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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsBioMysteryBench is an evaluation of Claude’s ability to investigate messy biological datasets, choose analytical methods and reach answers that can be checked against objective data properties or independently validated metadata. Anthropic’s results indicate a substantial capability on tasks that at least one domain expert could solve, but much less dependable performance on tasks its expert panel did not solve. The benchmark is evidence about performance on this particular collection of problems—not proof that Claude can replace scientists or that an unsolved problem is impossible.
What BioMysteryBench is designed to measure
Many language-model tests present a self-contained question and reward a short answer. BioMysteryBench instead starts with scientific data. A solver may need to inspect files, use bioinformatics software, query databases, form hypotheses and combine several analyses before answering an open-ended question. The answer is then checked against a property of the data or against metadata validated independently of the solver’s interpretation.
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Open methods, verifiable outcomes
Anthropic describes the benchmark as method-agnostic. A trajectory is graded on its final answer, so two solvers can receive credit even if they use different tools or analytical routes. This matters in computational biology, where a legitimate result can often be reached through more than one pipeline. The benchmark is not asking Claude to imitate one prescribed notebook; it is asking whether the conclusion is correct.
The benchmark’s questions are built around controlled facts rather than a requirement to reproduce an individual scientist’s preferred explanation. Examples include identifying an organism from a crystal structure, identifying a virus from RNA sequencing plus sample metadata validated by PCR, finding a knocked-out gene from expression data and inferring relationships among samples from whole-genome sequences.
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Scope of the dataset
Anthropic says the set contains 99 expert-authored questions spanning multiple bioinformatics fields. Most tasks use raw or minimally processed DNA or RNA sequencing data. The collection includes whole-genome sequencing, single-cell RNA sequencing, methylation, ChIP-seq, metagenomics and Hi-C, along with proteomics and metabolomics tasks. Authors supplied validation notebooks showing that the relevant signal is present in each task’s data.
That design gives the benchmark two useful properties at once: the inputs resemble the imperfect material used in real analysis, while the target answer is intended to be objectively checkable. It also means that success depends on data handling and scientific reasoning, not only on recalling facts about genes, organisms or pathways.
How the human baseline was established
Anthropic asked as many as five domain experts to solve each question from scratch. A task was labeled human-solvable when at least one expert answered correctly. After quality review removed four broken or malformed tasks, the remaining questions were divided into two outcome groups.
| Task group | Anthropic’s count | What the label means |
|---|---|---|
| Human-solvable | 76 tasks | At least one member of the expert panel reached the correct answer. |
| Human-difficult | 23 tasks | The panel did not solve the task; this is a panel outcome, not proof that no human could solve it. |
The distinction is essential when reading the model scores. The second group is not a set of scientifically impossible questions. A validation notebook can demonstrate that a signal exists and that the files are well formed, but it cannot guarantee that a particular person—or a particular model run—will find that signal. The “human-difficult” label therefore describes the observed baseline under the stated conditions.
Anthropic’s reported Claude results
Anthropic reports that Claude Sonnet 4.6 and more capable models solved meaningful portions of the human-difficult group. The post gives a 30% solve rate on that group for Claude Mythos Preview. For Claude Opus 4.6, the gap between the two task groups is large:
| Model and task set | Reported solve rate | How to read it |
|---|---|---|
| Claude Opus 4.6, human-solvable tasks | 77.4% | Performance on tasks at least one panel expert solved. |
| Claude Opus 4.6, human-difficult tasks | 23.5% | Performance on tasks the panel did not solve; the difficulty profile is different. |
| Claude Mythos Preview, human-difficult tasks | 30% | Anthropic’s reported result for this preview model on the difficult group. |
These are Anthropic’s measurements, not an independent replication. They should be treated as benchmark findings for the models and task set described in the April 29, 2026 post, rather than as a general accuracy guarantee for every biological dataset or every Claude product configuration.
Why a single accuracy score is not enough
Anthropic evaluated each problem through five attempts and examined whether a correct answer recurred. That exposes a difference between a result that a model can reproduce and a result that appears only occasionally.
| Opus 4.6 measure | Human-solvable set | Human-difficult set |
|---|---|---|
| Share of solved problems solved at least four times out of five | 86% | 44% |
| Share of wins occurring only once or twice out of five | 9% | 44% |
On the difficult tasks, nearly half of the problems Opus solved were solved only once or twice in five attempts. A headline solve rate that counts one lucky trajectory the same as five consistent trajectories can therefore overstate practical reliability. For scientific use, repeatability is a separate quality dimension: a team needs to know whether the same data and instructions produce the same conclusion before trusting an answer in a downstream analysis.
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What appears to help Claude solve difficult tasks
Connecting mechanisms, databases and prior knowledge
Transcript analysis in Anthropic’s post attributes some successes to Claude’s ability to connect biological mechanisms with information in scientific databases. That kind of background knowledge can narrow a search space: an expression pattern, structural feature or taxonomic clue may suggest which database records or follow-up tests are relevant.
Prior knowledge is not an unconditional advantage. The post also describes an example in which an existing expectation misled the model. A plausible biological story can cause a solver to stop before checking whether the data actually supports it. In practice, every mechanistic explanation should remain a hypothesis until the underlying measurements and controls confirm it.
