Introducing Parallel Search Fast: A web search API for agents that’s fast, cheap, and accurate. Learn more.[Learn more.]

July 13, 2026

# Introducing Turbo mode for Parallel Search

Tags:Product
Reading time: 3 min

Today, we're releasing **Turbo **mode for Parallel Search[Parallel Search], the fastest and most affordable way to ground your agents in high-quality information from the web.

[Turbo Mode for Parallel Search](https://youtu.be/sAg-EsRzNWk)

## Do 14x more research in 5x less time

With a median latency of 200ms (p50) and a price of just $1 per 1,000 requests, Turbo is in a class of its own. It’s up to 14x cheaper than the default search in frontier models, while maintaining similar or better accuracy.

Parallel Search TurboFast Search APIsFrontier Model SearchSERP APIs
Cost / 1K Requests$1$5-7$10-14$1
Median Latency200ms200-350ms~1s~1s
OutputLLM-Ready ResultsLLM-Ready ResultsLLM-Ready ResultsRaw Search Results

## New use cases unlocked by low-latency, low-cost search

Turbo is built for the workloads where latency is core to the product experience: voice agents, chat, support, anywhere a user is waiting for an answer.

Voice and chat apps have previously had to make compromises on search quality. Parallel Search Turbo eliminates the tradeoff with the added benefit of being highly cost-effective.

  • - **Voice AI:** Voice agents need to be low-latency to feel magical. Use Turbo with real-time voice models to add web grounding without introducing awkward pauses.
  • - **Deep Research: **Applications that use deep research for decision-making can now perform significantly more searches, broadening the surface in comprehensive fan-outs.
  • - **Consumer chat**: Consumer chat applications rely on heuristics to decide when web grounding can enhance a response. Apps can now use Turbo’s low latency and cost to offer grounded search as the default user experience.
  • - **Reinforcement learning**: Training models with web grounding depends on millions of live queries per rollout. Turbo cuts that latency so more of your compute budget goes to learning.
  • - **Small, local models: **Small models can answer harder questions with generous use of web search, making efficient local AI more viable.
BrowseCompHLEWebWalkerSimpleQACoding

Accuracy (%)

Parallel Turbo51% / 216ms
Brave Search38.3% / 430ms
Exa Instant33.7% / 361ms
SerpAPI23.3% / 999ms
Tavily Ultra Fast19.3% / 357ms

Latency: p50 client-side wall clock per search API request, in ms, shown on a log scale (best across runs). OpenAI Web Search is omitted (single search-call latency not available).

Parallel
Others
Benchmark comparison across p50 Search Latency (ms) and Accuracy (%). Latency: p50 client-side wall clock per search API request, in ms, shown on a log scale (best across runs). OpenAI Web Search is omitted (single search-call latency not available).
+Methodology

**Dataset**

BrowseComp[BrowseComp], created by OpenAI, contains 1,266 questions that require persistent browsing to locate hard-to-find, entangled information on the web.

**Evaluation methodology**

Multi-hop evaluation: a GPT-5.4 agent runs with up to 20 tool calls (search_web, plus web_fetch for engines with an extract API: Parallel, Exa, and Tavily; Brave and SerpAPI are search-only). Answers are graded by an LLM judge (GPT-5.4, per-suite grader prompts). Benchmarks were run across multiple sessions, with the best observed scores selected for each provider.

Latency: search-call latency is the client-side wall clock measured around a single provider search API request, from a client in us-central; we report the p50 across all questions (best across runs). OpenAI Web Search scored 57.7% accuracy on this suite but is not plotted because its single search-call latency is not available.

**Testing dates**

Evals were run between July 10 and 12, 2026.

### BrowseComp (Latency)

| Series   | Model             | p50 Search Latency (ms) | Accuracy (%) |
| -------- | ----------------- | ----------------------- | ------------ |
| Parallel | Parallel Turbo    | 216                     | 51           |
| Others   | Exa Instant       | 361                     | 33.7         |
| Others   | Tavily Ultra Fast | 357                     | 19.3         |
| Others   | Brave Search      | 430                     | 38.3         |
| Others   | SerpAPI           | 999                     | 23.3         |

Latency: p50 client-side wall clock per search API request, in ms, shown on a log scale (best across runs). OpenAI Web Search is omitted (single search-call latency not available).

