 53.6% | acc,none; base | Official leaderboard | |
| 102 | tiiuae/falcon-40b (base) | 53.6% | acc,none; base | Official leaderboard | |
| 103 | | 53.6% | exact_match,strict_match; instruct | Sep 2023 | Official leaderboard |
| 104 | Qwen/Qwen2.5-7B (base) | 53.6% | acc,none; base | Official leaderboard | |
| 105 | facebook/xglm-4.5B (base) | 53.5% | acc,none; base | Official leaderboard | |
| 106 | SeaLLMs/SeaLLMs-v3-7B (base) | 53.4% | acc,none; base | Official leaderboard | |
| 107 | Unbabel/TowerBase-13B-v0.1 (base) | 53.4% | acc,none; base | Official leaderboard | |
| 108 | mistralai/Mistral-7B-v0.1 (base) | 53.3% | acc,none; base | Official leaderboard | |
| 109 | bertin-project/Gromenauer-7B (base) | 53.3% | acc,none; base | Official leaderboard | |
| 110 | meta-llama/Llama-3.2-1B-Instruct | 53.3% | exact_match,strict_match; instruct | Official leaderboard | |
| 111 | BSC-LT/salamandra-2b (base) | 53% | acc,none; base | Official leaderboard | |
| 112 | sail/Sailor2-8B (base) | 52.9% | acc,none; base | Official leaderboard | |
| 113 | Unbabel/TowerInstruct-13B-v0.1 | 52.6% | exact_match,strict_match; instruct | Official leaderboard | |
| 114 | LumiOpen/Viking-13B (base) | 52.6% | acc,none; base | Official leaderboard | |
| 115 | | 52.5% | exact_match,strict_match; instruct | Feb 2026 | Official leaderboard |
| 116 | bigscience/bloom-7b1 (base) | 52.4% | acc,none; base | Official leaderboard | |
| 117 | Unbabel/TowerInstruct-7B-v0.1 | 52.3% | exact_match,strict_match; instruct | Official leaderboard | |
| 118 | CraneAILabs/swahili-gemma-1b (base) | 52.1% | acc,none; base | Official leaderboard | |
| 119 | Unbabel/TowerBase-7B-v0.1 (base) | 52% | acc,none; base | Official leaderboard | |
| 120 | Qwen/Qwen3.5-0.8B-Base (base) | 51.8% | acc,none; base | Official leaderboard | |
| 121 | Qwen/Qwen2.5-3B (base) | 51.7% | acc,none; base | Official leaderboard | |
| 122 | CraneAILabs/ganda-gemma-1b (base) | 51.7% | acc,none; base | Official leaderboard | |
| 123 | ilsp/Meltemi-7B-v1.5 (base) | 51.7% | acc,none; base | Official leaderboard | |
| 124 | bigscience/bloom-3b (base) | 51.6% | acc,none; base | Official leaderboard | |
| 125 | SeaLLMs/SeaLLMs-v3-1.5B-Chat | 51.6% | exact_match,strict_match; instruct | Official leaderboard | |
| 126 | LGAI-EXAONE/EXAONE-4.0-1.2B | 51.5% | exact_match,strict_match; instruct | Official leaderboard | |
| 127 | meta-llama/Llama-3.2-3B-Instruct | 51.2% | exact_match,strict_match; instruct | Official leaderboard | |
| 128 | LumiOpen/Viking-7B (base) | 51.2% | acc,none; base | Official leaderboard | |
| 129 | | 51.1% | exact_match,strict_match; instruct | Jul 2025 | Official leaderboard |
| 130 | facebook/xglm-2.9B (base) | 51% | acc,none; base | Official leaderboard | |
| 131 | bigscience/bloom-1b7 (base) | 50.9% | acc,none; base | Official leaderboard | |
| 132 | facebook/xglm-7.5B (base) | 50.8% | acc,none; base | Official leaderboard | |
| 133 | kakaocorp/kanana-1.5-8b-base (base) | 50.7% | acc,none; base | Official leaderboard | |
| 134 | facebook/xglm-1.7B (base) | 50.3% | acc,none; base | Official leaderboard | |
| 135 | Yellow-AI-NLP/komodo-7b-base (base) | 50.3% | acc,none; base | Official leaderboard | |
| 136 | bigscience/bloom-1b1 (base) | 50% | acc,none; base | Official leaderboard | |
| 137 | vilm/vinallama-7b-chat | 49.9% | exact_match,strict_match; instruct | Official leaderboard | |
| 138 | Qwen/Qwen2.5-1.5B (base) | 49.9% | acc,none; base | Official leaderboard | |
