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Serper

Fast, cheap raw web search for agents — strong on ambiguity, but too snippet-thin to replace a scrape.

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$1/1k2,755 ms p50100% dates162-char median
TL;DR — our verdictUpdated August 2026 · 1 test artifact

Best as the search layer, not the content layer

Where it wins
  • you want a cheap raw search layer for an agent, chatbot, or research workflow
  • you can add your own scraper or extractor to turn result snippets into usable text
  • you care about strong disambiguation and low-error search ranking
Main limitation
  • you need full-page extracted content in one call
Strongest test artifacts
Per-query raw outputs →

Feature scores on this page: 64.5/100 (3 scored features)

Our take

Serper is still the strongest value pick in this set for agent-facing web search: it was the cheapest and fastest tool tested, returned published dates on every result, and led the benchmark on ambiguity handling. But the rerun showed the top-3 lead was noisy rather than durable, and the 162-character median snippet length plus 1.33 extraction quality mean it cannot stand in for a content API. Use it when you want cheap ranked discovery, then add your own scraper for the text the model actually reads.

In-Depth Review

Our detailed analysis of Serper — features, performance, and real-world testing.

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AI Demos Team
Expert Reviewer
Verified Review

Feature-by-Feature Breakdown

Live Web Search Retrieval
Competitive ranking, but the rerun weakened the claim.
57.4/100
▾
Test Summary
Feature tested: Live Web Search Retrieval
Result: Partial (57.4/100) — Competitive ranking, but the rerun weakened the claim.

Feature tested: Live Web Search Retrieval

Result: Partial (57.4/100)

Verdict: Competitive ranking, but the rerun weakened the claim.

Expected behavior: Serper returns ranked web results for shared ground-truth queries, often surfacing a supporting page near the top of the list. The card's evidence covers top-1/top-3/top-10 performance and rerun variability on answerable queries.

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

Input type: Text/code file

Input used: Input artifact (Text/code file): Ground-truth query set — QUERY-SET-ground-truth.csv

Observed output: Output artifact (Text/code file): Raw per-query outputs for the 52-call /search run; the scored rerun summary reports 57.4% top-3 on run 1, 48.9% top-3 on run 2, and 98% call-level agreement. — SERPER-per-query-output.md

Input artifact: Input artifact (Text/code file): Ground-truth query set — QUERY-SET-ground-truth.csv

Output artifact: Output artifact (Text/code file): Raw per-query outputs for the 52-call /search run; the scored rerun summary reports 57.4% top-3 on run 1, 48.9% top-3 on run 2, and 98% call-level agreement. — SERPER-per-query-output.md

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

Why it matters / Conclusion: Good enough to discover supporting pages for many agent queries, but the top-3 result did not survive rerun noise.

Serper returns ranked web results for shared ground-truth queries, often surfacing a supporting page near the top of the list. The card's evidence covers top-1/top-3/top-10 performance and rerun variability on answerable queries.

csv
QUERY-SET-ground-truth.csv
Loading file...
↓
md
SERPER-per-query-output.md
Loading file...
Raw per-query outputs for the 52-call /search run; the scored rerun summary reports 57.4% top-3 on run 1, 48.9% top-3 on run 2, and 98% call-level agreement.
Bottom Line
Good enough to discover supporting pages for many agent queries, but the top-3 result did not survive rerun noise.
Entity-Aware Search Disambiguation
Best ambiguity handling in the benchmark.
86/100
▾
Test Summary
Feature tested: Entity-Aware Search Disambiguation
Result: Passed (86/100) — Best ambiguity handling in the benchmark.

Feature tested: Entity-Aware Search Disambiguation

Result: Passed (86/100)

Verdict: Best ambiguity handling in the benchmark.

Expected behavior: Serper resolves ambiguous or entity-collision queries to the intended entity more reliably than the other tools tested. The evidence frames this as its strongest performance on ambiguity-heavy queries.

Test case: Text/code file → Text prompt

Input type: Text/code file

Input used: Input artifact (Text/code file): Ambiguity / entity-collision queries in the shared ground-truth set — QUERY-SET-ground-truth.csv

Observed output: Output artifact (Text prompt): Observed result

Input artifact: Input artifact (Text/code file): Ambiguity / entity-collision queries in the shared ground-truth set — QUERY-SET-ground-truth.csv

Output artifact: Output artifact (Text prompt): Observed result

What changed: Text/code file transformed into Text prompt

Why it matters / Conclusion: This is Serper's clearest strength: if the query is ambiguous, it is the safest ranker in this set.

