ThaiExam
Models answer multiple-choice questions from standardized examinations in Thailand, such as ONET, IC, TGAT, TPAT-1 and A-Level.
HELM releases v1.0.0 (2024-09-04) to v1.2.0 (2025-06-18).
- Level
- School Students, from grade school to high school
- Scored
- Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
- Answers
- Multiple choice
Claude 3.5 Sonnet leads with 75.1% among models released by Aug 2024
Best score per entry from the benchmark's own leaderboard, read on 2 Nov 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.
The newest model with a score here came out in Aug 2024, and nobody has tested a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:
- GPT-6 Astra
- Claude Opus 5.5
- GPT-6.1 Terra
- GPT-6.1 Sol
- and 16 more
- 75.1 Claude 3.5 Sonnet
- 68.6 Claude 3 Opus
- 68.2
Llama 3.1-405B
- 64.7
Gemini 1.5 Pro
- 63.8
Qwen2-72B
- 63.3 GPT-4o
- 62.3
Llama 3.1-70B
- 61.7 llama-3-typhoon-v1.5x-70b-instruct
- 59.8
Gemini 1.5 Flash
- 59.5 typhoon-v1.5-72b-instruct
- 59.3 OpenThaiLLM-Prebuilt-7B
- 57.9 Claude 3 Sonnet
- 57.5 GPT-4 Turbo
- 56.8
Llama 3-70B
- 55.7 GPT-4o mini
- 54.0 Claude 3 Haiku
- 53.9 Pathumma-llm-text-1.0.0
- 53.8
Qwen1.5-72B
- 53.0 llama3-8b-cpt-sea-lionv2.1-instruct
- 51.8 sambalingo-thai-chat-70b
- 49.3 llama-3-typhoon-v1.5x-8b-instruct
- 48.5 sailor-14b-chat
- 47.9 typhoon-v1.5-8b-instruct
- 47.8 seallm-7b-v2.5
- 47.7
Gemini 1.0 Pro
- 45.4 llama3-8b-cpt-sea-lionv2-base
- 42.3 typhoon-7b
- 41.6
Qwen1.5-7B-Chat
- 39.3 sailor-7b-chat
- 39.2 GPT-3.5 Turbo
- 37.9
Command R+
- 37.8 openthaigpt-1.0.0-13b-chat
- 37.7 seallm-7b-v2
- 37.7
Mixtral 8x22B
- 37.7
Command R (03-2024)
- 33.6 sambalingo-thai-chat
- 32.0
Mistral 7B v0.3
- 31.1 openthaigpt-1.0.0-7b-chat
- 30.0
Mixtral 8x7B
- 24.0
Llama 3.1-8B
- 20.2 sea-lion-7b-instruct
- 19.8
Llama 3-8B
- 1 Claude 3.5 Sonnet 75.1%
- 2 Claude 3 Opus 68.6%
- 3
Llama 3.1-405B 68.2%
- 4
Gemini 1.5 Pro May 2024 64.7%
- 5
Qwen2-72B 63.8%
- 6 GPT-4o May 2024 63.3%
- 7
Llama 3.1-70B 62.3%
- 8 llama-3-typhoon-v1.5x-70b-instruct 61.7%
- 9
Gemini 1.5 Flash May 2024 59.8%
- 10 typhoon-v1.5-72b-instruct 59.5%
- 11 OpenThaiLLM-Prebuilt-7B 59.3%
- 12 Claude 3 Sonnet 57.9%
- 13 GPT-4 Turbo Apr 2024 57.5%
- 14
Llama 3-70B 56.8%
- 15 GPT-4o mini 55.7%
- 16 Claude 3 Haiku 54%
- 17 Pathumma-llm-text-1.0.0 53.9%
- 18
Qwen1.5-72B 53.8%
- 19 llama3-8b-cpt-sea-lionv2.1-instruct 53%
- 20 sambalingo-thai-chat-70b 51.8%
- 21 llama-3-typhoon-v1.5x-8b-instruct 49.3%
- 22 sailor-14b-chat 48.5%
- 23 typhoon-v1.5-8b-instruct 47.9%
- 24 seallm-7b-v2.5 47.8%
- 25
Gemini 1.0 Pro 47.7%
- 26 llama3-8b-cpt-sea-lionv2-base 45.4%
- 27 typhoon-7b 42.3%
- 28
Qwen1.5-7B-Chat 41.6%
- 29 sailor-7b-chat 39.3%
- 30 GPT-3.5 Turbo Jan 2024 39.2%
- 31
Command R+ 37.9%
- 32 openthaigpt-1.0.0-13b-chat 37.8%
- 33 seallm-7b-v2 37.7%
- 34
Mixtral 8x22B 37.7%
- 35
Command R (03-2024) 37.7%
- 36 sambalingo-thai-chat 33.6%
- 37
Mistral 7B v0.3 32%
- 38 openthaigpt-1.0.0-7b-chat 31.1%
- 39
Mixtral 8x7B 30%
- 40
Llama 3.1-8B 24%
- 41 sea-lion-7b-instruct 20.2%
- 42
Llama 3-8B 19.8%
| # | Entry | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Claude 3.5 Sonnet | 75.1% | Jun 2024 | Official leaderboard | |
| 2 | Claude 3 Opus | 68.6% | Feb 2024 | Official leaderboard | |
| 3 | | 68.2% | Jul 2024 | Official leaderboard | |
| 4 | | 64.7% | May 2024 | Official leaderboard | |
| 5 | | 63.8% | Jun 2024 | Official leaderboard | |
| 6 | GPT-4o May 2024 | 63.3% | May 2024 | Official leaderboard | |
