--- title: "Best Web Search APIs for Developers & Coding Agents (2026): Comparison, Independently Benchmarked" description: "Perplexity leads search at 77.3% accuracy (task completion). Fastest search: Parallel turbo, 333ms. Lowest recorded query price: TinyFish, $0.00 per 1,000 queries (free-tier limits apply). Exa deep leads search + scrape at 83.0%. Compare the best web search APIs for developers and coding agents. Independent 2026 benchmark of Exa, Tavily, Brave, Parallel and Firecrawl." url: https://openbenchmarks.com/web-search-for-coding-agents --- [](https://openbenchmarks.com/) [← web search benchmark](https://openbenchmarks.com/web-search) [benchmarks](https://openbenchmarks.com/) / [web-search](https://openbenchmarks.com/web-search) / web search for coding agents 100 tasks · search & fetch · search-only · last updated 26 Sept 2026 # Web Search for Coding Agents Benchmark Exa deep leads with fetch at 83.0% task completion on 100 tickets. Perplexity leads search-only at 77.3%. Different APIs lead depending on whether the agent can open pages, so the two boards are ranked separately and never averaged. **Why we built this.** This is the hard retrieval task of the [web search benchmark](https://openbenchmarks.com/web-search). We evaluate web search APIs for coding agents and developers using realistic product tickets. We test search APIs on **hard enterprise SaaS documentation**. The agent receives a small existing `httpx`client and must patch it against the vendor's **current official API documentation**. The ticket describes the desired capability in natural language, but deliberately does **not** reveal the exact implementation details. **Model-only baseline: 0.** A submission only passes if the patch compiles, the ground-truth tokens are present, and each is grounded in a `# source:`URL that actually appeared in that run's search or fetch results. Without a search tool the model cannot provide that URL, so it cannot pass. The ticket also does not name the implementation detail, so the path cannot be recalled. **Sample task.** A sales team needs to price a quote in ServiceNow Sales CRM. The file the agent gets already makes a live ServiceNow request, but it is the default help-desk call: read the incident table. The gold path is one endpoint. ticket Price this quote using the current Sales CRM pricing engine. The starter still reads the incident table, which is the help-desk API for listing support tickets — that cannot compute a price. Look up the official docs and switch the call. what the docs actually require `POST /api/sn_csm_pricing/v1/pricingengine/computePrice` The inherited code would run today: `GET /api/now/table/incident`. That returns help-desk tickets. It does not price a quote. The path above lives in Sales CRM pricing docs and is not written anywhere in the ticket. starter → required patch @@ price.py def run() -> dict: - response = httpx.get( - f"{BASE}/api/now/table/incident", + response = httpx.post( + # source: https://www.servicenow.com/docs/.../sales_crm_pricing-POST-compute-price.html + f"{BASE}/api/sn_csm_pricing/v1/pricingengine/computePrice", headers={ "Authorization": f"Bearer {TOKEN}", "Accept": "application/json", }, Red is the inherited client. Green is the change after the agent finds the official docs. The `# source:`comment has to be a docs URL that actually showed up in that run's search or fetch results and not a URL hallucinated by the model. **How it is measured.** The model receives only the ticket and the starter code. The search or fetch provider is varied while the model (`gpt-5.6-sol`), task, budgets (5 search, 32 turns), and runner remain fixed. A submission passes only if the submitted file compiles, all ground-truth tokens are present, each has an associated `# source:` URL, and that URL actually appeared in the search or fetch results from that run. Both boards are published: search & fetch, then search-only. **Vendors benchmarked.