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OpenAI web search

A citation-honest control arm for live-web answering, but not a reliable retrieval layer for fresh content.

Visit OpenAI web search
0 false citations5/5 abstained52-query benchmarkRun 1 + rerun
TL;DR — our verdictUpdated August 2026 · 8 test artifacts

Best-in-class citation honesty, but weak freshness and unstable reruns.

Where it wins
  • You need a built-in answer API that returns citations and can abstain cleanly on no-answer questions.
  • You want the safest citation-honesty control or fallback in a web-search benchmark.
  • You care more about trustworthy answers than about building your primary retrieval layer on top of it.
Main limitation
  • You need strong freshness or last-week coverage.

Our take

OpenAI web_search is the safest built-in answer mode in this benchmark: it had zero false citations and the best no-answer behavior. But it was the weakest retrieval layer overall, missed freshness badly, and its rerun performance dropped sharply, so it works better as a control or fallback than as the grounding layer for an agent.

In-Depth Review

Our detailed analysis of OpenAI web search — features, performance, and real-world testing.

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AI Demos Team
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Verified Review

Feature-by-Feature Breakdown

Citation-backed answer synthesis
Strong
▾
Test Summary
Feature tested: Citation-backed answer synthesis
Result: Passed — Strong

Feature tested: Citation-backed answer synthesis

Result: Passed

Verdict: Strong

Expected behavior: Returns synthesized answers with citations instead of a ranked result list. The member card was exercised on answer generation with clean citations across the checkable set.

Test case: Text/code file → Text/code file

Input type: Text/code file

Input used: Input artifact (Text/code file): Fixed benchmark query set with human-verified answers and supporting URLs. — QUERY-SET-ground-truth.csv

Observed output: Output artifact (Text/code file): Citation checking export reporting a clean sheet: 0.0% false citations across the checkable set. — OPENAI-citation-verdicts.csv

Input artifact: Input artifact (Text/code file): Fixed benchmark query set with human-verified answers and supporting URLs. — QUERY-SET-ground-truth.csv

Output artifact: Output artifact (Text/code file): Citation checking export reporting a clean sheet: 0.0% false citations across the checkable set. — OPENAI-citation-verdicts.csv

What changed: Text/code file transformed into Text/code file

Test case: Text/code file → Text/code file

Input type: Text/code file

Input used: Input artifact (Text/code file): Same fixed query set used for the answer-mode run. — QUERY-SET-ground-truth.csv

Observed output: Output artifact (Text/code file): Per-query answer export for the run; the report summarizes this mode as the only one with zero false citations. — OPENAI-per-query-output.md

Input artifact: Input artifact (Text/code file): Same fixed query set used for the answer-mode run. — QUERY-SET-ground-truth.csv

Output artifact: Output artifact (Text/code file): Per-query answer export for the run; the report summarizes this mode as the only one with zero false citations. — OPENAI-per-query-output.md

What changed: Text/code file transformed into Text/code file

Why it matters / Conclusion: Best citation honesty in the set, but the exact citation choice is not stable enough to quote as a point estimate.

Returns synthesized answers with citations instead of a ranked result list. The member card was exercised on answer generation with clean citations across the checkable set.

csv
QUERY-SET-ground-truth.csv
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Fixed benchmark query set with human-verified answers and supporting URLs.
↓
csv
OPENAI-citation-verdicts.csv
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Citation checking export reporting a clean sheet: 0.0% false citations across the checkable set.
csv
QUERY-SET-ground-truth.csv
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Same fixed query set used for the answer-mode run.
↓
md
OPENAI-per-query-output.md
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Per-query answer export for the run; the report summarizes this mode as the only one with zero false citations.
Bottom Line
Best citation honesty in the set, but the exact citation choice is not stable enough to quote as a point estimate.
Abstention on unanswerable questions
Very good
4/5
▾
Test Summary
Feature tested: Abstention on unanswerable questions
Result: Partial (4/5) — Very good

Feature tested: Abstention on unanswerable questions

Result: Partial (4/5)

Verdict: Very good

Expected behavior: Declines genuinely unanswerable questions rather than inventing sources. The member card was exercised on the 5/5 unanswerable-query block, including one muddied edge case.

