#
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 best-in-class AI businesses
Highest accuracy at every price point
State of the art across the most challenging benchmarks
### About this benchmark
This benchmark[benchmark](https://lastexam.ai/) consists of 2,500 questions developed by subject-matter experts across dozens of subjects (e.g. math, humanities, natural sciences). Each question has a known solution that is unambiguous and easily verifiable, but requires sophisticated web retrieval and reasoning. Results are reported on a sample of 100 questions from this benchmark. Learn more in our latest blog[latest blog](/blog/introducing-parallel-search).
### Methodology
- - **Evaluation**: Results are based on tests run using official Search MCP servers provided as an MCP tool to OpenAI's GPT-5 model using the Responses API. In all cases, the MCP tools were limited to only the appropriate web search tool. Answers were evaluated using an LLM as a judge (GPT 4.1).
- - **Cost Calculation**: Cost reflects the average cost per query across all questions run. This cost includes both the search API call and LLM token cost.
- - **Testing Dates**: Testing was conducted from November 3rd to November 5th.
## Highest accuracy at every price point
State of the art across the most challenging benchmarks
### HLE Search LP
| Series | Model | Cost (CPM) | Accuracy (%) | | --------- | ------------ | ----------- | ------------ | | Parallel | parallel | 82 | 47 | | Others | exa | 138 | 24 | | Others | tavily | 190 | 21 | | Others | perplexity | 126 | 30 | | Others | openai gpt-5 | 143 | 45 |
### About this benchmark
This benchmark[benchmark](https://lastexam.ai/) consists of 2,500 questions developed by subject-matter experts across dozens of subjects (e.g. math, humanities, natural sciences). Each question has a known solution that is unambiguous and easily verifiable, but requires sophisticated web retrieval and reasoning. Results are reported on a sample of 100 questions from this benchmark. Learn more in our latest blog[latest blog](/blog/introducing-parallel-search).
### Methodology
- - **Evaluation**: Results are based on tests run using official Search MCP servers provided as an MCP tool to OpenAI's GPT-5 model using the Responses API. In all cases, the MCP tools were limited to only the appropriate web search tool. Answers were evaluated using an LLM as a judge (GPT 4.1).
- - **Cost Calculation**: Cost reflects the average cost per query across all questions run. This cost includes both the search API call and LLM token cost.
- - **Testing Dates**: Testing was conducted from November 3rd to November 5th.
### BrowseComp Search LP
| Series | Model | Cost (CPM) | Accuracy (%) | | --------- | ------------ | ----------- | ------------ | | Parallel | parallel | 156 | 58 | | Others | exa | 233 | 29 | | Others | tavily | 314 | 23 | | Others | perplexity | 256 | 22 | | Others | openai gpt-5 | 253 | 53 |
### 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 sample of 100 questions from this benchmark. Learn more in our latest blog[latest blog](/blog/introducing-parallel-search).
### Methodology
- - **Evaluation**: Results are based on tests run using official Search MCP servers provided as an MCP tool to OpenAI's GPT-5 model using the Responses API. In all cases, the MCP tools were limited to only the appropriate web search tool. Answers were evaluated using an LLM as a judge (GPT 4.1).
- - **Cost Calculation**: Cost reflects the average cost per query across all questions run. This cost includes both the search API call and LLM token cost.
- - **Testing Dates**: Testing was conducted from November 3rd to November 5th.
### 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[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[blog](/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[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[blog](/blog/deep-research-benchmarks).
### Methodology
- - Dates: All measurements were made between 08/11/2025 and 08/29/2025.
- - Win Rate: Calculated by comparing RACE[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[here](/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
Search, built for AIs
The most accurate search tool, to bring web context to your AI agents
The most accurate deep and wide research
Run deeper and more accurate research at scale, for the same compute budget
Build a dataset from the web
Define your search criteria in natural language, and get back a structured table of matches
Custom web enrichment
Bring existing data, define output columns to research, and get fresh web enrichments back
Monitor any event on the web
Continuously monitor for any changes on the web
Start building
## Towards a programmatic web for AIs
Parallel is building new interfaces, infrastructure, and business models for AIs to work with the web
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