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There is no evidence-based overall winner for scientific research. Claude can assist with literature reviews, coding and other research tasks, but performance depends on the job, model version and tools used. Available comparisons measure different things—from scientific reasoning questions to clinical-trial interpretation and a simulated coding-agent workflow—so their scores cannot be combined into a universal ranking. Choose by testing models on the work you actually need done, and verify important outputs yourself.
What Claude can do in a research workflow
Anthropic describes scientists it works with using Claude for literature reviews, coding, experiment planning, tool-assisted work and finding patterns in large datasets. Those examples suggest where researchers may try the model: organizing information, drafting or checking code, exploring options and assisting with analysis. They are provider-reported examples from Anthropic’s research relationships, not an independent, controlled comparison showing that Claude outperforms other models on those tasks.
“Scientific research” is not one capability. Reviewing papers, interpreting statistical results, reading figures, executing analysis code and following a sequence of tool-use instructions each place different demands on a model. A model can be useful for one part of that workflow and weaker on another. The relevant comparison is therefore not simply Claude versus another brand; it is a specific version of Claude versus a specific alternative, given the same materials and task.
Think of a model as an assistant for bounded work, not as an authority that validates a scientific claim. It can help a researcher work through material, but the researcher still needs to check source fidelity, calculations, code and conclusions before relying on the result.
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What the available comparisons show
The figures below come from different evaluations, so each answers a different question. They should not be read as results from one head-to-head scientific-research test.
| Evaluation | Reported result | What it measures—and what it does not establish |
|---|---|---|
| FrontierScience, OpenAI page accessed in 2026 | GPT-5.2 scored 77% on FrontierScience-Olympiad and 25% on FrontierScience-Research. | Expert-designed physics, chemistry and biology questions and research subtasks. These are OpenAI-reported results on constrained benchmark tasks, not a measure of general research quality or everyday scientific productivity. |
| Clinical-trial conclusion study, Scientific Reports, 2026 | Median total scores: ChatGPT 25/25 (100%), Gemini 21/25 (84%), Grok3 18/25 (72%) and Claude 17/25 (68%). | Agreement with published clinical-trial conclusions in that study. These are not general accuracy rates for scientific research or a ranking across all model versions and tasks. |
| Gemini model card, Google DeepMind, September 2026 | On the human-solvable BioMysteryBench row: Gemini 3.8 Flash 88.8%, Claude Opus 5 90.1% and GPT-5.6 Sol 79.5%. On LABBench2: 86.2%, 84.2% and 82.1%, respectively. | Provider-published results for specified model versions and benchmark setups. They provide a narrow view of performance on those evaluations, not a verdict on general research assistance. |
| Simulated gravitational-wave coding workflow, arXiv preprint, May 27, 2026 | The authors report that Claude Code finished faster in the reported runs, while it silently departed from an instruction in one run. Codex took longer and used explicit corrective restarts. | One specified simulated analysis pipeline. This preprint does not establish a general speed or reliability advantage for either coding agent. |
The clinical-trial paper reports Cronbach’s α = 0.868 for inter-rater reliability. It also notes that raters were not blinded to model identity, creating a stated risk of subjective bias. Both details matter when interpreting its scores: the reported agreement between raters does not remove the potential effect of knowing which model produced an answer.
Why benchmark scores do not produce one “best” model
The tests ask different questions
FrontierScience evaluates expert-written Olympiad-style and research tasks. Its reported evaluation includes more than 700 textual questions, with a 160-question gold set; the Olympiad track has 100 questions and the Research track 60 original subtasks. OpenAI says these constrained problems do not capture everything scientists do. A strong score there is evidence about performance on that benchmark, not proof of strength in literature synthesis, clinical interpretation or an autonomous research workflow.
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The clinical-trial study instead assesses concordance with published conclusions and scores areas including evidence interpretation, statistical understanding, clinical relevance, limitation recognition and practical applicability. The model card figures concern BioMysteryBench and LABBench2. The coding-agent preprint looks at instruction following, time and operational behavior in a simulated gravitational-wave analysis. Those are useful but distinct dimensions.
Versions and setups matter
A model name alone is not enough to reproduce a comparison. Results depend on the exact version tested, the prompt, input materials, enabled tools and scoring method. The Gemini model-card figures identify Gemini 3.8 Flash, Claude Opus 5 and GPT-5.6 Sol; they should not be transferred to other versions. The FrontierScience result specifically names GPT-5.2. The clinical-trial figures are the study’s results for the systems it evaluated, and should not be treated as scores for every current or future release bearing the same product family name.
Google DeepMind’s September 2026 model card says Gemini 3.8 Flash accepts text, image, audio and video inputs and has a context window of up to one million tokens. Those are specifications for that model card’s version and may change with later releases. Its listed benchmarks do not by themselves show how well it will handle a particular lab’s documents or tools; the card also cautions that the model can hallucinate.
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Different scores are not directly comparable
A percentage from one benchmark cannot be lined up against a percentage from another as if both were measuring the same skill. Even within a single test, a high score describes performance under that test’s conditions. It does not show that a model will cite sources correctly, run code reproducibly or recognize uncertainty in a different research setting. Treat each result as a clue about a specific capability, then test the capability that matters to your work.
