INCLUDE
Models answer multiple-choice questions from local academic and professional exams in 44 languages that test regional knowledge. Scored by accuracy.
INCLUDE-base has 22,637 exam questions in 44 languages. A lite subset of 11,095 questions also exists, and the dataset cards show scores within about a point of the base set. Reports rarely say which set they used.
In the authors’ words
The performance differential of large language models (LLM) between languages hinders their effective deployment in many regions, inhibiting the potential economic and societal value of generative AI tools in many communities. However, the development of functional LLMs in many languages (i.e., multilingual LLMs) is bottlenecked by the lack of high-quality evaluation resources in languages other than English. Moreover, current practices in multilingual benchmark construction often translate English resources, ignoring the regional and cultural knowledge of the environments in which multilingual systems would be used. In this work, we construct an evaluation suite of 197,243 QA pairs from local exam sources to measure the capabilities of multilingual LLMs in a variety of regional contexts. Our novel resource, INCLUDE, is a comprehensive knowledge- and reasoning-centric benchmark across 44 written languages that evaluates multilingual LLMs for performance in the actual language environments where they would be deployed.
- Level
- University Undergraduates and entry-level professionals
- Scored
- Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
- Answers
- Multiple choice
Claude Opus 5 leads with 89.8% among models released by Jul 2026
Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.
The newest model with a score here came out in Jul 2026, and nobody has reported 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 14 more
- 89.8 Claude Opus 5
- 87.6 Claude Opus 4.8
- 87.0 Claude Opus 4.7
- 86.5 Claude Sonnet 5
- 86.2
Qwen3.7-Max
- 85.6
Qwen3.5 397B-A17B
- 85.1
Qwen 3.6 Plus
- 83.0
Qwen3.7-Plus
- 82.8
Qwen3.5-122B-A10B
- 81.0
Qwen3-235B-A22B-Thinking
- 80.0
Qwen3-VL-235B-A22B
- 78.9
Qwen3-Next-80B-A3B
- 78.7
Qwen3-235B-A22B
- 74.3
Cohere Command A
- 73.7
Qwen3-32B
- 71.9
Qwen3-30B-A3B
- 71.7
Qwen3-14B
- 67.8
Qwen3-8B
- 67.3 Granite 4.1 30B
- 64.0 Granite 4.0 H Small
- 61.8
Qwen3-4B
- 58.2
Apertus 70B
- 57.2
Gemma 3n E4B
- 51.8
Qwen3-1.7B
- 42.1 granite-4.0-1b
- 1 Claude Opus 5 89.8%
- 2 Claude Opus 4.8 87.6%
- 3 Claude Opus 4.7 87%
- 4 Claude Sonnet 5 86.5%
- 5
Qwen3.7-Max 86.2%
- 6
Qwen3.5 397B-A17B 85.6%
- 7
Qwen 3.6 Plus 85.1%
- 8
Qwen3.7-Plus 83%
- 9
Qwen3.5-122B-A10B 82.8%
- 10
Qwen3-235B-A22B-Thinking Jul 2025 81%
- 11
Qwen3-VL-235B-A22B 80%
- 12
Qwen3-Next-80B-A3B 78.9%
- 13
Qwen3-235B-A22B 78.7%
- 14
Cohere Command A 74.3%
- 15
Qwen3-32B 73.7%
- 16
Qwen3-30B-A3B 71.9%
- 17
Qwen3-14B 71.7%
- 18
Qwen3-8B 67.8%
- 19 Granite 4.1 30B 67.3%
- 20 Granite 4.0 H Small 64%
- 21
Qwen3-4B 61.8%
- 22
Apertus 70B 58.2%
- 23
Gemma 3n E4B 57.2%
- 24
Qwen3-1.7B 51.8%
- 25 granite-4.0-1b 42.1%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Claude Opus 5 | 89.8% | Jul 2026 | Model card | |
| 2 | Claude Opus 4.8 | 87.6% | May 2026 | Model card | |
| 3 | Claude Opus 4.7 | 87% | Apr 2026 | Model card | |
| 4 | Claude Sonnet 5 | 86.5% | Jun 2026 | Model card | |
| 5 | | 86.2% | May 2026 | Model card | |
| 6 | | 85.6% | Feb 2026 | Model card | |
| 7 | | 85.1% | Apr 2026 | Model card | |
| 8 | | 83% | Jun 2026 | Model card | |
| 9 | | 82.8% | Feb 2026 | Model card | |
| 10 | | 81% | Jul 2025 | Model card | |
| 11 | | 80% | Sep 2025 | Model card | |
| 12 | | 78.9% | Sep 2025 | Model card | |
| 13 | | 78.7% | Apr 2025 | Model card | |
| 14 | | 74.3% | Apr 2025 | Model card | |
| 15 | | 73.7% | May 2025 | Model card | |
| 16 | | 71.9% | May 2025 | Model card | |
| 17 | | 71.7% | May 2025 | Model card | |
| 18 | | 67.8% | May 2025 | Model card | |
| 19 | Granite 4.1 30B | 67.3% | Apr 2026 | Model card | |
| 20 | Granite 4.0 H Small | 64% | Oct 2025 | Model card | |
| 21 | | 61.8% | May 2025 | Model card | |
| 22 | | 58.2% | Sep 2025 | Model card | |
| 23 | | 57.2% | Jun 2025 | Model card | |
| 24 | | 51.8% | May 2025 | Model card | |
| 25 | granite-4.0-1b | 42.1% | Oct 2025 | Model card |
7 models have held number one since its release in Nov 2024
Lab claims count from the day the card came out, independent runs from the model’s release.
