OmniEdu-4B
Our smallest checkpoint for local experimentation.
Download checkpoint ↗69,999instruction examples
15.96Msupervised response tokens
3open model scales
20task-specific system instructions
01 / OPEN MODEL FAMILY
Choose your scale.
Start building.
Download the weights, explore the training data, and run OmniEdu with Transformers or vLLM. All three scales use a 32,768-token training sequence length.
OmniEdu-9B
The middle scale for education applications.
Download checkpoint ↗OmniEdu-27B
Our strongest overall results across the education evaluations.
Download checkpoint ↗Trained on 60,951 education-specific and 9,048 general-purpose examples from more than 100 sources. Explore the dataset ↗
02 / CAPABILITIES
One family for the full learning–teaching loop.
01
Curriculum grounding
Map a question to knowledge points, grade levels, prerequisite relations, and curriculum evidence.
02
K–12 problem solving
Solve authentic school-level questions across subjects, formats, and multimodal inputs.
03
Diagnostic reasoning
Identify misconceptions, missing prerequisites, and knowledge-state gaps from learner work.
04
Pedagogical tutoring
Choose the next instructional action: a hint, question, prerequisite review, or explanation.
03 / EVALUATION
Leading curriculum grounding.
Stronger instructional support.
Among the 16 models evaluated in the paper, OmniEdu-27B leads K12-Bench EM/F1, MathFish accuracy, and LongTutor's teaching score. Explore the full comparison with open-weight and proprietary models below.
K12-Bench · OmniEdu-27B63.12% EM76.69% F1 · best on both metrics
MathFish · OmniEdu-27B85.89%Best accuracy among evaluated models
LongTutor · OmniEdu-27B3.02Best teaching score · original scale
All models. All three education evaluations.
Higher is better. Per-column top three: 1st · 2nd · 3rd. Shaded rows identify OmniEdu.
Curriculum grounding16 models · Table 1
| Model | Size | K12-Bench EM | K12-Bench F1 | MathFish Acc. | EDUMATH MaC |
|---|---|---|---|---|---|
| Open-weight Models | |||||
| Qwen3.5-4B-Base | 4B | 42.72% | 69.20% | 80.33% | 47.60% |
| OmniEdu-4B (ours) | 4B | 54.25% | 71.75% | 83.19% | 68.40% |
| Qwen3.5-9B-Base | 9B | 48.52% | 71.99% | 79.66% | 58.60% |
| OmniEdu-9B (ours) | 9B | 55.46% | 73.68% | 83.70% | 74.00% |
| Qwen3.8-27B | 27B | 52.11% | 73.48% | 83.54% | 70.60% |
| OmniEdu-27B (ours) | 27B | 63.12% | 76.69% | 85.89% | 86.95% |
| Confucius3-Math | 14B | 5.23% | 19.23% | 0.00% | 3.00% |
| MuduoLLM | 14B | 48.24% | 70.46% | 82.85% | 57.20% |
| EduChat-SFT-Qwen2.5-7B | 7B | 45.20% | 67.58% | 78.23% | 28.00% |
| EduChat-R1-Qwen3-8B | 8B | 25.25% | 49.67% | 19.49% | 47.00% |
| EduChat-R1-Qwen3-32B | 32B | 49.42% | 71.21% | 83.71% | 61.20% |
| Proprietary Models | |||||
| GPT-5.4 | – | 43.23% | 67.80% | 80.65% | 80.50% |
| GPT-5.6-Sol | – | 48.63% | 71.03% | 83.68% | 84.92% |
| Claude-Opus-5 | – | 48.48% | 71.34% | 83.52% | 75.00% |
| GLM-5.3 | – | 43.38% | 57.66% | 85.23% | 62.78% |
| Kimi-K3 | – | 55.03% | 74.71% | 85.40% | 90.00% |
K–12 problem solving16 models · Table 2
| Model | Size | GAOKAO-Bench Full | EXAMS-V Overall | MDK12-Bench Full |
|---|---|---|---|---|
| Open-weight Models | ||||
| Qwen3.5-4B-Base | 4B | 88.98% | 44.69% | 35.43% |
| OmniEdu-4B (ours) | 4B | 90.46% | 57.62% | 46.36% |
| Qwen3.5-9B-Base | 9B | 92.94% | 63.00% | 44.50% |
| OmniEdu-9B (ours) | 9B | 93.66% | 66.40% | 50.80% |
| Qwen3.8-27B | 27B | 91.55% | 68.65% | 46.04% |
| OmniEdu-27B (ours) | 27B | 94.87% | 69.52% | 57.76% |
| Confucius3-Math | 14B | 84.51% | 21.95% | 39.13% |
| MuduoLLM | 14B | 89.66% | 21.95% | 45.44% |
| EduChat-SFT-Qwen2.5-7B | 7B | 70.75% | 21.95% | 32.50% |
| EduChat-R1-Qwen3-8B | 8B | 76.08% | 0.69% | 41.29% |
| EduChat-R1-Qwen3-32B | 32B | 83.86% | 21.95% | 41.84% |
| Proprietary Models | ||||
| GPT-5.4 | – | 93.44% | 35.66% | 54.77% |
| GPT-5.6-Sol | – | 95.96% | 24.82% | 54.67% |
| Claude-Opus-5 | – | 97.22% | 64.34% | 57.46% |
| GLM-5.3 | – | 89.76% | 22.29% | 55.33% |
| Kimi-K3 | – | 94.86% | 87.29% | 63.60% |
