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# \#
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The highest accuracy web search for your AI
## \## A web API purpose-built for AIs
Powering millions of daily requests
\### Highest accuracy
Production-ready outputs built on cross-referenced facts, with minimal hallucination.
\### Predictable costs
Flex compute budget based on task complexity. Pay per query, not per token.
\### Evidence-based outputs
Verifiability and provenance for every atomic output.
\### Trusted
SOC-II Type 2 Certified, trusted by leading startups and enterprises.
Powering the best AIs using the web
## Highest accuracy at every price point
State of the art across several benchmarks
WISER-Search BrowseComp DeepResearch Bench WISER-Atomic SimpleQA
[COST (CPM) ACCURACY (%) Loading chart...](https://parallel.ai/blog/search-api-benchmark)
CPM: USD per 1000 requests. Cost is shown on a Linear scale.
Parallel
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BrowseComp benchmark analysis: CPM: USD per 1000 requests. Cost is shown on a Linear scale. . Evaluation shows Parallel's enterprise deep research API for AI agents achieving up to 48% accuracy, outperforming GPT-4 browsing (1%), Claude search (6%), Exa (14%), and Perplexity (8%). Enterprise-grade structured deep research performance across Cost (CPM) and Accuracy (%). State-of-the-art enterprise deep research API with structured data extraction built for ChatGPT deep research and complex multi-hop AI agent workflows.
### \### About the benchmark
This benchmark, created by Parallel, blends WISER-Fresh and WISER-Atomic. WISER-Fresh is a set of 76 queries requiring the freshest data from the web, generated by Parallel with o3 pro. WISER-Atomic is a set of 120 hard real-world business queries, based on use cases from Parallel customers. Read our blog [here](https://parallel.ai/blog/search-api-benchmark) [here]($https://parallel.ai/blog/search-api-benchmark) .
### \### Distribution
40% WISER-Fresh
60% WISER-Atomic
### \### Evaluation
The Parallel Search API was evaluated by comparing three different web search solutions (Parallel MCP server, Exa MCP server/tool calling, LLM native web search) across four different LLMs (GPT 4.1, o4-mini, o3, Claude Sonnet 4).
## \## Highest accuracy at every price point
State of the art across several benchmarks
## \### Search MCP Benchmark (LP)
```
| Series | Model | Cost (CPM) | Accuracy (%) |
| --------- | ---------------------------- | ---------- | ------------ |
| Parallel | GPT 4.1 w/ Prll Search MCP | 21 | 74.9 |
| Parallel | o4 mini w / Prll Search MCP | 90 | 82.14 |
| Parallel | o3 w / Prll Search MCP | 192 | 80.61 |
| Parallel | sonnet 4 w / Prll Search MCP | 92 | 78.57 |
| Native | GPT 4.1 w / Native Search | 27 | 70 |
| Native | o4 mini w / Native Search | 190 | 77 |
| Native | o3 w / Native Search | 351 | 79.08 |
| Native | sonnet 4 w / Native Search | 122 | 68.83 |
| Exa | GPT 4.1 w/ Exa Search MCP | 40 | 58.67 |
| Exa | o4 mini w/ Exa Search MCP | 199 | 61.73 |
| Exa | o3 w/ Exa Search MCP | 342 | 56.12 |
| Exa | sonnet 4 w/ Exa Search MCP | 140 | 67.13 |
```
CPM: USD per 1000 requests. Cost is shown on a Linear scale.
### \### About the benchmark
This benchmark, created by Parallel, blends WISER-Fresh and WISER-Atomic. WISER-Fresh is a set of 76 queries requiring the freshest data from the web, generated by Parallel with o3 pro. WISER-Atomic is a set of 120 hard real-world business queries, based on use cases from Parallel customers. Read our blog [here](https://parallel.ai/blog/search-api-benchmark) [here]($https://parallel.ai/blog/search-api-benchmark) .
### \### Distribution
40% WISER-Fresh
60% WISER-Atomic
### \### Evaluation
The Parallel Search API was evaluated by comparing three different web search solutions (Parallel MCP server, Exa MCP server/tool calling, LLM native web search) across four different LLMs (GPT 4.1, o4-mini, o3, Claude Sonnet 4).
