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There is no single, universally proven winner for deep research. Google’s DeepSearchQA leaderboard, updated December 10, 2025, puts Gemini Deep Research Agent first with an 81.9% F1 score, ahead of GPT-5 Pro at 79.0%. A separate LiveResearchBench snapshot ranks Grok-4 Deep Research first at 69.4, followed by Perplexity at 67.1, Gemini at 66.4 and OpenAI o3 at 62.9. Those results measure different tasks, prompts, model versions and judging systems, so they cannot be combined into one definitive ranking.
If your priority is the benchmark that measures comprehensive, fully supported research, Gemini has the strongest published result in this set. If you care about the LiveResearchBench snapshot, Grok leads. The responsible conclusion is to match the tool to your task and verify the citations rather than repeat the claim that one service wins every use case.
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The answer depends on the evaluation. Gemini Deep Research Agent leads Google’s DeepSearchQA leaderboard, while Grok-4 Deep Research leads the cited LiveResearchBench snapshot. A third comparison, the Deep Research Arena’s RACE board, also places Gemini first, but with a different score and judging setup. These are useful signals, not a universal championship.
For a reader choosing one service today, the practical interpretation is:
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- Choose Gemini as the best-supported starting point for comprehensiveness: it has the highest result in the dated DeepSearchQA and RACE figures cited here.
- Consider Grok for the LiveResearchBench-style task mix: Grok-4 Deep Research leads that published snapshot.
- Treat Perplexity and ChatGPT as task-dependent options: Perplexity is second on LiveResearchBench, while GPT-5 Pro is second on DeepSearchQA and OpenAI’s deep-research system appears in the middle of the RACE and LiveResearchBench tables.
None of those statements proves that a particular assistant will produce the best answer to your own question. Your results can change with the model tier, browsing permissions, date, prompt wording, source availability and whether the system is asked to trade speed for depth.
What do the published head-to-head benchmarks show?
DeepSearchQA: Gemini leads on F1 and fully correct answers
Google describes DeepSearchQA as a benchmark for the comprehensiveness of deep-research agents. Its leaderboard, updated December 10, 2025, reports several outcome categories, including fully correct, fully incorrect, correct with excessive answers and F1. F1 combines precision and recall, so it rewards answers that cover the needed material without adding too much unsupported content.
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| System | DeepSearchQA F1 | Fully correct | What the figure means |
|---|---|---|---|
| Gemini Deep Research Agent | 81.9% | 66.1% | Highest result on this leaderboard; measured for the benchmark’s defined research questions and scoring rules. |
| GPT-5 Pro | 79.0% | 65.2% | Second on the same dated leaderboard, 2.9 F1 points behind Gemini. |
The 2.9-point F1 gap is meaningful within this test, but it is not a forecast of a 2.9-point advantage on every topic. The leaderboard’s fully correct rate is lower than its F1 score because an answer can cover most required facts while still missing a requirement or including an excess answer.
RACE: another comparison still puts Gemini first
The Deep Research Arena’s 2025 RACE results report a different scale and scoring process. Gemini recorded 49.98, OpenAI deep research 47.84 and Perplexity Research 43.05. The systems, prompts, task mix and judges are not identical to DeepSearchQA, so the RACE values should be read as a separate snapshot rather than converted into percentages or averaged with F1.
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| RACE entry | Score | Position in the cited table |
|---|---|---|
| Gemini | 49.98 | First |
| OpenAI deep research | 47.84 | Second |
| Perplexity Research | 43.05 | Third |
LiveResearchBench: Grok leads a different snapshot
A snapshot from Salesforce AI Research, UW–Madison and Stanford reports Grok-4 Deep Research at 69.4, Perplexity at 67.1, Gemini Deep Research at 66.4 and OpenAI o3 Deep Research at 62.9. Because this is a separate benchmark family, its ranking does not cancel the DeepSearchQA result; it shows how changing the questions, source environment, model versions or judges can change the order.
| System | LiveResearchBench score | Rank in the cited snapshot |
|---|---|---|
| Grok-4 Deep Research | 69.4 | 1 |
| Perplexity | 67.1 | 2 |
| Gemini Deep Research | 66.4 | 3 |
| OpenAI o3 Deep Research | 62.9 | 4 |
Why these numbers cannot be merged
A single league table would be misleading for four reasons:
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- Different model labels: “Gemini” can mean a particular Deep Research Agent, while “OpenAI” entries refer to GPT-5 Pro, deep research or o3 in different rows.