**Dataset**

BrowseComp[BrowseComp], created by OpenAI, contains 1,266 questions that require persistent browsing to locate hard-to-find, entangled information on the web.

**Evaluation methodology**

Multi-hop evaluation: a GPT-5.4 agent runs with up to 20 tool calls (search_web, plus web_fetch for engines with an extract API: Parallel, Exa, and Tavily; Brave and SerpAPI are search-only). Answers are graded by an LLM judge (GPT-5.4, per-suite grader prompts). Benchmarks were run across multiple sessions, with the best observed scores selected for each provider.

Latency: search-call latency is the client-side wall clock measured around a single provider search API request, from a client in us-central; we report the p50 across all questions (best across runs). OpenAI Web Search scored 57.7% accuracy on this suite but is not plotted because its single search-call latency is not available.

**Testing dates**

Evals were run between July 10 and 12, 2026.

### HLE (Latency)

| Series   | Model             | p50 Search Latency (ms) | Accuracy (%) |
| -------- | ----------------- | ----------------------- | ------------ |
| Parallel | Parallel Turbo    | 220                     | 52.7         |
| Others   | Exa Instant       | 358                     | 49.3         |
| Others   | Tavily Ultra Fast | 243                     | 42           |
| Others   | Brave Search      | 563                     | 47.7         |
| Others   | SerpAPI           | 865                     | 40           |

Latency: p50 client-side wall clock per search API request, in ms, shown on a log scale (best across runs). OpenAI Web Search is omitted (single search-call latency not available).

**Dataset**

Humanity's Last Exam (HLE)[Humanity's Last Exam (HLE)], created by CAIS and Scale AI, is a benchmark of expert-written questions at the frontier of human knowledge across dozens of subjects.

**Evaluation methodology**

Multi-hop evaluation: a GPT-5.4 agent runs with up to 20 tool calls (search_web, plus web_fetch for engines with an extract API: Parallel, Exa, and Tavily; Brave and SerpAPI are search-only). Answers are graded by an LLM judge (GPT-5.4, per-suite grader prompts).

Latency: search-call latency is the client-side wall clock measured around a single provider search API request, from a client in us-central; we report the p50 across all questions (best across runs). OpenAI Web Search scored 66% accuracy on this suite but is not plotted because its single search-call latency is not available.

**Testing dates**

Evals were run between July 10 and 12, 2026.

### WebWalker (Latency)

| Series   | Model             | p50 Search Latency (ms) | Accuracy (%) |
| -------- | ----------------- | ----------------------- | ------------ |
| Parallel | Parallel Turbo    | 217                     | 75.7         |
| Others   | Exa Instant       | 336                     | 65           |
| Others   | Tavily Ultra Fast | 240                     | 63.7         |
| Others   | Brave Search      | 503                     | 65.7         |
| Others   | SerpAPI           | 761                     | 50.7         |

Latency: p50 client-side wall clock per search API request, in ms, shown on a log scale (best across runs). OpenAI Web Search is omitted (single search-call latency not available).

**Dataset**

WebWalkerQA[WebWalkerQA] evaluates an agent's ability to traverse the web — navigating through linked pages to find information that a single search does not surface.

**Evaluation methodology**

Multi-hop evaluation: a GPT-5.4 agent runs with up to 20 tool calls (search_web, plus web_fetch for engines with an extract API: Parallel, Exa, and Tavily; Brave and SerpAPI are search-only). Answers are graded by an LLM judge (GPT-5.4, per-suite grader prompts).

Latency: search-call latency is the client-side wall clock measured around a single provider search API request, from a client in us-central; we report the p50 across all questions (best across runs). OpenAI Web Search scored 80.7% accuracy on this suite but is not plotted because its single search-call latency is not available.

**Testing dates**

Evals were run between July 10 and 12, 2026.

### SimpleQA (Latency)

| Series   | Model                   | p50 Search Latency (ms) | Accuracy (%) |
| -------- | ----------------------- | ----------------------- | ------------ |
| Parallel | Parallel Search (Turbo) | 240                     | 91           |
| Others   | Exa Instant             | 335                     | 89.3         |
| Others   | Tavily Ultra Fast       | 150                     | 72           |
| Others   | Brave Search            | 475                     | 87           |
| Others   | SerpAPI                 | 652                     | 76.7         |

Latency: p50 client-side wall clock per search API request, in ms, shown on a log scale (best across runs).