Serper resolves ambiguous or entity-collision queries to the intended entity more reliably than the other tools tested. The evidence frames this as its strongest performance on ambiguity-heavy queries.

file
QUERY-SET-ground-truth.csv
Loading file...
Ambiguity / entity-collision queries in the shared ground-truth set
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OUTPUT
Serper was the strongest disambiguator in the set at 86% top-3, ahead of Exa and You.com at 71% and Tavily at 14%.
Bottom Line
This is Serper's clearest strength: if the query is ambiguous, it is the safest ranker in this set.
Result Metadata with Published Dates
Metadata coverage is excellent, but freshness is only middling.
50/100
▾
Test Summary
Feature tested: Result Metadata with Published Dates
Result: Partial (50/100) — Metadata coverage is excellent, but freshness is only middling.

Feature tested: Result Metadata with Published Dates

Result: Partial (50/100)

Verdict: Metadata coverage is excellent, but freshness is only middling.

Expected behavior: Serper includes a published-date field on results, letting downstream code judge recency without an extra crawl. The evidence says published dates appeared on all results in this run, though freshness on probe queries was only partially reliable.

Test case: Text/code file → Text prompt

Input type: Text/code file

Input used: Input artifact (Text/code file): Freshness-probe queries in the shared set — QUERY-SET-ground-truth.csv

Observed output: Output artifact (Text prompt): Observed result

Input artifact: Input artifact (Text/code file): Freshness-probe queries in the shared set — QUERY-SET-ground-truth.csv

Output artifact: Output artifact (Text prompt): Observed result

What changed: Text/code file transformed into Text prompt

Why it matters / Conclusion: The date metadata is strong, but the index was only halfway reliable on genuinely fresh queries.

Serper includes a published-date field on results, letting downstream code judge recency without an extra crawl. The evidence says published dates appeared on all results in this run, though freshness on probe queries was only partially reliable.

file
QUERY-SET-ground-truth.csv
Loading file...
Freshness-probe queries in the shared set
↓
OUTPUT
Published-date coverage was 100%, while the freshness block scored 50%.
Bottom Line
The date metadata is strong, but the index was only halfway reliable on genuinely fresh queries.
Snippet-Based Result Extraction
Too thin for deep answers without another crawl.
▾
Test Summary
Feature tested: Snippet-Based Result Extraction
Result: Failed — Too thin for deep answers without another crawl.

Feature tested: Snippet-Based Result Extraction

Result: Failed

Verdict: Too thin for deep answers without another crawl.

Expected behavior: Serper returns short, snippet-like result text instead of full extracted page content. The evidence highlights limited content depth and shows that it is better for discovery than for delivering complete page text.

Test case: Text/code file → Text prompt

Input type: Text/code file

Input used: Input artifact (Text/code file): Q37 — GDPR Article 17(3) exception list — QUERY-SET-ground-truth.csv

Observed output: Output artifact (Text prompt): Observed result

Input artifact: Input artifact (Text/code file): Q37 — GDPR Article 17(3) exception list — QUERY-SET-ground-truth.csv

Output artifact: Output artifact (Text prompt): Observed result

What changed: Text/code file transformed into Text prompt

Why it matters / Conclusion: Good for ranked discovery, not for delivering the full content the model needs.

Serper returns short, snippet-like result text instead of full extracted page content. The evidence highlights limited content depth and shows that it is better for discovery than for delivering complete page text.

file
QUERY-SET-ground-truth.csv
Loading file...
Q37 — GDPR Article 17(3) exception list
↓
OUTPUT
Serper returned 1,369 characters and carried only one of the five exceptions, showing that a snippet cannot hold the full answer-bearing passage.
Bottom Line
Good for ranked discovery, not for delivering the full content the model needs.
✓ Use This If
●you want a cheap raw search layer for an agent, chatbot, or research workflow
●you can add your own scraper or extractor to turn result snippets into usable text
●you care about strong disambiguation and low-error search ranking
✕ Skip This If
●you need full-page extracted content in one call
●you need multi-source answers without a second crawl
●you need the top-3 ranking to stay fixed across reruns
developer-toolssearch-enginetextOther
On the 47 answerable queries, Serper scored 38% top-1, 57.4% top-3 on run 1, and 66% top-10. On the rerun, top-3 fell to 48.9%, though call-level agreement stayed at 98%.
Ambiguity handling. The report says Serper was the strongest disambiguator in the set, with 86% top-3 on entity-collision queries.
It returns lightweight snippets, not full extracted pages. The median result length was 162 characters, and the report's Q37 example shows that even 1,369 characters was not enough to carry all five GDPR Article 17(3) exceptions.
Yes. The report says published dates were present on 100% of returned results.
It was the fastest median tool in the benchmark at 2,755 ms p50 and 3,677 ms p95, with zero errors. The cost was $1 per 1,000 queries.
No. The report's conclusion is that Serper should be paired with a scraper or content layer, because the snippets are too thin for deep or multi-part answers.
The report says the free tier is a one-time 2,500-query grant that expires after six months. It also says the pricing page 404s to a plain fetch, so the plan details were not reliably verified from the page.

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