| 139 | tiiuae/falcon-7b (base) | 49.8% | acc,none; base | Official leaderboard | |
| 140 | speakleash/Bielik-4.5B-v3 (base) | 49.8% | acc,none; base | Official leaderboard | |
| 141 | google/gemma-3-270m (base) | 49.8% | acc,none; base | Official leaderboard | |
| 142 | bigscience/bloom-560m (base) | 49.7% | acc,none; base | Official leaderboard | |
| 143 | Qwen/Qwen2.5-0.5B-Instruct | 49.6% | exact_match,strict_match; instruct | Official leaderboard | |
| 144 | AI-Sweden-Models/gpt-sw3-6.7b-v2 (base) | 49.6% | acc,none; base | Official leaderboard | |
| 145 | SeaLLMs/SeaLLMs-v3-1.5B (base) | 49.6% | acc,none; base | Official leaderboard | |
| 146 | Gen2B/HyGPT-10b (base) | 49.4% | acc,none; base | Official leaderboard | |
| 147 | facebook/xglm-564M (base) | 49.2% | acc,none; base | Official leaderboard | |
| 148 | AI-Sweden-Models/gpt-sw3-1.3b (base) | 49.1% | acc,none; base | Official leaderboard | |
| 149 | vilm/vinallama-7b (base) | 49.1% | acc,none; base | Official leaderboard | |
| 150 | speakleash/Bielik-1.5B-v3 (base) | 49.1% | acc,none; base | Official leaderboard | |
| 151 | naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B | 49% | exact_match,strict_match; instruct | Official leaderboard | |
| 152 | universitytehran/PersianMind-v1.0 (base) | 48.8% | acc,none; base | Official leaderboard | |
| 153 | sapienzanlp/Minerva-7B-base-v1.0 (base) | 48.8% | acc,none; base | Official leaderboard | |
| 154 | croissantllm/CroissantLLMBase (base) | 48.8% | acc,none; base | Official leaderboard | |
| 155 | sail/Sailor2-1B (base) | 48.7% | acc,none; base | Official leaderboard | |
| 156 | vilm/vinallama-2.7b (base) | 48.5% | acc,none; base | Official leaderboard | |
| 157 | TucanoBR/Tucano-2b4 (base) | 48% | acc,none; base | Official leaderboard | |
| 158 | TucanoBR/Tucano-1b1 (base) | 48% | acc,none; base | Official leaderboard | |
| 159 | sapienzanlp/Minerva-3B-base-v1.0 (base) | 47.9% | acc,none; base | Official leaderboard | |
| 160 | sapienzanlp/Minerva-1B-base-v1.0 (base) | 47.7% | acc,none; base | Official leaderboard | |
| 161 | sail/Sailor2-1B-Chat | 47.5% | exact_match,strict_match; instruct | Official leaderboard | |
| 162 | lelapa/InkubaLM-0.4B (base) | 47.4% | acc,none; base | Official leaderboard | |
| 163 | Azurro/APT3-1B-Base (base) | 47.3% | acc,none; base | Official leaderboard | |
| 164 | ilsp/Meltemi-7B-Instruct-v1.5 | 46.5% | exact_match,strict_match; instruct | Official leaderboard | |
| 165 | | 27.9% | exact_match,strict_match; instruct | Jan 2025 | Official leaderboard |
| 166 | AI-Sweden-Models/gpt-sw3-20b-instruct | 21% | exact_match,strict_match; instruct | Official leaderboard | |
| 167 | BSC-LT/salamandra-7b-instruct | 19.4% | exact_match,strict_match; instruct | Official leaderboard | |
| 168 | BSC-LT/salamandra-2b-instruct | 19.1% | exact_match,strict_match; instruct | Official leaderboard | |
| 169 | google/gemma-3-270m-it | 15.8% | exact_match,strict_match; instruct | Official leaderboard | |
| 170 | croissantllm/CroissantLLMChat-v0.1 | 13.1% | exact_match,strict_match; instruct | Official leaderboard | |
| 171 | sapienzanlp/Minerva-7B-instruct-v1.0 | 12.3% | exact_match,strict_match; instruct | Official leaderboard | |
| 172 | TucanoBR/Tucano-1b1-Instruct | 8.08% | exact_match,strict_match; instruct | Official leaderboard | |
| 173 | AI-Sweden-Models/gpt-sw3-6.7b-v2-instruct | 5.79% | exact_match,strict_match; instruct | Official leaderboard | |
