For all Research evaluations, scores were averaged across 30 independent trials, using a threshold of a response earning at least seven rubric points as correct.
We evaluated several frontier models: GPT-4o, OpenAI o4-mini, OpenAI o3, GPT-5.2, Claude Opus 4.5, Gemini 3 Pro, Grok 4, GPT-5.1, and GPT-5 on FrontierScience-Olympiad and FrontierScience-Research. All reasoning models were evaluated at “high” reasoning effort with the exception of GPT-5.2 at “xhigh”, and without browsing. In our initial evaluations, GPT-5.2 is the top performing model on FrontierScience, scoring 77% on the Olympiad Set and 25% on the Research set. Surprisingly, GPT-5 outperforms GPT-5.1 on the Research set and ties GPT-5.2. Overall, we’ve seen substantial progress on solving expert-level questions while leaving headroom for more progress, especially on open-ended research-style tasks.
For both sets, we use a model-based judge of GPT-5 (with a reasoning effort of ”high”) to evaluate the models. For Olympiad, we give the judge the attempted answer and the actual answer and ask it to compare equivalency of the expression, number, or phrase. For Research, we give the judge the attempted answer and the rubric and ask it to return a single number reflecting the number of rubric points the answer earned (Appendix [B](https://arxiv.org/html/2601.21165#A2 "Appendix B Evaluation prompts ‣ FrontierScience: Evaluating AI’s ability to perform expert-level scientific tasks")).
![Refer to caption](https://arxiv.org/html/2601.21165v1/figures/olympiad_subject_accuracy.png)

![Refer to caption](https://arxiv.org/html/2601.21165v1/figures/research_subject_accuracy.png)

Figure 7: We compare accuracies across several frontier models on FrontierScience-Olympiad (Left) and FrontierScience-Research (Right) with accuracies split out by subject.
When separating by subject, models perform comparably across distributions. For the Olympiad set, models perform better on chemistry, followed by physics and biology. For the Research set, models perform better on chemistry, followed by biology and then physics. Analyzing the transcripts, models typically struggled with reasoning or logic error, failures in understanding niche concepts, calculation errors, and factual inaccuracy.
![Refer to caption](https://arxiv.org/html/2601.21165v1/figures/olympiad_reasoning_efforts.png)

![Refer to caption](https://arxiv.org/html/2601.21165v1/figures/research_reasoning_efforts.png)

Figure 8: We compare accuracies for GPT-5.2 and OpenAI o3 on FrontierScience-Olympiad and FrontierScience-Research across different reasoning efforts. Using more test-time tokens enables GPT-5.2 to go from 67.5% to 77.1% on the Olympiad set, as well as from 18% to 25% on the Research set. Surprisingly, o3 performs marginally worse at high reasoning effort compared to medium reasoning effort on the Research set.
## 4 Discussion

While FrontierScience represents an advance in understanding scientific research capabilities, there are multiple limitations:

1. 1.
Constrained problem-solving: A significant part of scientific research is proposing novel research directions, hypotheses, and ideas. FrontierScience is composed of questions with a constrained problem statement, which focuses on evaluating the reasoning to complete a research task and less on ideation. While the Research set aims to measure more open-ended reasoning, this is an inherent limitation of an autogradable Q&A style evaluation.
2. 2.
Rubric reliability: We sought to improve rubric reliability of the Research set through strict guidelines, verification, and consistency with human grading. However, the rubric is less objective than the equivalency checker of a single expression or number, and relies on the model judge’s capabilities.
3. 3.
Modalities: Problems are designed to be text-only without image or video outputs. Modalities beyond text are more representative of scientific research.