Test case: Text/code file → Text/code file

Input type: Text/code file

Input used: Input artifact (Text/code file): Five unanswerable queries, including Q49, where the correct behavior was to say there was no public answer rather than fabricate one. — QUERY-SET-ground-truth.csv

Observed output: Output artifact (Text/code file): Answer grading export showing 5/5 abstained, with 4/5 cleanly abstained because Q49 appended a third-party estimate after the decline. — OPENAI-answer-grades.csv

Input artifact: Input artifact (Text/code file): Five unanswerable queries, including Q49, where the correct behavior was to say there was no public answer rather than fabricate one. — QUERY-SET-ground-truth.csv

Output artifact: Output artifact (Text/code file): Answer grading export showing 5/5 abstained, with 4/5 cleanly abstained because Q49 appended a third-party estimate after the decline. — OPENAI-answer-grades.csv

What changed: Text/code file transformed into Text/code file

Why it matters / Conclusion: Best abstention behavior in the benchmark, with one muddied edge case.

Declines genuinely unanswerable questions rather than inventing sources. The member card was exercised on the 5/5 unanswerable-query block, including one muddied edge case.

file
QUERY-SET-ground-truth.csv
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Five unanswerable queries, including Q49, where the correct behavior was to say there was no public answer rather than fabricate one.
↓
csv
OPENAI-answer-grades.csv
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Answer grading export showing 5/5 abstained, with 4/5 cleanly abstained because Q49 appended a third-party estimate after the decline.
Bottom Line
Best abstention behavior in the benchmark, with one muddied edge case.
Entity disambiguation
Strong
6/7
▾
Test Summary
Feature tested: Entity disambiguation
Result: Passed (6/7) — Strong

Feature tested: Entity disambiguation

Result: Passed (6/7)

Verdict: Strong

Expected behavior: Resolves ambiguous entities instead of silently choosing the wrong one. The member card was exercised on the Apollo pricing collision, where it identified Apollo.io and flagged the ambiguity.

Test case: Text/code file → Text/code file

Input type: Text/code file

Input used: Input artifact (Text/code file): Q23 Apollo pricing / entity collision probe, where 'Apollo' could mean more than one product or company. — QUERY-SET-ground-truth.csv

Observed output: Output artifact (Text/code file): Per-query output showing the Apollo query resolved to Apollo.io, with the collision explicitly called out. — OPENAI-per-query-output.md

Input artifact: Input artifact (Text/code file): Q23 Apollo pricing / entity collision probe, where 'Apollo' could mean more than one product or company. — QUERY-SET-ground-truth.csv

Output artifact: Output artifact (Text/code file): Per-query output showing the Apollo query resolved to Apollo.io, with the collision explicitly called out. — OPENAI-per-query-output.md

What changed: Text/code file transformed into Text/code file

Why it matters / Conclusion: Clear disambiguation is a real strength, and the tool names the collision out loud.

Resolves ambiguous entities instead of silently choosing the wrong one. The member card was exercised on the Apollo pricing collision, where it identified Apollo.io and flagged the ambiguity.

file
QUERY-SET-ground-truth.csv
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Q23 Apollo pricing / entity collision probe, where 'Apollo' could mean more than one product or company.
↓
md
OPENAI-per-query-output.md
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Per-query output showing the Apollo query resolved to Apollo.io, with the collision explicitly called out.
Bottom Line
Clear disambiguation is a real strength, and the tool names the collision out loud.
Multi-source synthesis
Excellent
6/6
▾
Test Summary
Feature tested: Multi-source synthesis
Result: Passed (6/6) — Excellent

Feature tested: Multi-source synthesis

Result: Passed (6/6)

Verdict: Excellent

Expected behavior: Combines information from more than one source page when the answer requires it. The member card was exercised on the multi-source block that went 6/6.

