BABE
Models reason from experimental results in peer-reviewed biology papers to draw conclusions, with causal and cross-scale inference.
The Seed2.0 card says BABE uses interleaved text and images. Scoring and format fields are guesses from the abstract.
- Made by
- ByteDance Seed and collaborators (Zhou et al.)
- Released
- February 2026
- Kind
- Genomics and life sciences
- Level
- Expert
- Scored
- Graded by a model
- Answers
- Open-ended
- Strongest model tested
- GPT-5.1 #65 on the capability index
- Last in a launch post
- April 2026, dropped before it saturated
- Last model added
- April 2026
- Scores refreshed
- November 12th, 2026
In the authors’ words
The rapid evolution of large language models (LLMs) has expanded their capabilities from basic dialogue to advanced scientific reasoning. However, existing benchmarks in biology often fail to assess a critical skill required of researchers: the ability to integrate experimental results with contextual knowledge to derive meaningful conclusions. To address this gap, we introduce BABE(Biology Arena BEnchmark), a comprehensive benchmark designed to evaluate the experimental reasoning capabilities of biological AI systems. BABE is uniquely constructed from peer-reviewed research papers and real-world biological studies, ensuring that tasks reflect the complexity and interdisciplinary nature of actual scientific inquiry. BABE challenges models to perform causal reasoning and cross-scale inference. Our benchmark provides a robust framework for assessing how well AI systems can reason like practicing scientists, offering a more authentic measure of their potential to contribute to biological research.
GPT-5.1 leads with 52.3% among models released by November 2025
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.
Seed2.0 Lite (0428) leads with 57.9%, 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 November 2025, 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
- 52.3 GPT-5.1
- 52.0 Gemini-3-Pro-Preview-Exp
- 51.6 o3-high.code
- 43.3 Doubao-1.8-1228
- 42.2 Gemini-2.5-Pro (snapshot unspecified)
- 41.9 Claude Sonnet 4.5
- 39.8 Doubao-1.6-1015-high.foreval
- 39.5 Doubao-1.6-thinking.0715.foreval
- 39.3 Doubao-1.6-1015-high.foreval (A)
- 37.4 Claude Opus 4.1
- 36.9 GPT-4.1
- 36.6 Doubao-1.6-1015-high.foreval (B)
- 32.8 QwenAPI-vl-max.latest
- 31.7 Doubao-1.5-pro-thinking.vision.0428.eval
- 26.5 QwenAPI-3-max-0923
- 23.9 GPT-4o
- 22.0 Doubao-1.6-flash.0828.foreval
- 20.8 GLM-4.5V
- 1 GPT-5.1 52.3%
- 2 Gemini-3-Pro-Preview-Exp 52%
- 3 o3-high.code 51.6%
- 4 Doubao-1.8-1228 43.3%
- 5 Gemini-2.5-Pro (snapshot unspecified) 42.2%
- 6 Claude Sonnet 4.5 thinking 41.9%
- 7 Doubao-1.6-1015-high.foreval 39.8%
- 8 Doubao-1.6-thinking.0715.foreval 39.5%
- 9 Doubao-1.6-1015-high.foreval (A) 39.3%
- 10 Claude Opus 4.1 thinking 37.4%
- 11 GPT-4.1 36.9%
- 12 Doubao-1.6-1015-high.foreval (B) 36.6%
- 13 QwenAPI-vl-max.latest 32.8%
- 14 Doubao-1.5-pro-thinking.vision.0428.eval 31.7%
- 15 QwenAPI-3-max-0923 26.5%
- 16 GPT-4o Nov 2024 23.9%
- 17 Doubao-1.6-flash.0828.foreval 22%
- 18 GLM-4.5V 20.8%
Source: Official BABE leaderboard
- 1
Seed2.0 Lite (0428) 57.9%
- 2 GPT-5.1 52.3%
- 3
Seed2.0 Pro 50%
- 4 Claude Sonnet 4.5 thinking 41.9%
- 5 Claude Opus 4.1 thinking 37.4%
- 6 GPT-4.1 36.9%
- 7 GPT-4o Nov 2024 23.9%
- 8 GLM-4.5V 20.8%
Sources: Labs’ model cards and official BABE leaderboard
| # | Entry | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | GPT-5.1 | 52.3% | Nov 2025 | Official leaderboard | |
| 2 | Gemini-3-Pro-Preview-Exp | 52% | Official leaderboard | ||
| 3 | o3-high.code | 51.6% | Official leaderboard | ||