The best score passed 85% in Feb 2026
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Labs headline it less often now, last in Jun 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 |
|---|---|---|---|
| 1 Jun 2026 | 83 | INCLUDE | |
| 20 May 2026 | 86.2 | INCLUDE | |
| 29 Apr 2026 | Granite 4.1 30B | 67.3 | INCLUDE (5-shot, 14 languages) |
| 2 Apr 2026 | 85.1 | INCLUDE | |
| 24 Feb 2026 | 82.8 | INCLUDE | |
| 16 Feb 2026 | 85.6 | INCLUDE | |
| 28 Oct 2025 | granite-4.0-1b | 42.1 | INCLUDE (5-shot, 14 languages) |
| 2 Oct 2025 | Granite 4.0 H Small | 64 | INCLUDE (5-shot, 14 languages) |
| 23 Sep 2025 | 80 | INCLUDE | |
| 11 Sep 2025 | 78.9 | INCLUDE | |
| 25 Jul 2025 | 81 | INCLUDE | |
| 26 Jun 2025 | 57.2 | Include 0-shot (IT) | |
| 29 Apr 2025 | 78.7 | INCLUDE |
14 system cards report it, from 4 labs
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 |
|---|---|---|---|---|
| 24 Jul 2026 | Claude Opus 5 system card | 89.8 | INCLUDE average accuracy across 44 languages, max effort, five trials, printed on chart (8.16.3, Figure 8.16.3.A) | Capabilities |
| 30 Jun 2026 | Claude Sonnet 5 system card | 86.5 | INCLUDE average accuracy across 44 languages, max effort, five trials (8.13.3, Figure 8.13.3.A) | Capabilities |
| 28 May 2026 | Claude Opus 4.8 system card | 87.6 | INCLUDE average accuracy across 44 languages, adaptive thinking (8.15.3, Figure 8.15.3.A) | Capabilities |
| 16 Apr 2026 | Claude Opus 4.7 system card | 87 | INCLUDE average accuracy across 44 languages, adaptive thinking (8.12.3, Figure 8.12.3.A) | Capabilities |
| 26 Nov 2025 | | 80 | INCLUDE (Table 6) | Capabilities |
| 17 Sep 2025 | | 58.2 | INCLUDE, 44 languages (Table 20). INCLUDE V2 beta, 45 more languages, 41.6 | Capabilities |
| 14 May 2025 | | 67.8 | INCLUDE, thinking mode (Table 17). Non-thinking 62.5 (Table 18) | Capabilities |
| 14 May 2025 | | 61.8 | INCLUDE, thinking mode (Table 17). Non-thinking 53.8 (Table 18) | Capabilities |
| 14 May 2025 | | 73.7 | INCLUDE, thinking mode (Table 13). Non-thinking 70.9 (Table 14) | Capabilities |
| 14 May 2025 | | 71.9 | INCLUDE, thinking mode (Table 15). Non-thinking 67.8 (Table 16) | Capabilities |
| 14 May 2025 | | 78.7 | INCLUDE, thinking mode (Table 11). Non-thinking 75.6 (Table 12) | Capabilities |
| 14 May 2025 | | 71.7 | INCLUDE, thinking mode (Table 15). Non-thinking 67.8 (Table 16) | Capabilities |
| 14 May 2025 | | 51.8 | INCLUDE, thinking mode (Table 19). Non-thinking 42.6 (Table 20) | Capabilities |
| 1 Apr 2025 | | 74.3 | INCLUDE-44 average over 20 languages (Table 25) | Capabilities |
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%