**Dataset**

SimpleQA[SimpleQA], created by OpenAI, contains 4,326 short, fact-seeking questions across a variety of domains.

**Evaluation methodology**

Single-step evaluation: the raw question is sent as the search query (num_results=10, with an equal ~1,000 character-per-result content budget for every engine) and GPT-5.4 (reasoning: high) synthesizes an answer from the search results only. Answers are graded by an LLM judge (GPT-5.4, per-suite grader prompts).

Latency: search-call latency is the client-side wall clock measured around a single provider search API request, from a client in us-central; we report the p50 across all questions (best across runs).

**Testing dates**

Evals were run between July 10 and 12, 2026.

### Coding (Latency)

| Series   | Model             | p50 Search Latency (ms) | Accuracy (%) |
| -------- | ----------------- | ----------------------- | ------------ |
| Parallel | Parallel Turbo    | 216                     | 79.7         |
| Others   | Exa Instant       | 341                     | 76.7         |
| Others   | Tavily Ultra Fast | 208                     | 71.9         |
| Others   | Brave Search      | 514                     | 64.3         |
| Others   | SerpAPI           | 683                     | 54           |

Latency: p50 client-side wall clock per search API request, in ms, shown on a log scale (best across runs). OpenAI Web Search is omitted (single search-call latency not available).

**Dataset**

A proprietary coding dataset derived from production queries to Parallel's search API.

**Evaluation methodology**

Multi-hop evaluation: a GPT-5.4 agent runs with up to 20 tool calls (search_web, plus web_fetch for engines with an extract API: Parallel, Exa, and Tavily; Brave and SerpAPI are search-only). Answers are graded by an LLM judge (GPT-5.4, per-suite grader prompts).

Latency: search-call latency is the client-side wall clock measured around a single provider search API request, from a client in us-central; we report the p50 across all questions (best across runs). OpenAI Web Search scored 76.7% accuracy on this suite but is not plotted because its single search-call latency is not available.

**Testing dates**

Evals were run between July 10 and 12, 2026.

## Replace SERP APIs

Turbo is especially well-suited for replacing workflows built on SERP APIs. While SERP calls are affordable, they require additional work to fetch and process information into model-ready context. By default, Turbo returns dense, relevant excerpts directly, so the model spends fewer input tokens on a better answer.

Turbo is also highly cost-efficient on complex multi-hop benchmarks, outperforming other search APIs at a fraction of the total cost.

BrowseCompHLEWebWalker SimpleQACoding

Accuracy (%)

OpenAI Web Search57.7% / 912CPM
Parallel Turbo51% / 350CPM
Brave Search38.3% / 336CPM
Exa Instant33.7% / 966CPM
SerpAPI23.3% / 296CPM
Tavily Ultra Fast19.3% / 822CPM

CPM: USD per 1000 requests.

Parallel
Others
Benchmark comparison across Cost (CPM) and Accuracy (%). CPM: USD per 1000 requests.
+Methodology

**Dataset**

BrowseComp[BrowseComp], created by OpenAI, contains 1,266 questions that require persistent browsing to locate hard-to-find, entangled information on the web.

**Evaluation methodology**

Multi-hop evaluation: a GPT-5.4 agent runs with up to 20 tool calls (search_web, plus web_fetch for engines with an extract API: Parallel, Exa, and Tavily; Brave and SerpAPI are search-only). Answers are graded by an LLM judge (GPT-5.4, per-suite grader prompts).

Cost includes LLM token costs and tool call costs.

**Testing dates**

Evals were run between July 10 and 12, 2026.

### BrowseComp

| Series   | Model             | Cost (CPM) | Accuracy (%) |
| -------- | ----------------- | ---------- | ------------ |
| Parallel | Parallel Turbo    | 350        | 51           |
| Others   | Exa Instant       | 966        | 33.7         |
| Others   | Tavily Ultra Fast | 822        | 19.3         |
| Others   | Brave Search      | 336        | 38.3         |
| Others   | SerpAPI           | 296        | 23.3         |
| Others   | OpenAI Web Search | 912        | 57.7         |

CPM: USD per 1000 requests.