| 4 | Doubao-1.8-1228 | 43.3% | Official leaderboard | ||
| 5 | Gemini-2.5-Pro (snapshot unspecified) | 42.2% | Official leaderboard | ||
| 6 | Claude Sonnet 4.5 | 41.9% | thinking | Sep 2025 | Official leaderboard |
| 7 | Doubao-1.6-1015-high.foreval | 39.8% | Official leaderboard | ||
| 8 | Doubao-1.6-thinking.0715.foreval | 39.5% | Official leaderboard | ||
| 9 | Doubao-1.6-1015-high.foreval (A) | 39.3% | Official leaderboard | ||
| 10 | Claude Opus 4.1 | 37.4% | thinking | Aug 2025 | Official leaderboard |
| 11 | GPT-4.1 | 36.9% | Apr 2025 | Official leaderboard | |
| 12 | Doubao-1.6-1015-high.foreval (B) | 36.6% | Official leaderboard | ||
| 13 | QwenAPI-vl-max.latest | 32.8% | Official leaderboard | ||
| 14 | Doubao-1.5-pro-thinking.vision.0428.eval | 31.7% | Official leaderboard | ||
| 15 | QwenAPI-3-max-0923 | 26.5% | Official leaderboard | ||
| 16 | GPT-4o Nov 2024 | 23.9% | Nov 2024 | Official leaderboard | |
| 17 | Doubao-1.6-flash.0828.foreval | 22% | Official leaderboard | ||
| 18 | GLM-4.5V | 20.8% | Aug 2025 | Official leaderboard |
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 57.9% | Apr 2026 | Lab model card | |
| 2 | GPT-5.1 | 52.3% | Nov 2025 | Official leaderboard | |
| 3 | | 50% | Feb 2026 | Lab model card | |
| 4 | Claude Sonnet 4.5 | 41.9% | thinking | Sep 2025 | Official leaderboard |
| 5 | Claude Opus 4.1 | 37.4% | thinking | Aug 2025 | Official leaderboard |
| 6 | GPT-4.1 | 36.9% | Apr 2025 | Official leaderboard | |
| 7 | GPT-4o Nov 2024 | 23.9% | Nov 2024 | Official leaderboard | |
| 8 | GLM-4.5V | 20.8% | Aug 2025 | Official leaderboard |
Labs report scores for 2 models in their cards. Switch the scores above to rank them together.
Number one over time 2 models have held number one with claims included since its release in February 2026
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 BABE leaderboard and labs’ model cards
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Sources: Official BABE leaderboard and labs’ model cards
Labs headline it less often now, last in April 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 |
|---|---|---|---|
| Apr 28, 2026 | 57.9 | BABE | |
| Feb 14, 2026 | 50 | BABE |
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 | | 50 | BABE (Table 3). Its rival columns repeat the BIObench row of Table 13 | Capabilities |
More in Biology and chemistry
- GeneBench Pro In use Agents analyze messy simulated genomics and biology datasets to estimate a decision-relevant quantity across 129 problems. Scored automatically against known answers. GPT-5.6 Sol 31.5%
- SUPERChem In use Models solve 500 expert-written chemistry reasoning problems, offered in multimodal and text-only forms. Expert solution paths also allow a reasoning path score.
Tencent Hy4 preview 66.4%
- LifeSciBench In use Models complete 750 expert-written life science research tasks, often with attached figures, tables, or sequence files. Graded against expert rubrics as score and pass rate. GPT-Rosalind-6 61.3%
- MedChemBench (OpenAI) In use Models answer chemistry questions from an OpenAI internal set, grouped with OpenAI's life science research evaluations. OpenAI has not published the task format. GPT-Rosalind-6 49.6%
- ProtocolQA Open-Ended In use Models correct errors in published lab protocols using open-ended questions adapted from LAB-Bench ProtocolQA. Scored as pass@1. GPT-6 Cyber 47.2%
- MolecularIQ In use Models answer questions about molecular graphs, such as counting, indexing and constrained generation, and a symbolic checker verifies each answer. gpt-oss-120b 47.5%