** Exa deep, Exa auto, TinyFish, Perplexity, Parallel advanced, Parallel basic, Firecrawl, Nimble, Tavily advanced, Tavily basic, You, Linkup standard, Parallel fast, Exa fast, Parallel turbo, Exa instant, Linkup fast, Brave. Every task is evaluated under two tool configurations: **search only** and **search & fetch**. When fetch is enabled, `web_fetch` goes through the same vendor under test as `web_search`— a search & fetch pair from one vendor, not search from one and fetch from another. Vendors without a fetch endpoint, such as Brave, appear only on the search-only board. **How to pick a provider.**Start from whether fetch is the vendor's or yours. There is no single best search API. It is a tradeoff between grounded task completion, latency, tokens, and LLM cost. * **Search & fetch.** The agent searches and fetches through the same vendor. Read the [search & fetch](#leaderboard) board. * **Search only, bring your own fetch.** The agent only calls `web_search`. Use this if fetch is your own scrape, browser, or another vendor. Read the [search-only](#leaderboard-search-only) board. * **Highest grounded completion.** Read `Task completion` on the board that matches your tool setup. * **Fastest loop.** Read `Median task time` and `Avg search time`. * **Leanest context.** Read `Median task tokens`. * **Lowest all-in cost.** Read `Median task cost`. That is median LLM spend plus search/fetch list price per ticket. `API list price` is the unit rate of the search (and fetch) call. [Most accurate](https://openbenchmarks.com/web-search-for-coding-agents/most-accurate-search-api) · [Fastest](https://openbenchmarks.com/web-search-for-coding-agents/fastest-search-api) · [Most token efficient](https://openbenchmarks.com/web-search-for-coding-agents/most-token-efficient-search-api) 2026-09-15 Benchmarked Nimble lite and standard plus You highlights with and without knowledge=core on search-only and search + fetch; 100 tasks across three repeats per configuration and mode. [Full changelog →](#changelog) [Open Code Harness, scoring and vendor runners in openbenchmarks-labs/web-search-for-coding-agents. github → ](https://github.com/openbenchmarks-labs/web-search-for-coding-agents "Open the benchmark code on GitHub") [public dataset 30 public tickets on Hugging Face as openbenchmarks/OB-Code-Websearch. Boards are scored on a held-out private set that is not distributed so that vendors and models cannot train and fit to the Benchmark. huggingface → ](https://huggingface.co/datasets/openbenchmarks/OB-Code-Websearch "Open the public dataset on Hugging Face") tasks 100 boards 2 [Exasearch & fetch](https://exa.ai "Open Exa in a new tab") [TinyFishsearch & fetch](https://www.tinyfish.ai "Open TinyFish in a new tab") [Perplexitysearch & fetch](https://www.perplexity.ai "Open Perplexity in a new tab") [Parallelsearch & fetch](https://parallel.ai "Open Parallel in a new tab") [Firecrawlsearch & fetch](https://firecrawl.dev "Open Firecrawl in a new tab") [Nimblesearch & fetch](https://www.nimbleway.com "Open Nimble in a new tab") [Tavilysearch & fetch](https://tavily.com "Open Tavily in a new tab") [Yousearch & fetch](https://you.com "Open You in a new tab") [Linkupsearch & fetch](https://www.linkup.so "Open Linkup in a new tab") [Perplexitysearch-only](https://www.perplexity.ai "Open Perplexity in a new tab") [Firecrawlsearch-only](https://firecrawl.dev "Open Firecrawl in a new tab") [Parallelsearch-only](https://parallel.ai "Open Parallel in a new tab") [Exasearch-only](https://exa.ai "Open Exa in a new tab") [TinyFishsearch-only](https://www.tinyfish.ai "Open TinyFish in a new tab") [Tavilysearch-only](https://tavily.com "Open Tavily in a new tab") [Nimblesearch-only](https://www.nimbleway.com "Open Nimble in a new tab") [Linkupsearch-only](https://www.linkup.so "Open Linkup in a new tab") [Yousearch-only](https://you.com "Open You in a new tab") [Brave Searchsearch-only](https://brave.com/search/api/ "Open Brave Search in a new tab") \[01\] evidence ## Benchmark Data Each chart ranks its own top eight; no single column decides the order. The tables list the full field alphabetically, with quality, latency, token usage and cost side by side. \[01.a\] search & fetch ### Exa deep completes most tasks at 83.0% Perplexity is fastest at 21.5 s TinyFish costs least at $0.050 Top 8 per metric __Search & fetch coding-agent