**Dataset**

BrowseComp[BrowseComp], created by OpenAI, contains 1,266 questions that require persistent browsing to locate hard-to-find, entangled information on the web.

**Evaluation methodology**

Multi-hop evaluation: a GPT-5.4 agent runs with up to 20 tool calls (search_web, plus web_fetch for engines with an extract API: Parallel, Exa, and Tavily; Brave and SerpAPI are search-only). Answers are graded by an LLM judge (GPT-5.4, per-suite grader prompts).

Cost includes LLM token costs and tool call costs.

**Testing dates**

Evals were run between July 10 and 12, 2026.

### HLE

| Series   | Model             | Cost (CPM) | Accuracy (%) |
| -------- | ----------------- | ---------- | ------------ |
| Parallel | Parallel Turbo    | 298        | 52.7         |
| Others   | Exa Instant       | 596        | 49.3         |
| Others   | Tavily Ultra Fast | 426        | 42           |
| Others   | Brave Search      | 311        | 47.7         |
| Others   | SerpAPI           | 250        | 40           |
| Others   | OpenAI Web Search | 513        | 66           |

CPM: USD per 1000 requests.

**Dataset**

Humanity's Last Exam (HLE)[Humanity's Last Exam (HLE)], created by CAIS and Scale AI, is a benchmark of expert-written questions at the frontier of human knowledge across dozens of subjects.

**Evaluation methodology**

Multi-hop evaluation: a GPT-5.4 agent runs with up to 20 tool calls (search_web, plus web_fetch for engines with an extract API: Parallel, Exa, and Tavily; Brave and SerpAPI are search-only). Answers are graded by an LLM judge (GPT-5.4, per-suite grader prompts).

Cost includes LLM token costs and tool call costs.

**Testing dates**

Evals were run between July 10 and 12, 2026.

### WebWalker (multi-hop) 

| Series   | Model             | Cost (CPM) | Accuracy (%) |
| -------- | ----------------- | ---------- | ------------ |
| Parallel | Parallel Turbo    | 82         | 75.7         |
| Others   | Exa Instant       | 193        | 65           |
| Others   | Tavily Ultra Fast | 188        | 63.7         |
| Others   | Brave Search      | 143        | 65.7         |
| Others   | SerpAPI           | 124        | 50.7         |
| Others   | OpenAI Web Search | 275        | 80.7         |

CPM: USD per 1000 requests.

**Dataset**

WebWalkerQA[WebWalkerQA] evaluates an agent's ability to traverse the web — navigating through linked pages to find information that a single search does not surface.

**Evaluation methodology**

Multi-hop evaluation: a GPT-5.4 agent runs with up to 20 tool calls (search_web, plus web_fetch for engines with an extract API: Parallel, Exa, and Tavily; Brave and SerpAPI are search-only). Answers are graded by an LLM judge (GPT-5.4, per-suite grader prompts).

Cost includes LLM token costs and tool call costs.

**Testing dates**

Evals were run between July 10 and 12, 2026.

### SimpleQA

| Series   | Model             | Cost (CPM) | Accuracy (%) |
| -------- | ----------------- | ---------- | ------------ |
| Parallel | Parallel Turbo    | 8          | 91           |
| Others   | Exa Instant       | 20         | 89.3         |
| Others   | Tavily Ultra Fast | 23         | 72           |
| Others   | Brave Search      | 16         | 87           |
| Others   | SerpAPI           | 6          | 76.7         |

CPM: USD per 1000 requests.

**Dataset**

SimpleQA[SimpleQA], created by OpenAI, contains 4,326 short, fact-seeking questions across a variety of domains.

**Evaluation methodology**

Single-step evaluation: the raw question is sent as the search query (num_results=10, with an equal ~1,000 character-per-result content budget for every engine) and GPT-5.4 (reasoning: high) synthesizes an answer from the search results only. Answers are graded by an LLM judge (GPT-5.4, per-suite grader prompts).

Cost includes LLM token costs and tool call costs.

**Testing dates**

Evals were run between July 10 and 12, 2026.

