TroubleshootingBench
Models find and correct realistic experimental errors in expert-written wet lab protocols. Scored as pass@1.
In-house OpenAI evaluation. The tasks are not public.
- Level
- Expert Specialists with years of training, often over hours of work
- Scored
- Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
- Answers
- Short answer A number, word or short string matched to a key.
GPT-5.5 Pro leads with 49.6%
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.
- 49.6 GPT-5.5 Pro
- 48.4 GPT-6 Astra
- 48.0 GPT-5.6 Sol
- 48.0 GPT-6.1 Sol
- 45.3 GPT-6 Sol
- 44.1 GPT-5.5
- 41.5 GPT-6 Luna
- 37.7 GPT-5.6 Luna
- 35.8 GPT-5.4
- 35.0 GPT-5.2
- 34.0 GPT-5.2 Codex
- 33.7 GPT-5.5 Instant
- 32.0 GPT-5.3 Codex
- 31.9 GPT-5.4 Mini
- 29.0 GPT-5.1-Codex-Max
- 26.0 GPT-5 mini
- 25.0 gpt-oss-120b
- 22.0 GPT-5
- 19.0 gpt-oss-20b
- 1 GPT-5.5 Pro 49.6%
- 2 GPT-6 Astra 48.4%
- 3 GPT-5.6 Sol 48%
- 4 GPT-6.1 Sol 48%
- 5 GPT-6 Sol 45.3%
- 6 GPT-5.5 44.1%
- 7 GPT-6 Luna 41.5%
- 8 GPT-5.6 Luna 37.7%
- 9 GPT-5.4 35.8%
- 10 GPT-5.2 35%
- 11 GPT-5.2 Codex 34%
- 12 GPT-5.5 Instant 33.7%
- 13 GPT-5.3 Codex 32%
- 14 GPT-5.4 Mini 31.9%
- 15 GPT-5.1-Codex-Max 29%
- 16 GPT-5 mini 26%
- 17 gpt-oss-120b 25%
- 18 GPT-5 22%
- 19 gpt-oss-20b 19%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | GPT-5.5 Pro | 49.6% | Apr 2026 | Model card | |
| 2 | GPT-6 Astra | 48.4% | Sep 2026 | Model card | |
| 3 | GPT-5.6 Sol | 48% | Jun 2026 | Model card | |
| 4 | GPT-6.1 Sol | 48% | Sep 2026 | Model card | |
| 5 | GPT-6 Sol | 45.3% | Sep 2026 | Model card | |
| 6 | GPT-5.5 | 44.1% | Apr 2026 | Model card | |
| 7 | GPT-6 Luna | 41.5% | Oct 2026 | Model card | |
| 8 | GPT-5.6 Luna | 37.7% | Aug 2026 | Model card | |
| 9 | GPT-5.4 | 35.8% | Mar 2026 | Model card | |
| 10 | GPT-5.2 | 35% | Dec 2025 | Model card | |
| 11 | GPT-5.2 Codex | 34% | Dec 2025 | Model card | |
| 12 | GPT-5.5 Instant | 33.7% | May 2026 | Model card | |
| 13 | GPT-5.3 Codex | 32% | Feb 2026 | Model card | |
| 14 | GPT-5.4 Mini | 31.9% | Mar 2026 | Model card | |
| 15 | GPT-5.1-Codex-Max | 29% | Nov 2025 | Model card | |
| 16 | GPT-5 mini | 26% | Aug 2025 | Model card | |
| 17 | gpt-oss-120b | 25% | Aug 2025 | Model card | |
| 18 | GPT-5 | 22% | Aug 2025 | Model card | |
| 19 | gpt-oss-20b | 19% | Aug 2025 | Model card |
6 models have held number one since its release in Aug 2025
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.
Labs report it only in system cards
We read the headline results of 479 launch posts from 51 labs, and none of them list this benchmark. Some system cards do, below.
