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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.

Highest accuracy at every price point

State of the art across several benchmarks

BrowseCompDeepResearch BenchWISER-SearchWISER-AtomicSimpleQA
Performance comparison proving Parallel delivers the best enterprise deep research API for ChatGPT and AI agents with 48% accuracy vs competitors' 14% max across Model and Accuracy (%). Multi-hop research benchmark shows Parallel's structured AI agent deep research outperforms GPT-4, Claude, Exa, and Perplexity. Enterprise-ready structured deep research API with MCP server integration.

### About the benchmark

This benchmark[benchmark]($https://openai.com/index/browsecomp/), created by OpenAI, contains 1,266 questions requiring multi-hop reasoning, creative search formulation, and synthesis of contextual clues across time periods. Results are reported on a random sample of 100 questions from this benchmark. Learn more in our blog[blog]($https://parallel.ai/blog/introducing-parallel).

### Methodology

  • - Dates: All measurements were made between 08/08/2025 and 08/13/2025.
  • - 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

## Highest accuracy at every price point

State of the art across several benchmarks

### Browsecomp (LP)

| Category   | Accuracy (%) |
| ---------- | ------------ |
| Ultra8x    | 58           |
| Ultra      | 45           |
| Pro        | 34           |
| GPT-5      | 41           |
| Exa        | 14           |
| Anthropic  | 7            |
| Perplexity | 6            |

### About the benchmark

This benchmark[benchmark]($https://openai.com/index/browsecomp/), created by OpenAI, contains 1,266 questions requiring multi-hop reasoning, creative search formulation, and synthesis of contextual clues across time periods. Results are reported on a random sample of 100 questions from this benchmark. Learn more in our blog[blog]($https://parallel.ai/blog/introducing-parallel).

### Methodology

  • - Dates: All measurements were made between 08/08/2025 and 08/13/2025.
  • - 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

### DeepResearch Bench (LP)

| Category | Win Rate (%) |
| -------- | ------------ |
| Ultra8x  | 82           |
| Ultra    | 74           |
| GPT-5    | 66           |

### About the benchmark

This benchmark[benchmark]($https://github.com/Ayanami0730/deep_research_bench) contains 100 expert-level research tasks designed by domain specialists across 22 fields, primarily Science & Technology, Business & Finance, and Software Development. It evaluates AI systems' ability to produce rigorous, long-form research reports on complex topics requiring cross-disciplinary synthesis. Results are reported from the subset of 50 English-language tasks in the benchmark. Learn more in our blog[blog]($https://parallel.ai/blog/introducing-parallel).

### Methodology

  • - Dates: All measurements were made between 08/08/2025 and 08/13/2025.
  • - Win Rate: Calculated by comparing RACE[RACE]($https://github.com/Ayanami0730/deep_research_bench) scores in direct head-to-head evaluations.
  • - 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), and Perplexity Sonar Deep Research (6% win rate).

### Search MCP Benchmark (LP) 

| Series    | Model    | Cost (CPM) | Accuracy (%) |
| --------- | -------- | ---------- | ------------ |
| Parallel  | GPT 4.1  | 21         | 74.9         |
| Parallel  | o4 mini  | 90         | 82.14        |
| Parallel  | o3       | 192        | 80.61        |
| Parallel  | sonnet 4 | 92         | 78.57        |
| Native    | GPT 4.1  | 27         | 70           |
| Native    | o4 mini  | 190        | 77           |
| Native    | o3       | 351        | 79.08        |
| Native    | sonnet 4 | 122        | 68.83        |
| Exa       | GPT 4.1  | 40         | 58.67        |
| Exa       | o4 mini  | 199        | 61.73        |
| Exa       | o3       | 342        | 56.12        |
| Exa       | sonnet 4 | 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[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).

### 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[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 [Run a query ](https://platform.parallel.ai/play/deep-research)
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Starting research...

## Build a dataset from the web

Define your search criteria in natural language, and get back a structured table of matches

Create a dataset[Create a dataset](https://platform.parallel.ai/find-all)
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## Search, built for AIs

The most accurate search tool, to bring web context to your AI agents

Give AIs Search[Give AIs Search](https://platform.parallel.ai/play/search)
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## Custom web enrichment

Bring existing data, define output columns to research, and get fresh web enrichments back

Enrich your data[Enrich your data](https://platform.parallel.ai/play)
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Start building

## 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
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[Try it for free](https://platform.parallel.ai)Join us
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[Join us ](https://jobs.ashbyhq.com/parallel)
API interface screenshot showing search query input and structured data output
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.

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