- Different dates and web conditions: a browsing agent can see changing pages, updated documents and altered search indexes.
- Different judges and prompts: human or model judges may reward completeness, source quality, reasoning, style or another balance.
Stanford’s HELM Capabilities leaderboard illustrates the same limitation from another direction. Its tied 0.727 mean scores for Grok 3 Beta and GPT-4.1 are broad capability results, not a direct contest between research agents, so they should not replace a deep-research benchmark.
How should you compare ChatGPT, Gemini, Perplexity and Grok?
Scores are only one part of a useful comparison. For a report you intend to trust, inspect the following dimensions separately.
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| Comparison axis | What to check | Why it matters |
|---|---|---|
| Factual correctness | Does each material claim agree with the cited primary source? | A fluent report can still contain one damaging error. |
| Citation traceability | Can you open the exact page, find the quoted passage and confirm that it supports the claim? | A long reference list is not proof of evidence. |
| Source breadth and quality | Does the agent use official records, technical papers and original datasets rather than repeating one article? | Independent sources reduce circular reporting. |
| Research-plan execution | Does it break a broad question into subquestions, search each one and identify gaps? | Multi-step planning is the point of a research agent rather than a simple chatbot. |
| Conflict handling | Does it show competing figures, explain dates and say which source is stronger? | Disagreement is common in policy, science, markets and product specifications. |
| Speed and cost | Record elapsed time, usage limits and the price of the exact tier tested. | A slightly better report may not justify a much slower or more expensive workflow. |
| Model and version | Write down the named model, agent mode, date and browsing setting. | “ChatGPT” or “Gemini” alone is not a reproducible test description. |
What does the evidence say about each service?
Gemini Deep Research
Gemini has the strongest numerical case in the available comprehensiveness-focused results: 81.9% F1 and 66.1% fully correct on DeepSearchQA, plus a 49.98 RACE score. Its 66.4 LiveResearchBench result is lower than Grok’s score in that snapshot, demonstrating that a lead on one task family does not transfer automatically to another.
ChatGPT and OpenAI’s research agents
GPT-5 Pro scores 79.0% F1 and 65.2% fully correct on DeepSearchQA, second behind Gemini on that leaderboard. OpenAI deep research scores 47.84 on RACE, while OpenAI o3 Deep Research scores 62.9 on the cited LiveResearchBench snapshot. These labels represent different entries, so they should not be treated as one unchanging ChatGPT score.
OpenAI’s 2025 launch evaluation reported 26.6% accuracy on its cited expert benchmark and a 67.36% GAIA pass@1 result. Those figures are historical launch context, not evidence that the current service is superior to every competitor. OpenAI’s announcement described the design goal this way: “The model powering deep research showcased a human-like approach by effectively seeking out specialized information when necessary.” That is a product description, not an independent ranking.
Perplexity
Perplexity Research records 43.05 on the cited RACE table, below Gemini and OpenAI deep research, but Perplexity scores 67.1 on LiveResearchBench, second only to Grok-4 Deep Research. The spread shows why a service’s usefulness can depend heavily on the benchmark’s source mix and task design.
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Grok
Grok-4 Deep Research leads the cited LiveResearchBench snapshot with 69.4. No Grok value is supplied for the DeepSearchQA or RACE rows discussed above, so it would be inaccurate to infer how Grok would rank there.
How can you run a fair personal comparison?
A credible hands-on test needs more documentation than four screenshots. Use this procedure so another reader can reproduce your result.
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- Define the question set. Use at least five tasks covering factual lookup, a multi-source comparison, a question with conflicting evidence, a numerical calculation and a topic requiring primary documents. Keep the wording identical for every service.
- Lock the versions. Record the model name, agent mode, browsing or search setting, region and test date. Do not compare a paid research agent with a default chat mode without saying so.
- Run more than once when possible. Save every output, not just the best response. A single run cannot show how much answers vary.