### Coding (multi-hop)

| Series   | Model             | Cost (CPM) | Accuracy (%) |
| -------- | ----------------- | ---------- | ------------ |
| Parallel | Parallel Turbo    | 131        | 79.7         |
| Others   | Exa Instant       | 316        | 76.7         |
| Others   | Tavily Ultra Fast | 314        | 71.9         |
| Others   | Brave Search      | 204        | 64.3         |
| Others   | SerpAPI           | 131        | 54           |
| Others   | OpenAI Web Search | 475        | 76.7         |

CPM: USD per 1000 requests.

**Dataset**

A proprietary coding dataset derived from production queries to Parallel's search API.

**Evaluation methodology**

Multi-hop evaluation: a GPT-5.4 agent runs with up to 20 tool calls (search_web, plus web_fetch for engines with an extract API: Parallel, Exa, and Tavily; Brave and SerpAPI are search-only). Answers are graded by an LLM judge (GPT-5.4, per-suite grader prompts).

Cost includes LLM token costs and tool call costs.

**Testing dates**

Evals were run between July 10 and 12, 2026.

## New infrastructure for abundant web search

Agents change the shape of demand for web data. A typical person runs a handful of searches a day, but an agent runs thousands: inside loops, across tools, and often multiple times per question. Lowering the cost and latency of high-quality search expands where search can be offered.

At $1 per 1,000 requests, there is less need to ration search, and developers can run it in more scenarios than ever: from every chat request to every row in a batch job. With web search this fast and affordable, every piece of software can be grounded in the web.

Turbo is the first product powered by Parallel’s new search architecture: a re-engineered search stack with innovation across hardware, model training, and index design to bring the cost and speed of search closer to zero.