board, listed alphabetically__ | Vendor | Endpoint & configuration | Task completion | Median task time | Median task cost | Avg search time | Median task tokens | API list price | | --- | --- | --- | --- | --- | --- | --- | --- | | [Exa auto](https://exa.ai "Open Exa auto in a new tab") | POST /search type=auto POST /contents | 81.7 ± 1.1 | 23s | $0.107 $0.032 search+fetch $0.074 token cost | 1.19s | 27,433 | $0.007 / search $0.001 / fetch | | [Exa deep](https://exa.ai "Open Exa deep in a new tab") | POST /search type=deep POST /contents | 83.0 ± 1.0 | 37s | $0.127 $0.057 search+fetch $0.070 token cost | 3.97s | 23,660 | $0.012 / search $0.001 / fetch | | [Firecrawl](https://firecrawl.dev "Open Firecrawl in a new tab") | POST /v2/search POST /v2/scrape | 76.0 ± 1.0 | 34s | $0.082 $0.028 search+fetch $0.055 token cost | 2.81s | 17,379 | $0.005 / search $0.0025 / fetch | | [Linkup standard](https://www.linkup.so "Open Linkup standard in a new tab") | POST /v1/search depth=standard POST /v1/fetch mode=standard | 48.3 ± 4.9 | 48s | $0.147 $0.038 search+fetch $0.108 token cost | 2.00s | 57,791 | $0.005 / search $0.005 / fetch | | [Nimble](https://www.nimbleway.com "Open Nimble in a new tab") | POST /v2/search search\_depth=lite · full\_content=false · focus=general POST /v2/extract formats=\[markdown\] | 60.3 ± 2.5 | 40s | $0.054 $0.054 token cost | 1.93s | 16,828 | $0.0011 / search | | [Nimble](https://www.nimbleway.com "Open Nimble in a new tab") | POST /v2/search search\_depth=standard · full\_content=false · focus=general POST /v2/extract formats=\[markdown\] | 45.0 ± 1.0 | 44s | $0.078 $0.078 token cost | 905ms | 28,586 | $0.005 / search | | [Parallel advanced](https://parallel.ai "Open Parallel advanced in a new tab") | POST /v1/search mode=advanced POST /v1/extract | 77.0 ± 1.0 | 33s | $0.096 $0.019 search+fetch $0.077 token cost | 3.11s | 27,092 | $0.005 / search $0.001 / fetch | | [Parallel basic](https://parallel.ai "Open Parallel basic in a new tab") | POST /v1/search mode=basic POST /v1/extract | 76.0 ± 0.0 | 30s | $0.107 $0.023 search+fetch $0.084 token cost | 1.59s | 32,809 | $0.005 / search $0.001 / fetch | | [Perplexity](https://www.perplexity.ai "Open Perplexity in a new tab") | POST /search search\_context\_size=high POST /search search\_context\_size=high | 77.7 ± 1.5 | 22s | $0.083 $0.025 search+fetch $0.058 token cost | 991ms | 20,062 | $0.005 / search $0.005 / fetch | | [Tavily advanced](https://tavily.com "Open Tavily advanced in a new tab") | POST /search search\_depth=advanced POST /extract extract\_depth=advanced | 60.0 ± 2.0 | 46s | $0.153 $0.085 search+fetch $0.069 token cost | 3.41s | 26,269 | $0.016 / search $0.0032 / fetch | | [Tavily basic](https://tavily.com "Open Tavily basic in a new tab") | POST /search search\_depth=basic POST /extract extract\_depth=basic | 59.0 ± 1.7 | 32s | $0.113 $0.043 search+fetch $0.070 token cost | 1.50s | 27,405 | $0.008 / search $0.0016 / fetch | | [TinyFish](https://www.tinyfish.ai "Open TinyFish in a new tab") | GET api.search.tinyfish.ai POST api.fetch.tinyfish.ai format=markdown | 79.0 ± 2.0 | 24s | $0.050 $0.000 search+fetch $0.050 token cost | 1.32s | 12,844 | $0 / search $0 / fetch | | [You](https://you.com "Open You in a new tab") | POST /v1/search extraction\_mode=highlights POST /v1/contents | 55.0 ± 1.7 | 27s | $0.119 $0.027 search+fetch $0.092 token cost | 638ms | 42,806 | $0.005 / search $0.001 / fetch | | [You](https://you.com "Open You in a new tab") | POST /v1/search extraction\_mode=highlights · knowledge=core POST /v1/contents | 54.0 ± 2.0 | 28s | $0.117 $0.026 search+fetch $0.091 token cost | 678ms | 37,453 | $0.005 / search $0.001 / fetch | Task completion is mean ± SD of 3 runs; n = 100 tasks. Median task time, median task cost, avg search time, and median task tokens are pooled across those same 3 runs. \[01.b\] search only ### Perplexity completes most tasks at 77.3% Perplexity is fastest at 18.3 s TinyFish costs least at $0.035 Top 8 per metric __Search-only coding-agent board, listed alphabetically__ | Vendor | Endpoint & configuration | Task completion | Median task time | Median task cost | Avg search time | Median task tokens | API list price | | --- | --- | --- | --- | --- | --- | --- | --- | | [Brave](https://brave.com/search/api/ "Open Brave in a new tab") | POST /res/v1/llm/context | 38.0 ± 2.6 | 20s | $0.082 $0.025 search $0.057 token cost | 523ms | 14,103 | $0.005 / search | | [Exa fast](https://exa.ai "Open Exa fast in a new tab") | POST /search type=fast | 66.3 ± 1.5 | 20s | $0.103 $0.035 search $0.068 token cost | 626ms | 22,344 | $0.007 / search | | [Exa instant](https://exa.ai "Open Exa instant in a new tab") | POST /search type=instant | 61.3 ± 2.9 | 21s | $0.105 $0.035 search $0.070 token cost | 447ms | 22,423 | $0.007 / search | | [Firecrawl](https://firecrawl.dev "Open Firecrawl in a new tab") | POST /v2/search | 70.3 ± 1.5 | 28s | $0.059 $0.025 search $0.034 token cost | 2.87s | 7,456 | $0.005 / search | | [Linkup fast](https://www.linkup.so "Open Linkup fast in a new tab") | POST /v1/search depth=fast | 43.3 ± 1.1 | 27s | $0.100 $0.025 search $0.075 token cost | 1.39s | 24,057 | $0.005 / search | | [Nimble](https://www.nimbleway.com "Open Nimble in a new tab") | POST /v2/search search\_depth=lite · full\_content=false · focus=general | 46.7 ± 3.8 | 33s | $0.043 $0.005 search $0.037 token cost | 3.15s | 8,713 | $0.0011 / search | | [Nimble](https://www.nimbleway.com "Open Nimble in a new tab") | POST /v2/search search\_depth=standard · full\_content=false · focus=general | 42.3 ± 2.5 | 25s | $0.079 $0.025 search $0.054 token cost | 878ms | 14,859 | $0.005 / search | | [Parallel fast](https://parallel.ai "Open Parallel fast in a new tab") | POST /v1/search mode=fast | 66.7 ± 1.5 | 22s | $0.054 $0.005 search $0.049 token cost | 953ms | 12,460 | $0.001 / search | | [Parallel turbo](https://parallel.ai "Open Parallel turbo in a new tab") | POST /v1/search mode=turbo | 64.7 ± 2.1 | 19s | $0.059 $0.005 search $0.054 token cost | 333ms | 14,130 | $0.001 / search | | [Perplexity](https://www.perplexity.ai "Open Perplexity in a new tab") | POST /search search\_context\_size=low | 77.3 ± 2.1 | 18s | $0.059 $0.025 search $0.034 token cost | 957ms | 8,765 | $0.005 / search | | [Tavily basic](https://tavily.com "Open Tavily basic in a new tab") | POST /search search\_depth=basic | 51.0 ± 5.3 | 26s | $0.097 $0.040 search $0.057 token cost | 1.72s | 16,299 | $0.008 / search | | [TinyFish](https://www.tinyfish.ai "Open TinyFish in a new tab") | GET api.search.tinyfish.ai | 59.3 ± 1.5 | 28s | $0.035 $0.000 search $0.035 token cost | 2.15s | 7,469 | $0 / search | | [You](https://you.com "Open You in a new tab") | POST /v1/search extraction\_mode=highlights | 41.3 ± 3.1 | 24s | $0.097 $0.025 search $0.072 token cost | 596ms | 20,333 | $0.005 / search | | [You](https://you.com "Open You in a new tab") | POST /v1/search extraction\_mode=highlights · knowledge=core | 38.3 ± 3.2 | 21s | $0.096 $0.025 search $0.071 token cost | 677ms | 20,786 | $0.005 / search | Task completion is mean ± SD of 3 runs; n = 100 tasks. Median task time, median task cost, avg search time, and median task tokens are pooled across those same 3 runs. \[02\] methodology and metric definitions+ \[02.a\] why we built this ### Real User Workflow in this Benchmark We evaluate web search in a coding-agent workflow using realistic product tickets. The agent receives a small existing `httpx` client and must patch it against the vendor's **current official API documentation**. The ticket describes the desired capability in natural language, but deliberately does **not** reveal the exact implementation details. The ground-truth answer requires discovering an opaque detail from official documentation, such as: * an API path * a required header * a function or import name * another vendor-specific implementation identifier Target documentation is enterprise SaaS documentation like Workday and SAP. The benchmark is intentionally designed so that transforming ticket language into an API-looking string should not be enough. The agent should have to **search**. The no-search condition is a necessity. If the model can solve a task by guessing the API or documentation structure, that task may still measure coding ability, but it no longer provides a clean measurement of web-search quality. \[02.b\] methodology ### How the benchmark is built Tasks are built from currently live official vendor documentation. Finding those pages is done by a script, not by the coding agent. The search path used here is a **neutral third party** that is not being evaluated in this benchmark. 