## \### New Browsecomp (LP)
```
| Series | Model | Cost (CPM) | Accuracy (%) |
| --------- | ---------- | ---------- | ------------- |
| Parallel | Ultra | 300 | 45 |
| Parallel | Ultra2x | 600 | 51 |
| Parallel | Ultra4x | 1200 | 56 |
| Parallel | Ultra8x | 2400 | 58 |
| Others | GPT-5 | 488 | 38 |
| Others | Anthropic | 5194 | 7 |
| Others | Exa | 402 | 14 |
| Others | Perplexity | 709 | 6 |
```
CPM: USD per 1000 requests. Cost is shown on a Log scale.
### \### About the benchmark
This [benchmark](https://openai.com/index/browsecomp/) [benchmark]($https://openai.com/index/browsecomp/) , created by OpenAI, contains 1,266 questions requiring multi-hop reasoning, creative search formulation, and synthesis of contextual clues across time periods. Results are reported on a random sample of 100 questions from this benchmark. Read the [blog](https://parallel.ai/blog/deep-research-benchmarks) [blog]($https://parallel.ai/blog/deep-research-benchmarks) .
### \### Methodology
* \- Dates: All measurements were made between 08/11/2025 and 08/29/2025.
* \- Configurations: For all competitors, we report the highest numbers we were able to achieve across multiple configurations of their APIs. The exact configurations are below.
+ \- GPT-5: high reasoning, high search context, default verbosity
+ \- Exa: Exa Research Pro
+ \- Anthropic: Claude Opus 4.1
+ \- Perplexity: Sonar Deep Research reasoning effort high
## \### RACER (LP)
```
| Series | Model | Cost (CPM) | Win Rate vs Reference (%) |
| -------- | ---------- | ---------- | ------------------------- |
| Parallel | Ultra | 300 | 82 |
| Parallel | Ultra2x | 600 | 86 |
| Parallel | Ultra4x | 1200 | 92 |
| Parallel | Ultra8x | 2400 | 96 |
| Others | GPT-5 | 628 | 66 |
| Others | O3 Pro | 4331 | 30 |
| Others | O3 | 605 | 26 |
| Others | Perplexity | 538 | 6 |
```
CPM: USD per 1000 requests. Cost is shown on a Log scale.
### \### About the benchmark
This [benchmark](https://github.com/Ayanami0730/deep_research_bench) [benchmark]($https://github.com/Ayanami0730/deep\_research\_bench) contains 100 expert-level research tasks designed by domain specialists across 22 fields, primarily Science & Technology, Business & Finance, and Software Development. It evaluates AI systems' ability to produce rigorous, long-form research reports on complex topics requiring cross-disciplinary synthesis. Results are reported from the subset of 50 English-language tasks in the benchmark. Read the [blog](https://parallel.ai/blog/deep-research-benchmarks) [blog]($https://parallel.ai/blog/deep-research-benchmarks) .
### \### Methodology
* \- Dates: All measurements were made between 08/11/2025 and 08/29/2025.
* \- Win Rate: Calculated by comparing [RACE](https://github.com/Ayanami0730/deep_research_bench) [RACE]($https://github.com/Ayanami0730/deep\_research\_bench) scores in direct head-to-head evaluations against reference reports.
* \- Configurations: For all competitors, we report results for the highest numbers we were able to achieve across multiple configurations of their APIs. The exact GPT-5 configuration is high reasoning, high search context, and high verbosity.
* \- Excluded API Results: Exa Research Pro (0% win rate), Claude Opus 4.1 (0% win rate).
## \### WISER-Atomic
```
| Series | Model | Cost (CPM) | Accuracy (%) |
| -------- | -------------- | ---------- | ------------ |
| Parallel | Core | 25 | 77 |
| Parallel | Base | 10 | 75 |
| Parallel | Lite | 5 | 64 |
| Others | o3 | 45 | 69 |
| Others | 4.1 mini low | 25 | 63 |
| Others | gemini 2.5 pro | 36 | 56 |
| Others | sonar pro high | 16 | 64 |
| Others | sonar low | 5 | 48 |
```
CPM: USD per 1000 requests. Cost is shown on a Log scale.