| 174 | LumiOpen/Llama-Poro-2-70B-Instruct | 1.26% | exact_match,strict_match; instruct | Official leaderboard | |
| 175 | AI-Sweden-Models/gpt-sw3-1.3b-instruct | 0.62% | exact_match,strict_match; instruct | Official leaderboard | |
| 176 | Sarvam-M | 0.13% | exact_match,strict_match; instruct | May 2025 | Official leaderboard |
| 177 | TucanoBR/Tucano-2b4-Instruct | 0.10% | exact_match,strict_match; instruct | Official leaderboard |
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 93.9% | exact_match; instruct | Feb 2026 | Official leaderboard |
| 2 | | 93.4% | Nov 2025 | Lab model card | |
| 3 | | 92.8% | Dec 2025 | Lab model card | |
| 4 | | 92.3% | Feb 2026 | Lab model card | |
| 5 | | 92% | exact_match; instruct | Mar 2026 | Official leaderboard |
| 6 | | 91.4% | May 2026 | Lab model card | |
| 7 | GPT-5.4 | 91.3% | exact_match; instruct | Mar 2026 | Official leaderboard |
| 8 | | 90.3% | Jun 2026 | Lab model card | |
| 9 | Claude Sonnet 4.6 | 90.2% | exact_match; instruct | Feb 2026 | Official leaderboard |
| 10 | | 89.8% | Feb 2026 | Lab model card | |
| 11 | | 89.8% | Apr 2026 | Lab model card | |
| 12 | GPT-5.4 Mini | 89.6% | exact_match; instruct | Mar 2026 | Official leaderboard |
| 13 | | 88.4% | Feb 2026 | Lab model card | |
| 14 | | 87.5% | Feb 2026 | Lab model card | |
| 15 | | 86.6% | Feb 2026 | Lab model card | |
| 16 | GPT-5.4 Nano | 85.4% | exact_match; instruct | Mar 2026 | Official leaderboard |
| 17 | | 84.9% | exact_match,strict_match; instruct | Apr 2026 | Official leaderboard |
| 18 | | 83.2% | Mar 2026 | Lab model card | |
| 19 | | 81.7% | exact_match,strict_match; instruct | Mar 2025 | Official leaderboard |
| 20 | | 79.8% | exact_match,strict_match; instruct | Mar 2026 | Official leaderboard |
| 21 | | 78.9% | Mar 2026 | Lab model card | |
| 22 | | 78.3% | exact_match,strict_match; instruct | Sep 2024 | Official leaderboard |
| 23 | gpt-oss-20b | 77.8% | exact_match,strict_match; instruct | Aug 2025 | Official leaderboard |
| 24 | | 77.1% | exact_match,strict_match; instruct | Apr 2025 | Official leaderboard |
| 25 | | 76% | exact_match,strict_match; instruct | Apr 2025 | Official leaderboard |
| 26 | | 74.3% | exact_match,strict_match; instruct | Sep 2025 | Official leaderboard |
| 27 | | 74.2% | exact_match,strict_match; instruct | Dec 2024 | Official leaderboard |
| 28 | | 74% | exact_match,strict_match; instruct | Apr 2025 | Official leaderboard |
| 29 | | 72.2% | exact_match,strict_match; instruct | Jan 2025 | Official leaderboard |
| 30 | | 72.1% | exact_match,strict_match; instruct | Mar 2026 | Official leaderboard |
| 31 | | 71% | exact_match,strict_match; instruct | Oct 2024 | Official leaderboard |
| 32 | | 70.7% | exact_match,strict_match; instruct | Jan 2025 | Official leaderboard |
| 33 | | 70.5% | exact_match,strict_match; instruct | Apr 2025 | Official leaderboard |
| 34 | gpt-oss-120b | 69.5% | exact_match,strict_match; instruct | Aug 2025 | Official leaderboard |
| 35 | | 69.3% | Mar 2026 | Lab model card | |
| 36 | c4ai-command-r-08-2024 | 68.9% | exact_match,strict_match; instruct | Aug 2024 | Official leaderboard |
| 37 | | 67% | exact_match,strict_match; instruct | Sep 2025 | Official leaderboard |
| 38 | | 63.9% | exact_match,strict_match; instruct | Oct 2024 | Official leaderboard |
| 39 | | 62.4% | exact_match,strict_match; instruct | Dec 2024 | Official leaderboard |
| 40 | | 60.8% | exact_match,strict_match; instruct | Apr 2025 | Official leaderboard |