1. **Discover.** We start from a known list of official docs pages. A script then runs ordinary web searches for more pages on the same enterprise SaaS documentation sites. Anything not on those official sites is dropped. No LLM is in this step. 2. **Fetch.** A second script downloads the full text of those pages into a local folder. Still no LLM. The Authoring Agent only sees these saved files later. 3. **Generate.** An Authoring Agent reads only those stored pages and proposes product tickets: opaque gold token from the page, capability English that does not name it, and a starter that calls a real but wrong API. Each ticket has a minimum of three very similar configurations; disambiguation is necessary to find the right one. 4. **Verify.** Automatic keep-gate rejects a ticket if it does not follow dataset guidelines such as naming the ground truth keywords in the ticket. 5. **Manual review.** A human reads each accepted draft before it is promoted. Before a task enters the dataset, it must pass an explicit **no-search baseline**. If the model can recover the ground-truth implementation from the ticket and starter alone, the task is excluded or rewritten. The public 30-ticket set is on Hugging Face as [openbenchmarks/OB-Code-Websearch ](https://huggingface.co/datasets/openbenchmarks/OB-Code-Websearch). The boards on this page are scored on a held-out private set that is not distributed so that vendors and models cannot train and fit to the Benchmark. Use the public rows to inspect the format; scores on those rows are not comparable to the boards. Harness, scoring and vendor runners are in [openbenchmarks-labs/web-search-for-coding-agents ](https://github.com/openbenchmarks-labs/web-search-for-coding-agents). The model receives only the ticket and the starter code. It is run under one of two tool configurations: `[web_search] [web_search, web_fetch]` Current limits: `Model: gpt-5.6-sol Max turns: 32 Search budget: 5 Fetch budget: 5 Repeats: 3` Searches that return no results or fail still use a turn, but do not count toward the search budget. Each vendor is run three times on the same locked ticket set, on both the search-only board and the search & fetch board, to account for model variability. The published task-completion score is the mean of those three runs, shown with the sample standard deviation. Median task time, median task cost, avg search time, and median task tokens are a single pooled number from the same three runs, not ± SD. \[02.c\] metric definitions ### What each metric means * `Task completion` · mean share of tickets that pass, across three independent runs of the same 100-task set. A submission passes only if the submitted file compiles, all ground-truth tokens are present, each has an associated `# source:` URL, and that URL actually appeared in the search or fetch results from that run. A sufficiently informative search snippet is enough. The table reports mean ± SD of the three board pass rates. * `Median task time` · median end-to-end wall-clock time for the agent to finish the task, from the first turn until submission, pooled across all three repeats. * `Avg search time` · mean latency of each `web_search` tool call, including calls that return no results or fail, pooled across all three repeats. * `Median task tokens` · median LLM prompt plus completion tokens per ticket, pooled across all three repeats. * `API list price`· PAYG dollar rate of one search call, and of one fetch call on the search & fetch board. * `Median task cost` · median LLM dollar cost per ticket (Braintrust `estimated_cost()` on the eval trace) plus median search/fetch API spend (call counts × list price), pooled across all three repeats. \[03\] faq ## Web search APIs for coding agents and developers ### Can the model pass these coding tasks without web search? No. The