Criteria
Five things to check when comparing deep research tools
A fluent report is not the same as a reliable one. These five factors decide whether a report can actually feed into your research.
Do claims trace back to sources?
A working link only proves the source exists. Check whether the original supports the numbers, judgments and causal claims in the report, and whether the source type fits the claim, such as an official document versus a repost for a policy statement.
Can you keep working on the same material?
Ask the tool to add a source, revise one section and leave the rest intact. If every follow-up amounts to a rewrite, or papers screened in the last round are gone, the rework adds up fast.
Can it run code and experiments?
Some questions cannot be answered by reading, such as testing a hypothesis on public data or reproducing a figure from a paper. Here the question is whether the tool can download data, write code, run it and keep the results.
Is the process reproducible?
Check whether search logs, screening reasons, code, dependencies and intermediate results are kept. With only a final report and no trail, others cannot audit the work and you cannot easily pick it up again.
Do you need to stay online?
It does not matter for a few minutes of research. For tasks that run for hours or days, check whether the work continues after you close your laptop and whether you can follow progress from another device.
Comparison
Three tools compared on the same dimensions
Deep research in ChatGPT and Kimi centers on searching, reading and synthesizing a report. Scientify also writes code and runs experiments, and keeps the whole process in one workspace.
| Dimension | ChatGPT Deep Research | Kimi Deep Research | Scientify |
|---|---|---|---|
| Source coverage | Public webpages, uploaded files and connected data sources | Public webpages and materials supplied by the user | Organizes its own search strategy around the project and tries to fetch full-text PDFs |
| Main output | A synthesized research report with source links | A Chinese-language research report with source links | Code, data, figures, experiment results and a report |
| Tracing claims to sources | Open each source page and check it against the claim | Open each source and check its date, author and original context | Papers and full texts stay in the workspace for side-by-side checking |
| Continuing the work | Ask follow-up questions and revise the report | Add material and revise within the conversation | Iterates in the same workspace based on the previous round's results |
| Running code and experiments | Focused on search and synthesis; check official documentation for related features | Focused on search and synthesis; check official documentation for related features | Writes and runs code, executes experiments and scientific computations |
| Reproducibility | Keeps the report and source links; check official documentation for how the search trail is kept | Keeps the report and source links; check official documentation for how the search trail is kept | Papers, code, dependencies, data, logs and results are stored together |
| Do you need to stay online? | Check official documentation | Check official documentation | Runs in an isolated cloud computer, continues after you close your laptop, progress viewable from your phone |
| Best suited to | Cross-source industry, policy, market and background research | Topics with substantial Chinese-language web material | Long-running projects that need data analysis, simulation or experiments |
Test
Test with one shared task instead of relying on reviews
- 01
Use the same task
Give every tool the same research question, time range, source requirements and report structure. Save the original prompt and the generation time so you can compare later.
- 02
Spot-check citations
Check the same number of sources in each report. Record whether links work, whether the original supports the claim, the publication date and the source type.
- 03
Test whether the work can continue
Ask for one specific source to be added and one section revised while the rest stays intact. Note any new errors and how much manual cleanup is needed.
- 04
Add a task that needs execution
If your project ultimately rests on data, add one more step: ask the tool to test a claim from the report on a public dataset, and see whether it produces runnable code and results you can inspect.
What Scientify does differently
Scientify keeps the research going instead of stopping at a report
Scientify is a scientific agent that runs in an isolated cloud computer. Give it a research goal and it keeps organizing its search strategy, tries to obtain full-text PDFs, forms hypotheses, writes code and runs experiments, then decides the next round based on the results.
Papers, code, dependencies, data, logs, figures and results live in one workspace, so the research can keep iterating and the resulting code can be reproduced. The task keeps running after you close your laptop, and you can check progress from your phone or any other device.
- Connects to compute and licensed scientific software to build models and run chemistry, materials and mechanics calculations
- The core agent is open source with 2k+ GitHub stars, and the related paper was accepted at ICML 2026
- Conversation history and workspace files stay in the isolated cloud computer; Scientify servers do not store this research data
- Offers models such as GPT-5.6-sol at roughly 30% of standard API pricing for the same usage
Combining tools
The three tools can split the work
Deep research reports and research agents cover different stages of a project, so you do not have to pick just one.
- Get oriented: use deep research in ChatGPT or Kimi to map a field, policies and industry developments, and screen web sources against scholarly standards before relying on them.
- Confirm the literature: check each key claim in the report against the original and keep a list of papers you can actually use.
- Run the verification: hand the research question, confirmed papers and next-step plan to Scientify so it can write code, run experiments and iterate in one workspace.
- Make the calls yourself: research gaps, method choices and interpretation are the researcher's responsibility, and every AI output needs checking.
References
- Scientify open-source agent (GitHub) — core agent source code
- Scientify paper (arXiv) — accepted at ICML 2026