- Use a written rubric. Score factual accuracy, completeness, citation support, source quality, conflict handling, instruction following and readability separately. Decide in advance whether an unsupported claim is a minor error or a failure.
- Check every important citation. Open the linked source, verify the date and confirm that it supports the exact sentence. Mark dead links, search-result snippets and sources that merely repeat another report.
- Test disagreement explicitly. Give the agents two credible sources with different numbers and ask them to reconcile the dates, definitions and populations instead of choosing silently.
- Measure practical constraints. Record start-to-finish time, number of follow-up prompts, usage limits and the price shown for the tested tier. The available public material does not provide a verified, current cost or latency comparison for these runs.
- Publish the raw evidence. Include the prompt set, run count, model versions, scoring rubric, source-quality rules and representative outputs. Without those details, “I tested four AIs” is an anecdote rather than an auditable comparison.
A reusable test prompt
Investigate [question]. Use primary sources where available. List the subquestions you will answer, show conflicting evidence with dates, provide a source link for every material claim, separate established facts from inference, and state what you could not verify.Which service fits different research jobs?
The following choices are evidence-based starting points, not guarantees. Re-run the comparison when the model or agent changes.
| Your priority | Reasonable first choice | Qualification |
|---|---|---|
| Maximum comprehensiveness on the published DeepSearchQA setup | Gemini Deep Research | It leads that dated leaderboard at 81.9% F1; your topic may differ. |
| Tasks resembling the cited LiveResearchBench snapshot | Grok-4 Deep Research | It leads that snapshot at 69.4, but the result is not interchangeable with DeepSearchQA. |
| A source-heavy exploratory workflow | Perplexity or Gemini | Perplexity is second on LiveResearchBench, while Gemini leads RACE; verify citation quality yourself. |
| OpenAI-specific workflows or GPT-5 Pro evaluation | ChatGPT’s relevant research mode | Use the exact model label; GPT-5 Pro, deep research and o3 are separate benchmark entries. |
| Lowest cost or fastest response | Undetermined from the published figures | No verified, same-day price and latency dataset is supplied, so measure the tier you can actually access. |
What are the common failure modes?
- Confident but uncited claims: ask for a link beside every material statement, then verify it manually.
- Outdated information: require publication dates and a “checked on” date for changing figures.
- Source laundering: trace a popular article back to the original filing, paper, dataset or announcement.
- Silent resolution of conflicts: request a side-by-side explanation of definitions, samples and dates.
- Overlong reports with little evidence: score claim coverage and citation support, not word count.
- Non-reproducible comparisons: publish the exact prompt, model version, number of runs and rubric.
What is the defensible verdict?
Gemini is the strongest choice if you define “best” as the highest result on the dated DeepSearchQA comprehensiveness leaderboard, and it also leads the cited RACE table. Grok-4 Deep Research is the leader in the cited LiveResearchBench snapshot. Because those evaluations disagree and use different systems, dates and metrics, the headline claim of one clear overall winner is not supported.
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The most reliable workflow is therefore benchmark-aware: select a likely fit, run a small identical test on your real questions, inspect the underlying sources and keep the raw outputs. That process tells you more about your research needs than a single cross-brand ranking.
Frequently Asked Questions
Can benchmark scores predict which AI will answer my niche question best?
Only imperfectly. Benchmarks estimate performance on a defined task distribution, while a niche question may involve specialist terminology, paywalled sources or local regulations that were absent from the test. Use the published score to choose candidates, then validate them on representative questions.
Are ChatGPT, Gemini, Perplexity and Grok benchmarked at the same subscription level?
Not necessarily. The reported entries use named agents and model versions such as GPT-5 Pro, OpenAI o3 Deep Research and Grok-4 Deep Research, rather than a single standardized consumer plan. A fair article must state the exact tier and access conditions for every run.
How often should I repeat a deep-research comparison?
Repeat it after a model, agent mode or browsing system changes, and periodically for fast-moving subjects. Web indexes, source pages and safety policies can change even when the product name stays the same. Keeping a dated test set makes performance drift visible.
What should I do when two agents cite different numbers?
Check whether they used different definitions, populations, currencies or publication dates before deciding that one is wrong. Prefer the primary or most recent source when it matches your question, and report the disagreement if the evidence remains unresolved.
Quick Recap
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