### Give this to your agent:
1
Use curl to read parallel.ai/agents.md and perform the setup to install Parallel``` 
Use curl to read parallel.ai/agents.md and perform the setup to install Parallel
```

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July 13, 2026

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Parallel | Vercel

Feb 4, 2026

- [Parallel Web Tools and Agents now available across Vercel AI Gateway, AI SDK, and Marketplace](https://parallel.ai/blog/vercel)

Tags:Product
Author: By Parallel
Product release: Authenticated page access for the Parallel Task API

Jan 28, 2026

- [Authenticated page access for the Parallel Task API](https://parallel.ai/blog/authenticated-page-access)

Tags:Product
Author: By Parallel
Introducing structured outputs for the Monitor API

Jan 21, 2026

- [Introducing structured outputs for the Monitor API](https://parallel.ai/blog/structured-outputs-monitor)

Tags:Product
Author: By Parallel
Product release: Research Models with Basis for the Parallel Chat API

Jan 15, 2026

- [Introducing research models with Basis for the Parallel Chat API](https://parallel.ai/blog/research-models-chat)

Tags:Product
Author: By Parallel
Parallel + Cerebras

Jan 8, 2026

- [Build a real-time fact checker with Parallel and Cerebras](https://parallel.ai/blog/cerebras-fact-checker)

Tags:Developers
Author: By Parallel
DeepSearch QA: Task API

Dec 17, 2025

- [Parallel Task API achieves state-of-the-art accuracy on DeepSearchQA](https://parallel.ai/blog/deepsearch-qa)

Tags:Benchmarks
Author: By Parallel
Product release: Granular Basis

Dec 16, 2025

- [Introducing Granular Basis for the Task API](https://parallel.ai/blog/granular-basis-task-api)

Tags:Product
Author: By Parallel
How Amp’s coding agents build better software with Parallel Search

Dec 11, 2025

- [How Amp’s coding agents build better software with Parallel Search](https://parallel.ai/blog/case-study-amp)

Tags:Customers
Author: By Parallel
Latency improvements on the Parallel Task API

Dec 10, 2025

- [Latency improvements on the Parallel Task API ](https://parallel.ai/blog/task-api-latency)

Tags:Product
Author: By Parallel
Product release: Extract

Nov 20, 2025

- [Introducing Parallel Extract](https://parallel.ai/blog/introducing-parallel-extract)

Tags:Product
Author: By Parallel
FindAll API - Product Release

Nov 18, 2025

- [Introducing Parallel FindAll](https://parallel.ai/blog/introducing-findall-api)

Tags:Product,Benchmarks
Author: By Parallel
Product release: Monitor API

Nov 13, 2025

- [Introducing Parallel Monitor](https://parallel.ai/blog/monitor-api-beta)

Tags:Product
Author: By Parallel
Parallel raises $100M Series A to build web infrastructure for agents

Nov 12, 2025

- [Parallel raises $100M Series A to build web infrastructure for agents](https://parallel.ai/blog/series-a)

Tags:Company
Author: By Parallel
How Macroscope reduced code review false positives with Parallel

Nov 11, 2025

- [How Macroscope reduced code review false positives with Parallel](https://parallel.ai/blog/case-study-macroscope)

Tags:Customers
Author: By Parallel
Product release - Parallel Search API

Nov 6, 2025

- [Introducing Parallel Search](https://parallel.ai/blog/parallel-search-api-beta)

Tags:Benchmarks
Author: By Parallel
Benchmarks: SealQA: Task API

Nov 3, 2025

- [Parallel processors set new price-performance standard on SealQA benchmark](https://parallel.ai/blog/benchmarks-task-api-sealqa)

Tags:Benchmarks
Author: By Parallel
Introducing LLMTEXT, an open source toolkit for the llms.txt standard

Oct 30, 2025

- [Introducing LLMTEXT, an open source toolkit for the llms.txt standard](https://parallel.ai/blog/LLMTEXT-for-llmstxt)

Tags:Product
Author: By Parallel
Starbridge + Parallel

Oct 23, 2025

- [How Starbridge powers public sector GTM with state-of-the-art web research](https://parallel.ai/blog/case-study-starbridge)

Tags:Customers
Author: By Parallel
Building a market research platform with Parallel Deep Research

Oct 22, 2025

- [Building a market research platform with Parallel Deep Research](https://parallel.ai/blog/cookbook-market-research-platform-with-parallel)

Tags:Developers
Author: By Parallel
How Lindy brings state-of-the-art web research to automation flows

Oct 17, 2025

- [How Lindy brings state-of-the-art web research to automation flows](https://parallel.ai/blog/case-study-lindy)

Tags:Customers
Author: By Parallel
Introducing the Parallel Task MCP Server

Oct 16, 2025

- [Introducing the Parallel Task MCP Server](https://parallel.ai/blog/parallel-task-mcp-server)

Tags:Product
Author: By Parallel
Introducing the Core2x Processor for improved compute control on the Task API

Oct 9, 2025

- [Introducing the Core2x Processor for improved compute control on the Task API](https://parallel.ai/blog/core2x-processor)

Tags:Product
Author: By Parallel
How Day AI merges private and public data for business intelligence

Oct 8, 2025

- [How Day AI merges private and public data for business intelligence](https://parallel.ai/blog/case-study-day-ai)

Tags:Customers
Author: By Parallel
Full Basis framework for all Task API Processors

Oct 7, 2025