| 41 | | 60.7% | exact_match,strict_match; instruct | Dec 2024 | Official leaderboard |
| 42 | | 59.5% | exact_match,strict_match; instruct | Apr 2024 | Official leaderboard |
| 43 | | 59.5% | exact_match,strict_match; instruct | Feb 2026 | Official leaderboard |
| 44 | | 59.4% | Mar 2026 | Lab model card | |
| 45 | | 59% | exact_match,strict_match; instruct | Dec 2024 | Official leaderboard |
| 46 | | 58.4% | exact_match,strict_match; instruct | Jul 2024 | Official leaderboard |
| 47 | | 55.5% | exact_match,strict_match; instruct | Jan 2025 | Official leaderboard |
| 48 | | 55.2% | exact_match,strict_match; instruct | Apr 2024 | Official leaderboard |
| 49 | | 53.9% | exact_match,strict_match; instruct | Apr 2025 | Official leaderboard |
| 50 | | 53.6% | exact_match,strict_match; instruct | Sep 2023 | Official leaderboard |
| 51 | | 51.1% | exact_match,strict_match; instruct | Jul 2025 | Official leaderboard |
| 52 | | 27.9% | exact_match,strict_match; instruct | Jan 2025 | Official leaderboard |
| 53 | Sarvam-M | 0.13% | exact_match,strict_match; instruct | May 2025 | Official leaderboard |
Labs report scores for 14 models in their cards. Switch the scores above to rank them together.
3 models have held number one on the official leaderboard since its release in October 2025 3 models have held number one with claims included since its release in October 2025
Lab claims count from the day the card came out, independent runs from the model’s release. We draw one row each for the benchmark’s own leaderboard and what labs claim in their cards.
Sources: Official Global PIQA leaderboard and labs’ model cards
Google DeepMind’s own number for Gemini 3 Pro passed 85% in November 2025
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Sources: Official Global PIQA leaderboard and labs’ model cards
Labs headline it less often now, last in June 2026
Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.
| Date | Launch post | Score | As the post writes it |
|---|---|---|---|
| Jun 1, 2026 | 90.3 | Global PIQA | |
| May 20, 2026 | 91.4 | Global PIQA | |
| Apr 2, 2026 | 89.8 | Global PIQA | |
| Mar 2, 2026 | 59.4 | Global PIQA | |
| Mar 2, 2026 | 69.3 | Global PIQA | |
| Mar 2, 2026 | 78.9 | Global PIQA | |
| Mar 2, 2026 | 83.2 | Global PIQA | |
| Feb 24, 2026 | 86.6 | Global PIQA | |
| Feb 24, 2026 | 87.5 | Global PIQA | |
| Feb 24, 2026 | 88.4 | Global PIQA | |
| Feb 16, 2026 | 89.8 | Global PIQA | |
| Feb 14, 2026 | 92.3 | Global PIQA | |
| Dec 17, 2025 | 92.8 | Global PIQA | |
| Nov 18, 2025 | 93.4 | Global PIQA |
One system card reports it
Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| Feb 14, 2026 | | 92.3 | Global PIQA (Table 3) | Capabilities |
The lab's number comes out ahead in 5 of 5 pairs that differ
Each score a lab reported for its own model, beside the same model's best score on the leaderboard. Gaps mostly come from different harnesses, effort levels and task subsets. Every benchmark.
| Model | Lab says | Board says | Gap |
|---|---|---|---|
| | 78.9% Global PIQA | 62.1% Leaderboard, exact_match,strict_match; instruct | +17 pts |
| | 69.3% Global PIQA | 58.9% Leaderboard, exact_match,strict_match; instruct | +10 pts |
| | 83.2% Global PIQA | 73.5% Leaderboard, exact_match,strict_match; instruct | +9.7 pts |
| | 87.5% Global PIQA | 78.5% Leaderboard, exact_match,strict_match; instruct | +9.0 pts |
| | 59.4% Global PIQA | 52.5% Leaderboard, exact_match,strict_match; instruct | +6.9 pts |
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