---

We increased review coverage for Research due to the questions being open-ended and rubrics being a newer and more imprecise grading architecture.
From over 500 Olympiad questions and over 200 Research questions, we did a meta-review with experts to filter down to an open-sourced gold set of 100 Olympiad questions and 60 Research questions. We keep the rest of the questions held-out to track potential contamination of the open-sourced set.
### 2.3 Rubric-based grading

![Refer to caption](https://arxiv.org/html/2601.21165v1/rubric.png)Figure 4: Each task in the research set is graded using a rubric totaling 10 points that can be used by an expert or a judge model. To scale our ability to evaluate models, we use another model to judge responses.
The Olympiad set is gradable with a number, expression, or fuzzy string match, which improves verification. However, this verification often trades off with the expressivity and open-endedness of the problem. For the Research set, we introduce an experimental rubric-based architecture for grading more open-ended tasks.
Each question includes a scoring rubric with multiple independent and objectively assessable items, totaling 10 points. Each rubric item contains a description for a specific pass/fail condition (e.g., “Writes the following equation X”) and points. The grading rubric assesses not only the accuracy of the final answer, but also the correctness of intermediate reasoning steps, allowing for nuanced model performance and failure analysis. Scoring seven out of 10 points is considered a suitable solution and marked as a success. Due to the experimental design, we expect the Research set to have a lower noise ceiling than the Olympiad set. The flexibility of rubric points also enables other future grading procedures, such as average rubric points or different thresholds for what is considered a ”success”.
Each question is accompanied by an explanatory solution path crafted by subject-matter experts. To run these evaluations without requiring human expert graders, we rely on judge models that assign a score given an attempted answer and a rubric. We provide model judge prompts in Appendix [B](https://arxiv.org/html/2601.21165#A2 "Appendix B Evaluation prompts ‣ FrontierScience: Evaluating AI’s ability to perform expert-level scientific tasks") that we use for all evaluations in this paper. We use GPT-5 at high reasoning effort for the model judge.
### 2.4 Benchmark composition

![Refer to caption](https://arxiv.org/html/2601.21165v1/figures/olympiad_composition.png)Figure 5: The Olympiad split is composed of a diverse set of topics, from biochemistry to quantum mechanics.
FrontierScience contains a diverse range of scientific questions (Fig. [5](https://arxiv.org/html/2601.21165#S2.F5 "Figure 5 ‣ 2.4 Benchmark composition ‣ 2 Benchmark Construction ‣ FrontierScience: Evaluating AI’s ability to perform expert-level scientific tasks")). The Olympiad set is grounded in topics common on international science olympiad exams and is more weighted toward physics and chemistry over biology because it’s more feasible to develop questions that resolve to verifiable expressions and numbers. The Research set is grounded in contributors’ research specialties, with the gold set of 60 questions equally split between physics, chemistry, and biology.
## 3 Experiments

### 3.1 Main results

![Refer to caption](https://arxiv.org/html/2601.21165v1/figures/olympiad_headline_plot.png)

![Refer to caption](https://arxiv.org/html/2601.21165v1/figures/research_headline_plot.png)

Figure 6: We compare accuracies across several frontier models. GPT-5.2 is our highest performing model across the Olympiad and the Research set. Gemini 3 Pro is comparable to GPT-5.2 on Olympiad, and GPT-5 is tied with GPT-5.2 on Research. For all Olympiad evaluations, scores were averaged across 20 independent trials.