28 system cards report it, from 1 lab
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 |
|---|---|---|---|---|
| 29 Oct 2026 | GPT-6 Luna system card addendum | 37.9 | TroubleshootingBench, pass@1 (Table 8) | Bio and chem risk |
| 7 Oct 2026 | GPT-6 Sol system card addendum | 41.8 | TroubleshootingBench, pass@1 (Table 8) | Bio and chem risk |
| 7 Oct 2026 | GPT-6 Luna system card addendum | 41.5 | TroubleshootingBench, pass@1 (Table 8) | Bio and chem risk |
| 29 Sep 2026 | GPT-6.1 Sol system card addendum | 48 | TroubleshootingBench, refusal-adjusted pass@1 (9.1.1.1) | Bio and chem risk |
| 22 Sep 2026 | GPT-6 Sol system card | 45.3 | TroubleshootingBench, pass@1 (Table 33, A.8.1.1.1) | Bio and chem risk |
| 22 Sep 2026 | GPT-6 Luna system card | 38.6 | TroubleshootingBench, pass@1 (Table 33, A.8.1.1.1) | Bio and chem risk |
| 3 Sep 2026 | GPT-6 Astra system card | 48.4 | TroubleshootingBench, pass@1 (refusal-adjusted 63.46%) (10.1.1.1.4) | Bio and chem risk |
| 6 Aug 2026 | GPT-5.6 Sol system card addendum | 47.6 | TroubleshootingBench, pass@1 with refusals or safe completions counted (46.4% without), printed chart label (Figure 6, 7.1.1.4) | Bio and chem risk |
| 6 Aug 2026 | GPT-5.6 Luna system card addendum | 37.7 | TroubleshootingBench, pass@1 with refusals or safe completions counted (37.5% without), printed chart label (Figure 6, 7.1.1.4) | Bio and chem risk |
| 9 Jul 2026 | GPT-5.6 Terra system card | TroubleshootingBench, pass@1 by cost, chart without printed numbers (Figures 28, 29, 9.1.1.4) | Bio and chem risk | |
| 9 Jul 2026 | GPT-5.6 Sol system card | 48 | TroubleshootingBench, pass@1, top score stated in text (9.1.1.4) | Bio and chem risk |
| 9 Jul 2026 | GPT-5.6 Luna system card | TroubleshootingBench, pass@1 by cost, chart without printed numbers (Figures 28, 29, 9.1.1.4) | Bio and chem risk | |
| 25 Jun 2026 | GPT-5.6 Terra system card | TroubleshootingBench, pass@1 by cost, chart without printed numbers (Figures 28, 29, 9.1.1.4) | Bio and chem risk | |
| 25 Jun 2026 | GPT-5.6 Sol system card | 48 | TroubleshootingBench, pass@1, top score stated in text (9.1.1.4) | Bio and chem risk |
| 25 Jun 2026 | GPT-5.6 Luna system card | TroubleshootingBench, pass@1 by cost, chart without printed numbers (Figures 28, 29, 9.1.1.4) | Bio and chem risk | |
| 4 May 2026 | GPT-5.5 Instant system card | 33.7 | TroubleshootingBench, pass@1 (33.91% counting refusals or safe completions), printed chart label (Figure 6, 8.1.1.4) | Bio and chem risk |
| 23 Apr 2026 | GPT-5.5 system card | 44.1 | TroubleshootingBench, pass@1, printed chart label (Figure 15, 9.1.1.4) | Bio and chem risk |
| 23 Apr 2026 | GPT-5.5 Pro system card | 49.6 | TroubleshootingBench, pass@1, GPT-5.5 Pro, printed chart label (Figure 15, 9.1.1.4) | Bio and chem risk |
| 17 Mar 2026 | GPT-5.4 Mini system card | 31.9 | TroubleshootingBench, pass@1 (33.11% counting refusals) (Table 18) | Bio and chem risk |
| 5 Mar 2026 | GPT-5.4 system card | 35.8 | TroubleshootingBench, pass@1 excluding refusals (38.85% counting refusals and safe completions) (Figure 11, 5.1.1.4) | Bio and chem risk |
| 5 Feb 2026 | GPT-5.3 Codex system card | 32 | TroubleshootingBench, pass@1, printed chart label (Figure 4, 5.1.1.4) | Bio and chem risk |
| 18 Dec 2025 | GPT-5.2 Codex system card addendum | 34 | TroubleshootingBench, pass@1, printed chart label (Figure 4, 5.1.1.4) | Bio and chem risk |
| 11 Dec 2025 | GPT-5.2 system card addendum | 35 | TroubleshootingBench, pass@1, gpt-5.2 browsing, printed chart label (Figure 8) | Bio and chem risk |
| 18 Nov 2025 | GPT-5.1-Codex-Max system card | 29 | TroubleshootingBench, pass@1, printed chart label (40% with refusals counted as successes) (Figure 5, 5.1.1.5) | Bio and chem risk |
| 7 Aug 2025 | GPT-5 system card | 22 | TroubleshootingBench, pass@1, browsing launch candidate (helpful-only 33%) (Figure 13) | Bio and chem risk |
| 7 Aug 2025 | GPT-5 mini system card | 26 | TroubleshootingBench, pass@1, gpt-5-thinking-mini browsing launch candidate (Figure 13) | Bio and chem risk |
| 5 Aug 2025 | gpt-oss-20b model card | 19 | TroubleshootingBench, pass@1, gpt-oss 20b browsing launch candidate (Figure 9) | Bio and chem risk |
| 5 Aug 2025 | gpt-oss-120b model card | 25 | TroubleshootingBench, pass@1, gpt-oss 120b browsing launch candidate (Figure 9) | Bio and chem risk |
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%