OncologyBench
Separate agents run analyses of cancer research data, and a final agent makes a decision on detection, diagnosis or treatment. Strict scoring needs full marks on both parts.
Part of LatchBio's BioBench suite on benchmarks.bio. The V1 set has 25 evaluations. Release month is the GitHub repo creation month.
- Level
- Expert Specialists with years of training, often over hours of work
- Scored
- Graded by a model A language model scores each response, usually against a rubric.
- Answers
- Agent task Many steps in a terminal, browser, desktop, app, game or robot.
Claude Opus 5.5 leads with 50.7%
Best score per model from the benchmark's own leaderboard, read on 2 Nov 2026. Where a model ran with several settings or harnesses, we keep its best. Every run is in the next chart.
- 50.7 Claude Opus 5.5
- 44.0 Claude Fable 5.1
- 42.7 Claude Sonnet 5.5
- 40.0 Claude Opus 5
- 33.3
Gemini 4 Argon
- 24.0 Kimi K3
- 24.0 GPT-6.1 Sol
- 24.0
Muse Wonder
- 22.7
Mistral Large 4
- 21.3 GPT-6 Astra
- 20.0 GPT-6 Sol
- 18.7 GPT-5.6 Sol
- 18.7 Grok 4.6
- 18.7 Grok 4.7
- 17.3 GLM-5.3
- 16.0 Claude Opus 4.8
- 16.0 GPT-6 Luna
- 16.0 GPT-6.1 Terra
- 14.7 GPT-5.5
- 14.7 Claude Sonnet 5
- 14.7
Gemini 3.7 Flash
- 14.7
DeepSeek V4.1 Flash
- 13.3
Gemini 3.8 Flash
- 12.0
Muse Spark Open
- 10.7 GPT-5.6 Luna
- 8.00 Mercury 3
- 5.33 Claude Sonnet 4.6
- 1.33
Nemotron 3 Ultra
- 1.33 Beam 1.0
- 0.0
Nemotron 3.5 Lightning 30B-A3B
- 1 Claude Opus 5.5 LSVP 50.7%
- 2 Claude Fable 5.1 LSVP 44%
- 3 Claude Sonnet 5.5 LSVP 42.7%
- 4 Claude Opus 5 40%
- 5
Gemini 4 Argon 33.3%
- 6 Kimi K3 24%
- 7 GPT-6.1 Sol 24%
- 8
Muse Wonder 24%
- 9
Mistral Large 4 22.7%
- 10 GPT-6 Astra 21.3%
- 11 GPT-6 Sol 20%
- 12 GPT-5.6 Sol 18.7%
- 13 Grok 4.6 18.7%
- 14 Grok 4.7 18.7%
- 15 GLM-5.3 17.3%
- 16 Claude Opus 4.8 16%
- 17 GPT-6 Luna 16%
- 18 GPT-6.1 Terra 16%
- 19 GPT-5.5 14.7%
- 20 Claude Sonnet 5 14.7%
- 21
Gemini 3.7 Flash 14.7%
- 22
DeepSeek V4.1 Flash 14.7%
- 23
Gemini 3.8 Flash 13.3%
- 24
Muse Spark Open 12%
- 25 GPT-5.6 Luna 10.7%
- 26 Mercury 3 8.00%
- 27 Claude Sonnet 4.6 5.33%
- 28
Nemotron 3 Ultra 1.33%
- 29 Beam 1.0 1.33%
- 30
Nemotron 3.5 Lightning 30B-A3B 0%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Claude Opus 5.5 | 50.7% | LSVP, Pi | Sep 2026 | Official leaderboard |
| 2 | Claude Fable 5.1 | 44% | LSVP, Pi | Sep 2026 | Official leaderboard |
| 3 | Claude Sonnet 5.5 | 42.7% | LSVP, Claude Code | Sep 2026 | Official leaderboard |
| 4 | Claude Opus 5 | 40% | Pi | Jul 2026 | Official leaderboard |
| 5 | | 33.3% | Pi | Sep 2026 | Official leaderboard |
| 6 | Kimi K3 | 24% | Pi | Jul 2026 | Official leaderboard |
| 7 | GPT-6.1 Sol | 24% | Pi | Sep 2026 | Official leaderboard |
| 8 | | 24% | Pi | Oct 2026 | Official leaderboard |
| 9 | | 22.7% | Pi | Oct 2026 | Official leaderboard |
| 10 | GPT-6 Astra | 21.3% | Pi | Sep 2026 | Official leaderboard |
| 11 | GPT-6 Sol | 20% | Codex | Sep 2026 | Official leaderboard |
| 12 | GPT-5.6 Sol | 18.7% | Pi | Jul 2026 | Official leaderboard |
| 13 | Grok 4.6 | 18.7% | Pi | Aug 2026 | Official leaderboard |
| 14 | Grok 4.7 | 18.7% | Pi | Sep 2026 | Official leaderboard |
| 15 | GLM-5.3 | 17.3% | Pi | Aug 2026 | Official leaderboard |
| 16 | Claude Opus 4.8 | 16% | Claude Code | May 2026 | Official leaderboard |
| 17 | GPT-6 Luna | 16% | Codex | Sep 2026 | Official leaderboard |
| 18 | GPT-6.1 Terra | 16% | Pi | Oct 2026 | Official leaderboard |
| 19 | GPT-5.5 | 14.7% | Pi | Apr 2026 | Official leaderboard |
| 20 | Claude Sonnet 5 | 14.7% | Pi | Jun 2026 | Official leaderboard |
| 21 | | 14.7% | Pi | Aug 2026 | Official leaderboard |
| 22 | | 14.7% | Pi | Sep 2026 | Official leaderboard |
| 23 | | 13.3% | Pi | Sep 2026 | Official leaderboard |
| 24 | | 12% | Pi | Oct 2026 | Official leaderboard |
| 25 | GPT-5.6 Luna | 10.7% | Codex | Jul 2026 | Official leaderboard |
| 26 | Mercury 3 | 8.00% | Pi | Oct 2026 | Official leaderboard |
| 27 | Claude Sonnet 4.6 | 5.33% | Claude Code | Feb 2026 | Official leaderboard |
| 28 | | 1.33% | Pi | Jun 2026 | Official leaderboard |
| 29 | Beam 1.0 | 1.33% | Pi | Oct 2026 | Official leaderboard |
| 30 | | 0% | Pi | Aug 2026 | Official leaderboard |
Scores at each effort level
Models the leaderboard ran at two or more effort levels. We don’t have enough cost data here to say what the extra effort costs.
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 Biology and chemistry
- 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%
- 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-6 Astra 37.1%
- 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-6 Astra 60.3%
- MolecularIQ In use Models answer questions about molecular graphs, such as counting, indexing and constrained generation, and a symbolic checker verifies each answer.
Intern-S2-397B 62.4%
- TOMG-Bench In use Models generate molecules from natural language requests in three tasks: molecule editing, molecule optimization and customized generation. Each output is checked for validity and for meeting the requested properties.
Intern-S2-397B 66.8%
- BABE Fading Models reason from experimental results in peer-reviewed biology papers to draw conclusions, with causal and cross-scale inference.
Seed2.0 Lite (0428) 57.9%