---

Narayanan, M. Ponnapati, A. D. White, and S. G. RodriquesLAB-bench: measuring capabilities of language models for biology research.
External Links: 2407.10362,
[Link](https://arxiv.org/abs/2407.10362 "")Cited by: [§5](https://arxiv.org/html/2601.21165#S5.p3.1 "5 Related Work ‣ FrontierScience: Evaluating AI’s ability to perform expert-level scientific tasks").
- Lu et al. (2022)P. Lu, S. Mishra, T. Xia, L. Qiu, and K. ChangLearn to explain: multimodal reasoning via thought chains for science question answering.
In Advances in Neural Information Processing Systems,
Cited by: [§1](https://arxiv.org/html/2601.21165#S1.p2.1 "1 Introduction ‣ FrontierScience: Evaluating AI’s ability to perform expert-level scientific tasks"),
[§5](https://arxiv.org/html/2601.21165#S5.p1.1 "5 Related Work ‣ FrontierScience: Evaluating AI’s ability to perform expert-level scientific tasks").
- Mirza et al. (2024)A. Mirza, N. Alampara, S. Kunchapu, M. Ríos-García, B. Emoekabu, A. Krishnan, T. Gupta, M. Schilling-Wilhelmi, M. Okereke, A. Aneesh, A. M. Elahi, M. Asgari, J. Eberhardt, H. M. Elbeheiry, M. V. Gil, M. Greiner, C. T. Holick, C. Glaubitz, T. Hoffmann, A. Ibrahim, L. C. Klepsch, Y. Köster, F. A. Kreth, J. Meyer, S. Miret, J. M. Peschel, M. Ringleb, N. Roesner, J. Schreiber, U. S. Schubert, L. M. Stafast, D. Wonanke, M. Pieler, P. Schwaller, and K. M. JablonkaAre large language models superhuman chemists?.
External Links: 2404.01475,
[Link](https://arxiv.org/abs/2404.01475 "")Cited by: [§5](https://arxiv.org/html/2601.21165#S5.p2.1 "5 Related Work ‣ FrontierScience: Evaluating AI’s ability to perform expert-level scientific tasks").
- OpenAI (2025)OpenAIIntroducing gpt-5.2.
Note: [https://openai.com/index/introducing-gpt-5-2/](https://openai.com/index/introducing-gpt-5-2/ "") Accessed: 2025-12-15Cited by: [§1](https://arxiv.org/html/2601.21165#S1.p1.1 "1 Introduction ‣ FrontierScience: Evaluating AI’s ability to perform expert-level scientific tasks").
- Qiu et al. (2025)S. Qiu, S. Guo, Z. Song, Y. Sun, Z. Cai, J. Wei, T. Luo, Y. Yin, H. Zhang, Y. Hu, C. Wang, C. Tang, H. Chang, Q. Liu, Z. Zhou, T. Zhang, J. Zhang, Z. Liu, M. Li, Y. Zhang, B. Jing, X. Yin, Y. Ren, Z. Fu, J. Ji, W. Wang, X. Tian, A. Lv, L. Man, J. Li, F. Tao, Q. Sun, Z. Liang, Y. Mu, Z. Li, J. Zhang, S. Zhang, X. Li, X. Xia, J. Lin, Z. Shen, J. Chen, Q. Xiong, B. Wang, F. Wang, Z. Ni, B. Zhang, F. Cui, C. Shao, Q. Cao, M. Luo, Y. Yang, M. Zhang, and H. X. ZhuPHYBench: holistic evaluation of physical perception and reasoning in large language models.
External Links: 2504.16074,
[Link](https://arxiv.org/abs/2504.16074 "")Cited by: [§5](https://arxiv.org/html/2601.21165#S5.p2.1 "5 Related Work ‣ FrontierScience: Evaluating AI’s ability to perform expert-level scientific tasks").
- Rein et al. (2023)D. Rein, B. L. Hou, A. C. Stickland, J. Petty, R. Y. Pang, J. Dirani, J. Michael, and S. R. BowmanGPQA: a graduate-level google-proof q&a benchmark.
arXiv preprint arXiv:2311.12022.
Cited by: [§1](https://arxiv.org/html/2601.21165#S1.p1.1 "1 Introduction ‣ FrontierScience: Evaluating AI’s ability to perform expert-level scientific tasks"),
[§1](https://arxiv.org/html/2601.21165#S1.p2.1 "1 Introduction ‣ FrontierScience: Evaluating AI’s ability to perform expert-level scientific tasks"),
[§5](https://arxiv.org/html/2601.21165#S5.p1.1 "5 Related Work ‣ FrontierScience: Evaluating AI’s ability to perform expert-level scientific tasks").
- Starace et al. (2025)G. Starace, O. Jaffe, D. Sherburn, J. Aung, J. S. Chan, L. Maksin, R. Dias, E. Mays, B. Kinsella, W. Thompson, J. Heidecke, A. Glaese, and T. PatwardhanPaperBench: evaluating ai’s ability to replicate ai research.
External Links: 2504.01848,
[Link](https://arxiv.org/abs/2504.01848 "")Cited by: [§5](https://arxiv.org/html/2601.21165#S5.p2.1 "5 Related Work ‣ FrontierScience: Evaluating AI’s ability to perform expert-level scientific tasks").
- Wang et al. (2024a)X. Wang, Z. Hu, P.