- [Full Basis framework for all Task API Processors](https://parallel.ai/blog/full-basis-framework-for-task-api)

Tags:Product
Author: By Parallel
Building a real-time streaming task manager with Parallel

Oct 6, 2025

- [Building a real-time streaming task manager with Parallel](https://parallel.ai/blog/cookbook-sse-task-manager-with-parallel)

Tags:Developers
Author: By Parallel
How Gumloop built a new AI automation framework with web intelligence as a core node

Sep 30, 2025

- [How Gumloop built a new AI automation framework with web intelligence as a core node](https://parallel.ai/blog/case-study-gumloop)

Tags:Customers
Author: By Parallel
Introducing the TypeScript SDK

Sep 16, 2025

- [Introducing the TypeScript SDK](https://parallel.ai/blog/typescript-sdk)

Tags:Product
Author: By Parallel
Building a serverless competitive intelligence platform with MCP + Task API

Sep 12, 2025

- [Building a serverless competitive intelligence platform with MCP + Task API](https://parallel.ai/blog/cookbook-competitor-research-with-reddit-mcp)

Tags:Developers
Author: By Parallel
Introducing Parallel Deep Research reports

Sep 11, 2025

- [Introducing Parallel Deep Research reports](https://parallel.ai/blog/deep-research-reports)

Tags:Product
Author: By Parallel
BrowseComp / DeepResearch: Task API

Sep 9, 2025

- [A new pareto-frontier for Deep Research price-performance](https://parallel.ai/blog/deep-research-benchmarks)

Tags:Benchmarks
Author: By Parallel
Building a Full-Stack Search Agent with Parallel and Cerebras

Sep 5, 2025

- [Building a Full-Stack Search Agent with Parallel and Cerebras](https://parallel.ai/blog/cookbook-search-agent)

Tags:Developers
Author: By Parallel
Webhooks for the Parallel Task API

Aug 21, 2025

- [Webhooks for the Parallel Task API](https://parallel.ai/blog/webhooks)

Tags:Product
Author: By Parallel
Introducing Parallel: Web Search Infrastructure for AIs

Aug 14, 2025

- [Introducing Parallel: Web Search Infrastructure for AIs ](https://parallel.ai/blog/introducing-parallel)

Tags:Benchmarks,Product
Author: By Parallel
Introducing SSE for Task Runs

Aug 7, 2025

- [Introducing SSE for Task Runs](https://parallel.ai/blog/sse-for-tasks)

Tags:Product
Author: By Parallel
A new line of advanced Processors: Ultra2x, Ultra4x, and Ultra8x

Aug 5, 2025

- [A new line of advanced Processors: Ultra2x, Ultra4x, and Ultra8x ](https://parallel.ai/blog/new-advanced-processors)

Tags:Product
Author: By Parallel
Introducing Auto Mode for the Parallel Task API

Aug 4, 2025

- [Introducing Auto Mode for the Parallel Task API](https://parallel.ai/blog/task-api-auto-mode)

Tags:Product
Author: By Parallel
A linear dithering of a search interface for agents

Jul 31, 2025

- [A state-of-the-art search API purpose-built for agents](https://parallel.ai/blog/search-api-benchmark)

Tags:Benchmarks
Author: By Parallel
Parallel Search MCP Server in Devin

Jul 31, 2025

- [Parallel Search MCP Server in Devin](https://parallel.ai/blog/parallel-search-mcp-in-devin)

Tags:Product
Author: By Parallel
Introducing Tool Calling via MCP Servers

Jul 28, 2025

- [Introducing Tool Calling via MCP Servers](https://parallel.ai/blog/mcp-tool-calling)

Tags:Product
Author: By Parallel
Introducing the Parallel Search MCP Server

Jul 14, 2025

- [Introducing the Parallel Search MCP Server ](https://parallel.ai/blog/search-mcp-server)

Tags:Product
Author: By Parallel
Starting today, Source Policy is available for both the Parallel Task API and Search API - giving you granular control over which sources your AI agents access and how results are prioritized.

Jul 8, 2025

- [Introducing Source Policy](https://parallel.ai/blog/source-policy)

Tags:Product
Author: By Parallel
The Parallel Task Group API

Jul 2, 2025

- [The Parallel Task Group API](https://parallel.ai/blog/task-group-api)

Tags:Product
Author: By Parallel
State of the Art Deep Research APIs

Jun 17, 2025

- [State of the Art Deep Research APIs](https://parallel.ai/blog/deep-research-browsecomp)

Tags:Benchmarks
Author: By Parallel
Introducing the Parallel Search API

Jun 10, 2025

- [Parallel Search API is now available in alpha](https://parallel.ai/blog/search-api-alpha)

Tags:Product
Author: By Parallel
Introducing the Parallel Chat API - a low latency web research API for web based LLM completions. The Parallel Chat API returns completions in text and structured JSON format, and is OpenAI Chat Completions compatible.

May 30, 2025

- [Introducing the Parallel Chat API ](https://parallel.ai/blog/chat-api)

Tags:Product
Author: By Parallel
Parallel Web Systems introduces Basis with calibrated confidences - a new verification framework for AI web research and search API outputs that sets a new industry standard for transparent and reliable deep research.

May 16, 2025

- [Introducing Basis with Calibrated Confidences ](https://parallel.ai/blog/introducing-basis-with-calibrated-confidences)

Tags:Product
Author: By Parallel
The Parallel Task API is a state-of-the-art system for automated web research that delivers the highest accuracy at every price point.

Apr 24, 2025

- [Introducing the Parallel Task API](https://parallel.ai/blog/parallel-task-api)

Tags:Product,Benchmarks
Author: By Parallel