model-only baseline is 0\. A pass requires a # source: URL that actually appeared in that run's search or fetch results. Without a search tool the model cannot provide that URL, and the ticket does not name the implementation detail. ### What is the best web search API for AI coding agents in 2026? Exa deep leads with fetch at 83.0% task completion on 100 tickets. Perplexity leads search-only at 77.3%. Compare task completion, latency, token usage, and cost for your workflow. Providers are listed alphabetically, with search-only and search-plus-fetch reported separately and no overall winner. ### What is the best web search API for developers? Exa deep leads with fetch at 83.0% task completion on 100 tickets. Perplexity leads search-only at 77.3%. Same board. Developers and coding agents use web search APIs to find implementation details in live official docs. This page compares quality, latency, token usage, and cost for that hard-retrieval job, with search+fetch and search-only reported separately. ### Does this benchmark measure Context7, Ref, or MCP code search? No. Those are documentation MCP / code-search tools. This board measures general web search APIs (Exa, Tavily, Brave, Parallel, Linkup, Firecrawl, TinyFish) used as search in a coding agent. They are a different product. ### Why don't we use popular docs like Stripe? The benchmark is designed against the model's reward hacking in the user workflow. Vendors with highly regular, developer-friendly documentation can be poor fits: in earlier Stripe tasks, the model often inferred the documentation slug or URL structure directly from the ticket and reached the correct implementation without performing meaningful retrieval. For example, when documentation followed a predictable pattern such as /changelog/dahlia/{kebab-case-ticket-title}, the model could construct the likely documentation location from the ticket itself. That tests the model's ability to exploit documentation naming conventions, not the quality of the search provider. Those tasks were removed from the active benchmark. Keep-gate: if a capable model can reliably solve the task without searching, the task does not belong in the primary search benchmark. \[04\] go deeper ## Web search tasks This is the hard retrieval task on the [web search benchmark](https://openbenchmarks.com/web-search). [Factual lookup: company news →](https://openbenchmarks.com/company-news) [Multi-hop: multi-turn company search →](https://openbenchmarks.com/multi-turn-company-search) ### Best web search API comparisons [Best web search APIs for AI agents](https://openbenchmarks.com/web-search/best-web-search-api-for-ai) · [Best web search APIs for developers](https://openbenchmarks.com/web-search/best-web-search-api-for-developers) · [Best web search APIs for LLM apps & agents](https://openbenchmarks.com/web-search/best-web-search-api-for-llm-apps) · [Best web search APIs for LLM agents](https://openbenchmarks.com/web-search/best-web-search-api-for-llm-agents) · [Agentic search comparison & benchmark](https://openbenchmarks.com/web-search/best-web-search-api-for-ai-agents-for-fact-finding) · [Deep search comparison & benchmark](https://openbenchmarks.com/web-search/best-web-search-api-for-ai-agents-for-deep-research) · [Web search APIs for AI & LLM developers](https://openbenchmarks.com/web-search/best-web-search-api-for-ai-llm-developers) · [Web search APIs & MCPs for AI agents and developers](https://openbenchmarks.com/web-search/best-web-search-apis-and-mcps-for-ai-agents) · [Search tools for AI agents](https://openbenchmarks.com/web-search/best-search-tools-for-ai-agents) · [Search providers for LLM applications](https://openbenchmarks.com/web-search/best-search-providers-for-llm-applications) · [AI search engines for agents](https://openbenchmarks.com/web-search/best-ai-search-engines-for-agents) · [Free web search APIs for AI agents](https://openbenchmarks.com/web-search/best-free-web-search-api-for-ai-agents) · [Web search APIs for RAG](https://openbenchmarks.com/web-search/best-web-search-api-for-rag) · [Independent web search API comparison](https://openbenchmarks.com/web-search/best-web-search-api-comparison) · [Best