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arXiv:2601.21165v1 \[cs.AI\] 29 Jan 2026

# FrontierScience: Evaluating AI’s ability to perform expert-level scientific tasks

Miles Wang
††thanks: Correspondence to milesw@openai.comRobi Lin
Kat Hu
Joy Jiao
Neil Chowdhury
Ethan Chang
Tejal Patwardhan
OpenAI

###### Abstract

We introduce FrontierScience, a benchmark evaluating AI capabilities for expert-level scientific reasoning. FrontierScience consists of two tracks: (1) Olympiad, which contains international olympiad problems (at the level of IPhO, IChO, and IBO), and (2) Research, which contains PhD-level, open-ended problems representative of sub-problems in scientific research. In total, FrontierScience is composed of several hundred questions (160 in the open-sourced gold set) covering subfields across physics, chemistry, and biology, from quantum electrodynamics to synthetic organic chemistry. Recent model progress has nearly saturated existing science benchmarks, which often rely on multiple-choice knowledge questions or already published information. In contrast, all Olympiad problems are originally produced by international olympiad medalists and national team coaches to ensure standards of difficulty, originality, and factuality. All Research problems are research sub-tasks written and verified by PhD scientists (doctoral candidates, post-doctoral researchers, or professors). For Research, we also introduce a granular rubric-based architecture to evaluate model capabilities throughout the process of solving a research task, as opposed to judging a standalone answer. In initial evaluations of several frontier models, GPT-5.2 is the top performing model on FrontierScience, scoring 77% on the Olympiad set and 25% on the Research set.
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## 1 Introduction

Language models’ reasoning capabilities have significantly advanced in scientific domains. When GPQA, a “Google-Proof” multiple-choice science benchmark written by PhD experts, was released in November 2023, GPT-4 scored 39%, below the expert baseline of 70% ( [Rein et al., 2023](https://arxiv.org/html/2601.21165#bib.bib1 "")). Two years later, GPT-5.2 scored 92% ( [OpenAI, 2025](https://arxiv.org/html/2601.21165#bib.bib6 "")).
As models’ reasoning and knowledge capabilities continue to scale, unsaturated benchmarks will be important to measure and forecast models’ ability to accelerate scientific research. Prior benchmarks have tracked useful scientific capabilities relative to model improvements ( [Rein et al., 2023](https://arxiv.org/html/2601.21165#bib.bib1 ""); [He et al., 2024](https://arxiv.org/html/2601.21165#bib.bib2 ""); [Lu et al., 2022](https://arxiv.org/html/2601.21165#bib.bib3 ""); [Hendrycks et al., 2021](https://arxiv.org/html/2601.21165#bib.bib4 "")). However, as models have rapidly improved at reasoning, a new generation of science benchmarks is required to keep apace with progress.
To assess real-world scientific capabilities, we introduce FrontierScience, composed of hundreds of questions that are difficult, verifiable, and original. FrontierScience questions are written and verified by subject matter experts across physics, chemistry, and biology, and are composed of two levels:

1. 1.
FrontierScience-Olympiad: Science Olympiad-style questions designed by international olympiad medalists to assess scientific reasoning in a short answer format.
2. 2.
FrontierScience-Research: Research subproblems designed by PhD scientists (doctoral candidates, professors, or postdoctoral researchers) that one might encounter while doing original research.