| 7 | | 62.3% | Jul 2024 | Official leaderboard | |
| 8 | llama-3-typhoon-v1.5x-70b-instruct | 61.7% | Official leaderboard | ||
| 9 | | 59.8% | May 2024 | Official leaderboard | |
| 10 | typhoon-v1.5-72b-instruct | 59.5% | Official leaderboard | ||
| 11 | OpenThaiLLM-Prebuilt-7B | 59.3% | Official leaderboard | ||
| 12 | Claude 3 Sonnet | 57.9% | Feb 2024 | Official leaderboard | |
| 13 | GPT-4 Turbo Apr 2024 | 57.5% | Apr 2024 | Official leaderboard | |
| 14 | | 56.8% | Apr 2024 | Official leaderboard | |
| 15 | GPT-4o mini | 55.7% | Jul 2024 | Official leaderboard | |
| 16 | Claude 3 Haiku | 54% | Mar 2024 | Official leaderboard | |
| 17 | Pathumma-llm-text-1.0.0 | 53.9% | Official leaderboard | ||
| 18 | | 53.8% | Feb 2024 | Official leaderboard | |
| 19 | llama3-8b-cpt-sea-lionv2.1-instruct | 53% | Official leaderboard | ||
| 20 | sambalingo-thai-chat-70b | 51.8% | Official leaderboard | ||
| 21 | llama-3-typhoon-v1.5x-8b-instruct | 49.3% | Official leaderboard | ||
| 22 | sailor-14b-chat | 48.5% | Official leaderboard | ||
| 23 | typhoon-v1.5-8b-instruct | 47.9% | Official leaderboard | ||
| 24 | seallm-7b-v2.5 | 47.8% | Official leaderboard | ||
| 25 | | 47.7% | Dec 2023 | Official leaderboard | |
| 26 | llama3-8b-cpt-sea-lionv2-base | 45.4% | Official leaderboard | ||
| 27 | typhoon-7b | 42.3% | Official leaderboard | ||
| 28 | | 41.6% | Feb 2024 | Official leaderboard | |
| 29 | sailor-7b-chat | 39.3% | Official leaderboard | ||
| 30 | GPT-3.5 Turbo Jan 2024 | 39.2% | Jan 2024 | Official leaderboard | |
| 31 | | 37.9% | Aug 2024 | Official leaderboard | |
| 32 | openthaigpt-1.0.0-13b-chat | 37.8% | Official leaderboard | ||
| 33 | seallm-7b-v2 | 37.7% | Official leaderboard | ||
| 34 | | 37.7% | Apr 2024 | Official leaderboard | |
| 35 | | 37.7% | Mar 2024 | Official leaderboard | |
| 36 | sambalingo-thai-chat | 33.6% | Official leaderboard | ||
| 37 | | 32% | Sep 2023 | Official leaderboard | |
| 38 | openthaigpt-1.0.0-7b-chat | 31.1% | Official leaderboard | ||
| 39 | | 30% | Dec 2023 | Official leaderboard | |
| 40 | | 24% | Jul 2024 | Official leaderboard | |
| 41 | sea-lion-7b-instruct | 20.2% | Official leaderboard | ||
| 42 | | 19.8% | Apr 2024 | Official leaderboard |
Number one over time
Lab claims count from the day the card came out, independent runs from the model’s release.
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
No lab model card reports it yet
We read the headline results of 479 launch posts from 51 labs, and none of them list this benchmark.
More in Multilingual
- MMMLU In use MMLU professionally translated into 14 languages. Multiple-choice accuracy. Claude Mythos Preview 92.7%
- WMT24++ In use English-to-55-language translation of WMT24 test data. Scored with automatic metrics such as MetricX or xCOMET.
K-EXAONE 90.5%
- MMLU-ProX In use MMLU-Pro translated into up to 29 languages with expert review. Multiple-choice accuracy per language.
Qwen3.7-Max 87%
- CLIcK In use Models answer 1,995 multiple-choice questions on Korean language and culture. Questions come from official Korean exams and textbooks in eleven categories.
Solar Open 2 90.7%
- Global MMLU Lite 3.0 In use Models answer 400 human-translated MMLU questions per language in 23 languages. Version 3.0 adds Czech, Hungarian, Oriya, Slovak and Tajik and updates Italian. Accuracy.
Inkling 88.7%
- MMMB In use Models answer multiple-choice questions about images in six languages across 15 categories. The set has about 12,000 questions. LFM2.5-VL-3B 83%