Test case: Text/code file → Text/code file

Input type: Text/code file

Input used: Input artifact (Text/code file): Six queries that required combining evidence from two pages rather than taking a single snippet at face value. — QUERY-SET-ground-truth.csv

Observed output: Output artifact (Text/code file): Answer grading export showing 6/6 correct on the multi-source block. — OPENAI-answer-grades.csv

Input artifact: Input artifact (Text/code file): Six queries that required combining evidence from two pages rather than taking a single snippet at face value. — QUERY-SET-ground-truth.csv

Output artifact: Output artifact (Text/code file): Answer grading export showing 6/6 correct on the multi-source block. — OPENAI-answer-grades.csv

What changed: Text/code file transformed into Text/code file

Why it matters / Conclusion: Strong synthesis across sources; this was one of its better-performing blocks.

Combines information from more than one source page when the answer requires it. The member card was exercised on the multi-source block that went 6/6.

file
QUERY-SET-ground-truth.csv
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Six queries that required combining evidence from two pages rather than taking a single snippet at face value.
↓
csv
OPENAI-answer-grades.csv
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Answer grading export showing 6/6 correct on the multi-source block.
Bottom Line
Strong synthesis across sources; this was one of its better-performing blocks.
Deep-page content extraction
Mixed
5/6
▾
Test Summary
Feature tested: Deep-page content extraction
Result: Partial (5/6) — Mixed

Feature tested: Deep-page content extraction

Result: Partial (5/6)

Verdict: Mixed

Expected behavior: Pulls answer-bearing text from inside long pages rather than relying only on meta descriptions or snippets. The member card was exercised on the content-depth block with buried passages.

Test case: Text/code file → Text/code file

Input type: Text/code file

Input used: Input artifact (Text/code file): Six queries where the answer was buried deep inside a long page and had to be extracted, not just guessed from a snippet. — QUERY-SET-ground-truth.csv

Observed output: Output artifact (Text/code file): Run export summarizing the content-depth performance and the overall extraction-quality score of 1.67. — OPENAI-scored-run-export.csv

Input artifact: Input artifact (Text/code file): Six queries where the answer was buried deep inside a long page and had to be extracted, not just guessed from a snippet. — QUERY-SET-ground-truth.csv

Output artifact: Output artifact (Text/code file): Run export summarizing the content-depth performance and the overall extraction-quality score of 1.67. — OPENAI-scored-run-export.csv

What changed: Text/code file transformed into Text/code file

Why it matters / Conclusion: Usable on buried passages, but only mid-pack compared with the stronger extraction tools.

Pulls answer-bearing text from inside long pages rather than relying only on meta descriptions or snippets. The member card was exercised on the content-depth block with buried passages.

file
QUERY-SET-ground-truth.csv
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Six queries where the answer was buried deep inside a long page and had to be extracted, not just guessed from a snippet.
↓
csv
OPENAI-scored-run-export.csv
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Run export summarizing the content-depth performance and the overall extraction-quality score of 1.67.
Bottom Line
Usable on buried passages, but only mid-pack compared with the stronger extraction tools.
Current web fact lookup
Strong
9/10
▾
Test Summary
Feature tested: Current web fact lookup
Result: Failed (9/10) — Strong

Feature tested: Current web fact lookup

Result: Failed (9/10)

Verdict: Strong

Expected behavior: Finds live factual answers on the web, including recent pages and release-date style lookups. The member cards were exercised on ordinary current-fact queries and on freshness/recent-release probes.

Test case: Text/code file → Text/code file

Input type: Text/code file

Input used: Input artifact (Text/code file): Six freshness queries, including Q43 about the Python 3.11.16 release date as of 15 Aug 2026. — QUERY-SET-ground-truth.csv

Observed output: Output artifact (Text/code file): Answer grading export showing weak freshness performance: 2/6 correct on the freshness block. — OPENAI-answer-grades.csv

Input artifact: Input artifact (Text/code file): Six freshness queries, including Q43 about the Python 3.11.16 release date as of 15 Aug 2026. — QUERY-SET-ground-truth.csv

Output artifact: Output artifact (Text/code file): Answer grading export showing weak freshness performance: 2/6 correct on the freshness block. — OPENAI-answer-grades.csv

What changed: Text/code file transformed into Text/code file

Why it matters / Conclusion: Solid for everyday live facts, but not the strongest retrieval layer overall.

