- [OpenClaw vs Claude Code: which AI agent should you actually use?](https://parallel.ai/articles/openclaw-vs-claude-code-which-ai-agent-should-you-actually-use)
Reading time: 12 min
July 27, 2026
# Gemini's Google Search grounding vs. Parallel: the best index, with strings attached
Grounding with Google Search gives a Gemini model access to Google's index. The best web index there is, by most measures anyone would use. If retrieval quality were the only variable, this comparison would be over before it started. It is not the only variable. The grounding tool comes with pricing, packaging, and integration constraints that decide whether it fits your architecture, and those are worth reading before you build around it.
Tags:Comparison
Reading time: 6 min## **How grounding works**
You enable the googleSearch tool on a Gemini request and the model decides when to search, issues its own queries, and answers from what it finds with grounding metadata attached. You never see a result list as data. The retrieval happens inside the model turn, and what you get back is the model's answer plus the sources it leaned on.
Parallel's Search API is a standalone endpoint. Send a natural-language objective, optionally with explicit search queries, and get ranked URLs with excerpts pulled from the page bodies. Three modes set the trade: Turbo at ~200ms and $1 per 1,000 requests, Basic at ~1s and $5 per 1,000, and Advanced at ~3s and $5 per 1,000, which is the default.
## **Pricing, and the billing change that matters**
Grounding prices differently depending on which model generation you are on, and the two schemes are not the same shape:
| Model | Free allowance | Paid rate |
|---|---|---|
| Gemini 3 models | 5,000 prompts a month | $14 per 1,000 search queries |
| Gemini 2.5 models | 1,500 requests a day, shared across Flash and Flash-Lite | $35 per 1,000 grounded prompts |
| Google Maps grounding | Not stated | $25 per 1,000 grounded prompts |
Read the unit carefully, because it changed. On Gemini 2.5 you were billed per prompt: one API call, one charge, however many searches the model ran. On Gemini 3 you are billed per search query the model executes, so a single prompt that searches three different things is three billable uses. The headline rate dropped from $35 to $14, but the multiplier is now unbounded and controlled by the model rather than by you.
Parallel charges $1 per 1,000 requests on Turbo and $5 per 1,000 on Basic and Advanced, with 10 results and excerpts included and additional results at $1 per 1,000. The count is whatever your code decided to call, so the bill is arithmetic rather than a forecast. Extract is $1 per 1,000 URLs; above that sit the Task API at $5 to $2,400 per 1,000 runs, Responses at $10 to $250 per 1,000, Monitor at $3 per 1,000 executions, Entity Search at $5 per 1,000, and FindAll at a fixed cost plus per match. There is $5 in free credits every month, applied automatically.
Google's free allowance is the more generous one for getting started: 5,000 grounded prompts a month against $5 of credit that buys 5,000 Turbo searches is roughly a tie on volume, but the Gemini 2.5 daily allowance of 1,500 requests is considerably more.
_Note: For the latest pricing, always check official documentation._
## **Two constraints to check before you design around it**
The first is a real architectural limit: the Gemini API does not support combining search tools such as googleSearch with non-search tools such as function calling in the same request. If your agent needs to search the web and call your own functions in one turn, grounding will not do it, and you are back to a separate retrieval call. For anything that looks like a tool-using agent rather than a question-answering endpoint, this is usually the deciding constraint.
The second is a product requirement rather than a technical one: using grounding obliges you to display Google Search Suggestions in your interface. That is fine for a consumer chat product and awkward for a backend pipeline, an internal tool, or any surface where you did not plan to render Google's UI.
Grounding is also only available on certain models: Gemini 3 Flash and Flash-Lite variants and the 2.5 family, with restrictions on Pro in the free tier. Check the current model support table rather than assuming.
## **Results in a model turn versus results as data**
As with any provider-side grounding tool, the retrieval is bound to the inference. You cannot take the results and hand them to a different model, cache them in your own store, or run a retrieval-only job without paying for a model turn. Changing model provider means rebuilding the retrieval layer, because the tool belongs to Google rather than to you.
A standalone API is model-agnostic by construction: the same Parallel call feeds Gemini today and something else next quarter, results are yours to store or route, and retrieval-only workloads cost only retrieval.
## **On index quality**
It would be silly to pretend otherwise: Google's index is the most comprehensive and freshest in the world, and for long-tail queries, obscure sources, and very recent events that advantage is real. No independent index matches its coverage.
What that advantage does not settle is how much of it survives the trip into a model's context window. Coverage is not the same as the right passage arriving in the prompt, which is the problem excerpt-based retrieval is built around. Parallel publishes benchmarks on SimpleQA, BrowseComp, and SealQA; Google publishes none for grounding as a retrieval component, so there is no head-to-head to point at. If this decision matters to you, measure both on your own queries rather than reasoning from index size.
## **Developer experience**
Grounding is a tool declaration on a request you were already making, which is about as low-friction as integration gets if you are on Gemini. Vertex AI adds enterprise controls and a limit of one million queries a day.
Parallel ships Python and TypeScript SDKs, an MCP server, and a playground, with default rate limits of 600 requests per minute on Search and Extract, and the Responses API OpenAI SDK-compatible:
12345678910111213from parallel import Parallel
client = Parallel(api_key=os.environ["PARALLEL_API_KEY"])
search = client.beta.search(
objective="your goal",
mode="turbo",
excerpts={"max_chars_per_result": 10000},
)``` from parallel import Parallel client = Parallel(api_key=os.environ["PARALLEL_API_KEY"]) search = client.beta.search( objective="your goal", mode="turbo", excerpts={"max_chars_per_result": 10000}, )``` Parallel is SOC 2 Type 2 certified, offers a Data Processing Addendum and zero data retention, and commits contractually to not training on customer data, with a public status page and trust center.
## **When to use each**
Use Google Search grounding when you are building a Gemini-based answering product with a user-facing interface. You get the world's best index, a free allowance that covers a real prototype, and an integration that is one tool declaration long. If your surface can display Search Suggestions and your requests do not need function calling alongside search, the constraints cost you nothing.
Use Parallel when you need search and tools in the same turn, results as data rather than as context, or a cost you can predict. Turbo at $1 per 1,000 requests is a fourteenth of the Gemini 3 rate per query and does not multiply with the model's search appetite. Extract, Task, Responses, FindAll, Entity Search, and Monitor cover extraction, deep research, list building, and change tracking, all on the same key and all callable from any model.
The function-calling restriction is the thing to check first. It rules grounding out of most genuine agent architectures regardless of what you think of the index, and no amount of coverage compensates for a tool your agent cannot combine with its own.
**Related reading: **Claude's web search tool vs. Parallel[Claude's web search tool vs. Parallel] · OpenAI web search vs. Parallel vs. Exa vs. Tavily[OpenAI web search vs. Parallel vs. Exa vs. Tavily] · Perplexity Sonar vs. Parallel[Perplexity Sonar vs. Parallel].
By Parallel
July 27, 2026