fast web search API](https://openbenchmarks.com/web-search/best-fast-web-search-api) · [Best search and scrape API](https://openbenchmarks.com/web-search/best-search-and-scrape-api) · [Best scrape API for AI agents](https://openbenchmarks.com/web-search/best-scrape-api-for-ai-agents) · [Best search API for deep research agents](https://openbenchmarks.com/web-search/best-search-api-for-deep-research-agents) · [Best search API for company research](https://openbenchmarks.com/web-search/best-search-api-for-company-research) · [Best search API for sales agents](https://openbenchmarks.com/web-search/best-search-api-for-sales-agents) · [Best search API for coding documentation](https://openbenchmarks.com/web-search/best-search-api-for-coding-documentation) · [Best web search API for grounding](https://openbenchmarks.com/web-search/best-web-search-api-for-grounding) · [Best search API for news](https://openbenchmarks.com/web-search/best-search-api-for-news) Best search APIs for coding agents: [Best Web Search API for Coding Agents](https://openbenchmarks.com/web-search-for-coding-agents/best-web-search-api-for-coding-agents) · [Best Search Tool for Coding Agents](https://openbenchmarks.com/web-search-for-coding-agents/best-search-tool-for-coding-agents) · [Best Search API for AI Coding Agents](https://openbenchmarks.com/web-search-for-coding-agents/best-search-api-for-ai-coding-agents) Documentation lookup: [Best Search API for Up-to-Date Docs](https://openbenchmarks.com/web-search-for-coding-agents/best-search-api-for-up-to-date-docs) · [Best Technical Search API for AI Agents](https://openbenchmarks.com/web-search-for-coding-agents/best-technical-search-api-for-ai-agents) · [Best Search and Fetch API for Coding Agents](https://openbenchmarks.com/web-search-for-coding-agents/best-search-and-fetch-api-for-coding-agents) By accuracy, speed, cost and tokens: [Cheapest Search API for Coding Agents](https://openbenchmarks.com/web-search-for-coding-agents/cheapest-search-api) · [Fastest Web Search API for Coding Agents](https://openbenchmarks.com/web-search-for-coding-agents/fastest-web-search-api-for-coding-agents) · [Most Accurate Web Search API for Coding Agents](https://openbenchmarks.com/web-search-for-coding-agents/most-accurate-web-search-api-for-coding-agents) · [Most Token-Efficient Web Search API for Coding Agents](https://openbenchmarks.com/web-search-for-coding-agents/most-token-efficient-web-search-api-for-coding-agents) Web search MCP: [Best Web Search MCP for Claude Code](https://openbenchmarks.com/web-search-for-coding-agents/best-web-search-mcp-for-claude-code) · [Best Web Search MCP for Cursor](https://openbenchmarks.com/web-search-for-coding-agents/best-web-search-mcp-for-cursor) · [Best Web Search MCP for Codex](https://openbenchmarks.com/web-search-for-coding-agents/best-web-search-mcp-for-codex) [All web search for coding agents comparisons →](https://openbenchmarks.com/web-search-for-coding-agents/comparisons) \[05\] changelog+ 2026-09-15 Nimble Search and Extract added Benchmarked Nimble lite and standard on 100 tasks across three repeats in both modes. Search uses `full_content=false` and `focus=general`; search + fetch calls `POST /v2/extract` separately with `formats=[markdown]`. 2026-09-15 Benchmarked Nimble lite and standard plus You highlights with and without knowledge=core on search-only and search + fetch; 100 tasks across three repeats per configuration and mode 2026-09-11 Tavily Basic added Replaced Tavily Fast with Tavily Basic on the search-only board; evaluated 100 tasks across three repeats. 2026-09-09 TinyFish Fetch refreshed Re-evaluated TinyFish after updates were rolled out to their GA Fetch endpoint. Openbenchmarks open benchmarks [← web search benchmark](https://openbenchmarks.com/web-search) [for developers](https://openbenchmarks.com/web-search/best-web-search-api-for-developers) [source ↗](https://github.com/openbenchmarks-labs/web-search-for-coding-agents "Open the benchmark code on GitHub") [dataset ↗](https://huggingface.co/datasets/openbenchmarks/OB-Code-Websearch "Public dataset on Hugging Face") © 2026 Openbenchmarks