Finds live factual answers on the web, including recent pages and release-date style lookups. The member cards were exercised on ordinary current-fact queries and on freshness/recent-release probes.

file
QUERY-SET-ground-truth.csv
Loading file...
Six freshness queries, including Q43 about the Python 3.11.16 release date as of 15 Aug 2026.
↓
csv
OPENAI-answer-grades.csv
Loading file...
Answer grading export showing weak freshness performance: 2/6 correct on the freshness block.
Bottom Line
Solid for everyday live facts, but not the strongest retrieval layer overall.
Long-tail technical fact retrieval
Strong
11/12
▾
Test Summary
Feature tested: Long-tail technical fact retrieval
Result: Passed (11/12) — Strong

Feature tested: Long-tail technical fact retrieval

Result: Passed (11/12)

Verdict: Strong

Expected behavior: Answers obscure or niche technical queries better than freshness probes. The member card was exercised on the niche-technical block with long-tail technical facts.

Test case: Text/code file → Text/code file

Input type: Text/code file

Input used: Input artifact (Text/code file): Twelve obscure or long-tail technical questions, including library errors and smaller repos that are harder for large indexes to cover. — QUERY-SET-ground-truth.csv

Observed output: Output artifact (Text/code file): Answer grading export showing 11/12 correct on the niche-technical block. — OPENAI-answer-grades.csv

Input artifact: Input artifact (Text/code file): Twelve obscure or long-tail technical questions, including library errors and smaller repos that are harder for large indexes to cover. — QUERY-SET-ground-truth.csv

Output artifact: Output artifact (Text/code file): Answer grading export showing 11/12 correct on the niche-technical block. — OPENAI-answer-grades.csv

What changed: Text/code file transformed into Text/code file

Why it matters / Conclusion: Good on long-tail technical facts, even though freshness is much weaker.

Answers obscure or niche technical queries better than freshness probes. The member card was exercised on the niche-technical block with long-tail technical facts.

file
QUERY-SET-ground-truth.csv
Loading file...
Twelve obscure or long-tail technical questions, including library errors and smaller repos that are harder for large indexes to cover.
↓
csv
OPENAI-answer-grades.csv
Loading file...
Answer grading export showing 11/12 correct on the niche-technical block.
Bottom Line
Good on long-tail technical facts, even though freshness is much weaker.
✓ Use This If
●You need a built-in answer API that returns citations and can abstain cleanly on no-answer questions.
●You want the safest citation-honesty control or fallback in a web-search benchmark.
●You care more about trustworthy answers than about building your primary retrieval layer on top of it.
✕ Skip This If
●You need strong freshness or last-week coverage.
●You need stable citations or reproducible reruns.
●You need a ranked result list or a dedicated search API with higher retrieval recall.
developer-toolssearch-enginetextOther
Yes. In this benchmark it had 0.0% false citations across the checkable set, making it the only tool with a clean citation sheet.
It abstained on all 5 of 5 unanswerable queries. The only caveat is Q49: it correctly said the answer was not publicly disclosed, then also surfaced a third-party estimate.
No. Freshness was its weakest area: only 2 of 6 freshness answers were correct, retrieval on that block was 0%, and Q43 missed a Python release that had shipped four days earlier.
Not very. Answer-mode agreement was 88%, retrieval fell from about 25.5% in run 1 to 14.9% in run 2, and the report says run 2 was 57% slower.
The corrected all-in cost was $0.01315 per query, or $13.15 per 1,000 queries. The pipeline's flat $0.010 figure undercounted because it omitted token usage.
Yes on the Apollo ambiguity probe. It resolved the query to Apollo.io and explicitly flagged the entity collision.

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