Pedagogical tutoring16 models · Table 3
| Model | Size | MathTutor-S | MathTutor-SH | TutorBench | LongTutor-E | LongTutor-T |
|---|---|---|---|---|---|---|
| Open-weight Models | ||||||
| Qwen3.5-4B-Base | 4B | 20.42% | 18.36% | 45.52% | 25.67% | 1.60 |
| OmniEdu-4B (ours) | 4B | 75.79% | 84.77% | 46.67% | 65.88% | 2.29 |
| Qwen3.5-9B-Base | 9B | 14.00% | 13.67% | 45.38% | 5.81% | 1.48 |
| OmniEdu-9B (ours) | 9B | 75.26% | 81.64% | 48.16% | 66.63% | 2.66 |
| Qwen3.8-27B | 27B | 57.16% | 55.86% | 58.58% | 36.80% | 2.74 |
| OmniEdu-27B (ours) | 27B | 78.74% | 83.59% | 59.42% | 78.20% | 3.02 |
| Confucius3-Math | 14B | 27.68% | 20.70% | 34.72% | 23.07% | 1.36 |
| MuduoLLM | 14B | 30.42% | 23.44% | 35.98% | 24.31% | 1.28 |
| EduChat-SFT-Qwen2.5-7B | 7B | 18.21% | 22.66% | 21.48% | 26.55% | 1.21 |
| EduChat-R1-Qwen3-8B | 8B | 11.58% | 7.42% | 23.71% | 33.75% | 1.14 |
| EduChat-R1-Qwen3-32B | 32B | 22.21% | 16.80% | 25.92% | 52.03% | 1.56 |
| Proprietary Models | ||||||
| GPT-5.4 | – | 6.32% | 1.96% | 46.34% | 75.50% | 1.62 |
| GPT-5.6-Sol | – | 10.53% | 17.65% | 50.97% | 77.50% | 1.89 |
| Claude-Opus-5 | – | 87.89% | 84.31% | 54.01% | 84.17% | 2.72 |
| GLM-5.3 | – | 77.89% | 76.92% | 38.62% | 77.42% | 2.66 |
| Kimi-K3 | – | 20.00% | 15.38% | 63.65% | 74.73% | 2.17 |
Scores reproduce arXiv v1 (Tables 1–3); higher is better. Values are percentages except LongTutor-T, which uses the original teaching-score scale. MathTutor-S / SH denote Scaffold / Scaffold-hard win rates; LongTutor-E / T denote Evidence / Teaching averages. The five education-specific baselines and GLM-5.3 receive text-only inputs on image-dependent examples, as described in the paper. Rankings apply to the evaluation protocol reported in the paper. Evaluation protocol ↗
General capabilities remain strong.
All three sizes improve on the selected metrics below after educational fine-tuning. Individual submetrics can be flat or decline; the appendix reports the full breakdown.
| Model | IFEval Prompt Strict | GPQA Diamond | MMMU-Pro Overall |
|---|---|---|---|
| Qwen3.5-4B-Base | 64.88% | 58.08% | 50.46% |
| OmniEdu-4B (ours) | 65.06% | 60.61% | 52.60% |
| Qwen3.5-9B-Base | 69.69% | 62.12% | 58.38% |
| OmniEdu-9B (ours) | 73.01% | 63.64% | 60.75% |
| Qwen3.8-27B | 80.59% | 74.24% | 64.97% |
| OmniEdu-27B (ours) | 82.07% | 77.78% | 67.98% |
IFEval: prompt-level strict accuracy. GPQA: Diamond accuracy. MMMU-Pro: overall accuracy. Values are percentages; sources: Tables 13–15 of arXiv v1.
04 / DEPLOYMENT
From checkpoint
to your application.
Start with OmniEdu-4B and serve an OpenAI-compatible endpoint using a recent vLLM release with Qwen3.5 support. Thinking is disabled to match the training template.
For 9B or 27B, change the model ID and adjust GPU parallelism. Memory needs depend on context length, concurrency, and image inputs.
pip install -U vllm
vllm serve lhpku20010120/Omni-Edu-4B \
--served-model-name omniedu \
--dtype bfloat16 \
--tensor-parallel-size 1 \
--max-model-len 32768 \
--reasoning-parser qwen3 \
--default-chat-template-kwargs '{"enable_thinking": false}'05 / THE PAPER
OmniEdu: Open Foundation Models
for Learning and Teaching
Hao Liang · Qihan Lin · Meiyi Qiang · Linzhuang Sun · Hengyi Feng · Mingrui Chen · Sizhe Qiu · Wentao Zhang
arXiv:2609.23088 · September 19, 2026
Cite OmniEdu · BibTeX
@misc{liang2026omniedu,
title = {OmniEdu: Open Foundation Models for Learning and Teaching},
author = {Hao Liang and Qihan Lin and Meiyi Qiang and Linzhuang Sun and Hengyi Feng and Mingrui Chen and Sizhe Qiu and Wentao Zhang},
year = {2026},
eprint = {2609.23088},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2609.23088}
}OmniEdu supports educational research and teacher-assistance tools. Benchmark results do not establish classroom learning gains; educators should review model outputs before consequential use.
“
An educational model must coordinate what to teach, where the learner is, and how to respond.
— OmniEdu design principle