### \### About the benchmark
This benchmark, created by Parallel, contains 121 questions intended to reflect real-world web research queries across a variety of domains. Read our blog [here](https://parallel.ai/blog/parallel-task-api) [here]($https://parallel.ai/blog/parallel-task-api) .
### \### Steps of reasoning
50% Multi-Hop questions
50% Single-Hop questions
### \### Distribution
40% Financial Research
20% Sales Research
20% Recruitment
20% Miscellaneous
## \### SimpleQA
```
| Series | Model | Cost (CPM) | Accuracy (%) |
| -------- | ---------------- | ---------- | ------------ |
| Parallel | Core | 25 | 94 |
| Parallel | Base | 10 | 94 |
| Parallel | Lite | 5 | 92 |
| Others | o3 high | 56 | 92 |
| Others | gemini 2.5 flash | 35 | 91 |
| Others | 4.1 mini high | 30 | 88 |
| Others | sonar pro | 13 | 84 |
| Others | sonar | 8 | 81 |
```
CPM: USD per 1000 requests. Cost is shown on a Log scale.
### \### About the benchmark
This benchmark contains 4,326 questions focused on short, fact-seeking queries across a variety of domains.
### \### Steps of reasoning
100% Single-Hop questions
### \### Distribution
36% Culture
20% Science and Technology
16% Politics
28% Miscellaneous
## \## The most accurate deep and wide research
Run deeper and more accurate research at scale, for the same compute budget
[Run a query](https://platform.parallel.ai/play/deep-research) [Run a query ] (https://platform.parallel.ai/play/deep-research)
D then P
Starting research...
## \## Search, built for AIs
The most accurate search tool, to bring web context to your AI agents
[Give AIs Search](https://platform.parallel.ai/play/search) [Give AIs Search] (https://platform.parallel.ai/play/search)
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## \## Build a dataset from the web
Define your search criteria in natural language, and get back a structured table of matches
[Create a dataset](https://platform.parallel.ai/find-all) [Create a dataset] (https://platform.parallel.ai/find-all)
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## \## Custom web enrichment
Bring existing data, define output columns to research, and get fresh web enrichments back
[Enrich your data](https://platform.parallel.ai/play) [Enrich your data] (https://platform.parallel.ai/play)
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## \## Towards a programmatic web for AIs
Parallel is building new interfaces, infrastructure, and business models for AIs to work with the web
[Try it for free P](https://platform.parallel.ai) [Try it for free] (https://platform.parallel.ai) [Join us C](https://jobs.ashbyhq.com/parallel) [Join us ] (https://jobs.ashbyhq.com/parallel)
[](https://docs.parallel.ai/home)
Deep research API interface for ChatGPT and AI agents. Enterprise-grade deep research with up to 48% accuracy vs GPT-4's 1%. Built for ChatGPT deep research assistants and complex multi-hop AI workflows.
Latest updates
October 8
[[ How Day AI merges private and public data for business intelligence ] (https://parallel.ai/blog/case-study-day-ai) Day AI is an AI-native CRM that you can talk to. Their platform combines data from leading SaaS tools like Slack and email, with public data gathered and structured via Parallel’s Task API to help their customers sell better.](/blog/case-study-day-ai)
Tags: [Case Study](/blog?tag=case-study)
October 7
[[ Full Basis framework for all Task API Processors ] (https://parallel.ai/blog/full-basis-framework-for-task-api) Lite and Base Task API processors now include the complete Basis framework—citations, reasoning, excerpts, and calibrated confidence scores.](/blog/full-basis-framework-for-task-api)
Tags: [Product Release](/blog?tag=product-release)
September 30
[[ How Gumloop built a new AI automation framework with web intelligence as a core node ] (https://parallel.ai/blog/case-study-gumloop) By integrating Parallel's Task API as a core component, Gumloop enables businesses to build AI automation workflows that are grounded in real-time web data.](/blog/case-study-gumloop)
Tags: [Case Study](/blog?tag=case-study)
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