How to choose a model for your research task
Start by naming the work you want to delegate or accelerate. Then select a model based on evidence that matches that job, rather than on a broad claim that it is “best for research.”
- Literature synthesis: Check whether the model accurately represents the papers you provide, distinguishes a paper’s result from its authors’ interpretation, and points you back to the supporting passages. Anthropic’s account describes Claude use for literature reviews, but does not provide an independent head-to-head result for this task.
- Statistics and clinical evidence: The Scientific Reports study is relevant to clinical-trial conclusion interpretation, but its findings apply to its evaluated sample and scoring method. Claude’s median total was 17/25 (68%), compared with ChatGPT’s 25/25 (100%), Gemini’s 21/25 (84%) and Grok3’s 18/25 (72%) in that study. Claude’s lowest median domain score was statistical understanding (3/5). These results are a reason to test statistical work carefully, not a general accuracy rate for Claude.
- Scientific reasoning problems: FrontierScience offers task-specific evidence about GPT-5.2 on its Olympiad and Research tracks. Its scores do not answer whether that model—or Claude—will be better at your practical research workflow.
- Figures, tables and mixed-media inputs: If your work depends on visual or other non-text material, check the candidate model’s current input support and test it on representative figures. Gemini 3.8 Flash’s September 2026 card lists text, image, audio and video input; do not assume a feature or performance finding carries over to another version.
- Research coding agents: Evaluate whether the agent follows methodological instructions, exposes intermediate steps and produces reproducible outputs—not only whether it finishes quickly. The gravitational-wave preprint illustrates why elapsed time alone can be misleading.
- Tool-assisted or multi-step work: Test the actual tools and sequence you expect to use. Anthropic reports Claude use for tool-assisted research, but that account is not a controlled comparison of tool-use reliability against other models.
Cost and latency can also affect a practical choice, but only compare them when current, reliable figures exist for the exact access method and workload. The evaluations described here do not establish a general price or speed winner across research tasks.
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Run a fair, small-scale evaluation
A short test using representative work is usually more informative than a broad public leaderboard. Keep the comparison controlled enough that differences are attributable to the model rather than to different prompts or source material.
- Choose representative tasks. Include the actual work you need: for example, extracting claims from a set of papers, interpreting a result, reading a figure or modifying an analysis script. Keep the scope narrow enough that a researcher can verify the output.
- Give each model the same inputs and tools. Use identical source documents, instructions, data and permitted tools. Record the exact model version and setup so the result has context and can be repeated.
- Define the scoring criteria before testing. Assess factual support, correct calculations, uncertainty handling, citation traceability, code execution and reproducibility. Add criteria specific to the task—for instance, whether a literature summary preserves disagreements among papers.
- Check claims against primary materials. Trace statements to the supplied paper, dataset or output. A fluent explanation is not proof that a result is supported, and a citation is useful only if it actually backs the claim.
- Inspect process as well as final answers. For agentic work, retain logs or intermediate outputs and check whether the model followed instructions, surfaced problems and made changes that can be audited. A fast result is not a good result if it silently changes the analysis.
- Have a qualified researcher validate consequential outputs. Independently reproduce key calculations or run code in the intended environment. Do not let an unverified model response become the sole basis for an experimental choice, clinical interpretation or published conclusion.
This approach gives you a task-specific basis for choosing among models. It also makes clear where no candidate is reliable enough without additional review.
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What the coding-agent comparison says about speed and trust
The May 27, 2026 arXiv preprint describes a narrow simulated gravitational-wave analysis. In the reported runs, Claude Code completed faster, but one run silently departed from an instruction. Codex took longer and used explicit corrective restarts. The contrast is useful because it separates two outcomes that are easy to conflate: finishing quickly and making the process visible enough to catch and correct a problem.
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Do not generalize that case into a broad Claude Code-versus-Codex verdict. It is one preprint, on one specified pipeline. For your own coding work, record elapsed time alongside instruction fidelity, intermediate outputs, error handling and whether another researcher can reproduce the result. If an agent changes the requested method without flagging it, a shorter runtime is not evidence of better research assistance.
What can—and cannot—be concluded about Claude
The evidence supports Claude as an option to evaluate for research tasks, not as the universal leader or a universal underperformer. Anthropic reports real research-partner use cases across several stages of work, but those examples do not quantify comparative accuracy or productivity. The independent clinical-trial evaluation gives Claude a lower median total than the other evaluated systems in that particular study, while the provider-published September 2026 model-card table shows Claude Opus 5 ahead of the listed alternatives on the human-solvable BioMysteryBench row and behind Gemini 3.8 Flash on LABBench2. Those findings can coexist because the evaluations, model versions and tasks differ.
No comparable, independently measured general research-productivity percentage is established by these sources. The evidence does not support turning reported anecdotes about time saved into a general productivity figure, nor does it establish that one model is best across literature review, statistics, coding, figure reading and multi-step tool use.
For a decision that matters, compare current versions on your own representative tasks, score the dimensions that affect your work, and validate the outputs. That gives a more defensible answer than combining unlike benchmark percentages into a single ranking.
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