Combining analytical approaches
When uncertain, Claude sometimes combined several methods instead of relying on one fragile clue. In a real workflow, that might mean checking a sequence-based identification against metadata, comparing expression results with an independent statistical test, or using a structural observation to constrain a database search. Agreement among independent approaches can increase confidence; disagreement is a signal to investigate, not permission to average the answers.
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How BioMysteryBench differs from other scientific evaluations
Anthropic positions BioMysteryBench between several common evaluation styles. The useful comparison questions are whether the inputs are real or simulated, whether the answer has objective ground truth, whether the solver can choose its tools, whether the set contains tasks that human reviewers did not solve, and whether repeated-attempt reliability is reported.
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| Evaluation style | Typical evidence emphasized | How BioMysteryBench contrasts |
|---|---|---|
| Knowledge or reasoning benchmarks | Answers to self-contained prompts, often with a fixed expected response. | BioMysteryBench requires analysis of supplied biological data rather than recall alone. |
| Benchmarks matching scientists’ conclusions | Whether a model agrees with an expert interpretation. | BioMysteryBench targets data properties or independently validated metadata, allowing different valid routes. |
| Simulated laboratory environments | Actions in a controlled environment with known ground truth. | BioMysteryBench emphasizes noisy, real-world-style files while retaining answer verification. |
| CompBioBench | Anthropic describes 100 computational-biology tasks using synthetic or augmented data and scrubbed metadata. | Anthropic reports Opus 4.6 at 81% overall and 69% on its hardest questions, but those figures are not a head-to-head BioMysteryBench comparison. |
The categories are not interchangeable scoreboards. A higher percentage on one benchmark can reflect easier inputs, a different definition of correctness, or a different amount of scaffolding. Comparing the design and reliability measures is more informative than ranking percentages from unrelated tests.
What the results mean for scientists and technical teams
BioMysteryBench is most useful as a capability signal and a workflow warning. It shows that a model can sometimes perform open-ended computational biology, including tasks that defeated the participating experts, while also showing that difficult successes are less stable.
Use models as analytical partners, not final authorities
- Define the verifiable target. Write down what must be identified—an organism, gene, sample relationship or other property—and what evidence would count as a correct answer.
- Give the model the data and constraints. Include file formats, sample labels, reference builds, metadata definitions and any known quality exclusions. Ambiguous context can turn a valid analysis into an answer to the wrong question.
- Require an auditable trail. Ask for commands, parameter choices, intermediate tables and the reason each method was selected. A polished conclusion without a traceable path is difficult to review.
- Run independent checks. Repeat the analysis with a second method, database or statistical formulation. Where the benchmark’s objective signal permits it, use orthogonal validation rather than relying on the same evidence twice.
- Repeat important runs. Five attempts in Anthropic’s evaluation exposed instability that one run would hide. Teams should record prompts, model versions and outputs so they can distinguish a reproducible finding from a one-off trajectory.
- Escalate biological decisions to experts. A model can suggest a candidate gene or organism, but experimental design, clinical interpretation, publication claims and safety decisions require qualified human review.
Where a model can save time
A capable assistant may be valuable for generating candidate analyses, translating between tools, explaining unfamiliar file formats, locating relevant database concepts and drafting scripts for a scientist to inspect. Those benefits do not depend on treating the model’s first answer as correct. The strongest use is often iterative: the scientist sets the question and acceptance tests, the model proposes analyses, and the scientist verifies the result.
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Important limitations of the benchmark
Unsolved does not mean impossible
If neither the model nor the human panel solves a task, the evaluation cannot determine from that outcome alone whether the problem is impossible or simply exceptionally difficult. Validation notebooks establish that a signal exists; they do not establish that the signal is easy to discover, that every reasonable pipeline will recover it, or that the supplied metadata is sufficient for a first attempt.
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Small, curated task set
The benchmark contains 99 questions authored by experts across several fields. That breadth is useful, but it cannot represent every organism, assay, sequencing platform, file quality level or laboratory practice. A result on this set should not be converted into a universal claim about bioinformatics accuracy.
Human comparison conditions
The baseline used up to five experts working from scratch. Differences in time, software familiarity, compute access and domain specialization can affect who solves a task. The labels summarize what happened under those conditions; they are not a census of human capability.
Model and date dependence
The reported numbers concern the model versions and evaluation procedure described by Anthropic on April 29, 2026. Model updates, tool access, system prompts and data-preparation choices can change results. Reproducing the evaluation would require matching those conditions, and the post does not constitute an independent replication.
How to read the benchmark’s main lesson
The central finding is not that Claude has acquired a general-purpose laboratory mind. It is that a frontier model can sometimes navigate real bioinformatics data and reach objectively checkable answers, including on problems that a small expert panel did not solve. The same evaluation shows why capability and dependability must be separated: Opus 4.6 was far stronger on the panel-solvable group than on the difficult group, and its difficult-group wins were less repeatable.
Anthropic summarizes the design goal with the statement: “BioMysteryBench uses messy, real-world bioinformatics data, without allowing the complexity and challenges inherent in this data to corrupt the quality of the evaluation.” The qualification is important. The benchmark makes noisy data part of the test while preserving a way to check answers, but it cannot remove uncertainty about generalization, hidden failure modes or the reliability of an individual run.
For readers deciding whether to use Claude in a bioinformatics workflow, the practical conclusion is straightforward: use it to broaden and accelerate analysis, demand reproducible evidence, and keep an expert responsible for the final biological claim.
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