August 21, 2026

# Introducing Fast mode for Parallel Search

Reading time: 3 min

As intelligent models get cheaper, web search needs to follow.

Today, we’re introducing **Fast mode** for Parallel Search: high-quality web search for AI at 10x lower price than the default search in frontier models. It’s the only web search that’s cheap, fast, and accurate enough to pair with the latest class of cost-effective models.

## Fast mode highlights

- $1 per 1,000 requests (10x cheaper than frontier labs, 5x cheaper than other APIs)
- ~700ms average latency (#1 on Artificial Analysis Search Index[Artificial Analysis Search Index] for speed per task)
- #3 on Artificial Analysis Search Index[Artificial Analysis Search Index] for intelligence (Parallel Search Advanced is SOTA)
- Pairs well with models like GPT 5.6 Luna, DeepSeek V4 Pro, and Qwen3.8 27B
- Best for most agent workflows, customer-support assistants, factual Q&A, and general-purpose search

Illustration demonstrating deep research API concepts, web search capabilities, or AI agent integration features — As of August 20th, 2026

In an independent evaluation by Artificial Analysis[independent evaluation by Artificial Analysis], Parallel Fast achieved a Search Index score of 73 while delivering the lowest measured per-task cost across 12 products from seven providers. Just two points shy of #1, Parallel Search Advanced. Without search, the same model scored 33 on the index.

## The economics of the AI stack have changed

Capable models are getting much cheaper. Recently, OpenAI reduced the cost of GPT 5.6 Luna by 80%, dramatically shifting the price-performance frontier for high-volume agentic work. At the same time, models like DeepSeek V4, Qwen3.8 27B, and MiniMax M3 lead for usage on OpenRouter[OpenRouter] and OpenCode[OpenCode].

Over just a few months, cost-per-intelligence has dropped significantly:

- Cost for models scoring ≥ 60 Intelligence Index is **down 8.5x** (Fable 5 to GLM-5.3 Max)
- Cost for models scoring ≥ 50 Intelligence Index is down **12.5x** (GPT 5.4 xhigh to GPT 5.6 Luna Max)

## Better quality, and more than 2x work per dollar spent

Cheaper, more capable models encourage more use, but they also change the cost dynamics of end-to-end agentic work. For many use cases, web search takes up a large majority of the total agent cost — unless you use Parallel (less than 12% of the total cost):

- **2.2x** cheaper on end-to-end cost vs. Brave Search (48% total cost)
- **2.35x** cheaper on end-to-end cost vs. Exa Search Fast (48% total cost)
- **2.79x** cheaper on end-to-end cost vs. Tavily Search Basic (68% total cost)

As the cost of intelligence decreases, teams need search that works with efficient models and high-volume applications. Quality alone is no longer enough. The right measure is how much useful intelligence an agent gets for every dollar spent, and how many tasks they can do in a given period of time.

## When to use Fast mode for Parallel Search

Mode	Features	Use it when	Typical fit
Turbo	~250ms · $1 / 1,000 requests	Latency matters most	Voice/chat agents, autocomplete, simple lookups
Fast	~700ms · $1 / 1,000 requests · #3 quality (73)*	You want the best balance of speed, cost, and quality	Most agents, customer support, factual Q&A, general search
Advanced	~3s · $5 / 1,000 requests · #1 quality (75)*	Quality matters most	Background agents, investigations, code review, deep synthesis

_*Score on Artificial Analysis Search Index_

## How to use Fast mode

Switching modes is a single parameter change. Set mode to "fast" in your Search API[Search API] request, and the rest of your request stays the same:

### Search with Fast mode

1
2
3
4
5
6
7
8
curl https://api.parallel.ai/v1/search \
  -H "Content-Type: application/json" \
  -H "x-api-key: $PARALLEL_API_KEY" \
  -d '{
    "mode": "fast",
    "objective": "What is the current price of NVIDIA stock?",
    "search_queries": ["NVIDIA stock price", "NVDA quote today"]
  }'``` curl https://api.parallel.ai/v1/search \
  -H "Content-Type: application/json" \
  -H "x-api-key: $PARALLEL_API_KEY" \
  -d '{
    "mode": "fast",
    "objective": "What is the current price of NVIDIA stock?",
    "search_queries": ["NVIDIA stock price", "NVDA quote today"]
  }'
```

## Get started with Parallel

### 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
```

## Ready to get started?

Try Parallel[Try Parallel]Contact sales[Contact sales]

Are you an agent? Read this to onboard Parallel[Are you an agent? Read this to onboard Parallel]

By Parallel

August 21, 2026

## Related Posts88

Sep 15, 2026

- [Introducing the Search Capability Leaderboard](https://parallel.ai/blog/search-leaderboard)

Tags:Benchmarks,Product

Author: By Parallel

Sep 10, 2026

- [Merge launches Universal Context Layer with Parallel Search as its default web search provider](https://parallel.ai/blog/merge-universal-context-layer)

Tags:Company

Author: By Parallel

Aug 31, 2026

- [How to evaluate web search for AI](https://parallel.ai/blog/how-to-eval-web-search)

Tags:Developers

Author: By Vlad Shulman

Aug 29, 2026

- [Building an apartment discovery tool with Parallel](https://parallel.ai/blog/apartment-finder)

Tags:Developers

Author: By Elijah Jacob

Aug 25, 2026

# Introducing Fast mode for Parallel Search

## Fast mode highlights

## The economics of the AI stack have changed

## Better quality, and more than 2x work per dollar spent

## When to use Fast mode for Parallel Search

## How to use Fast mode

## Get started with Parallel

## Ready to get started?

## Related Posts88

- [Introducing the Search Capability Leaderboard](https://parallel.ai/blog/search-leaderboard)

- [Merge launches Universal Context Layer with Parallel Search as its default web search provider](https://parallel.ai/blog/merge-universal-context-layer)

- [How to evaluate web search for AI](https://parallel.ai/blog/how-to-eval-web-search)

- [Building an apartment discovery tool with Parallel](https://parallel.ai/blog/apartment-finder)

- [Full Parallel API suite is now available on Google Cloud Marketplace](https://parallel.ai/blog/gcp-marketplace-full-api-suite)

- [ Parallel ranks #1 on the Artificial Analysis Search Index, defining the Pareto frontier of quality, cost, and speed](https://parallel.ai/blog/artificial-analysis-best-search-api)

- [Always-on web monitoring research with Parallel Monitors and Hermes Agent](https://parallel.ai/blog/always-on-agents-monitor-api)

- [Building an always-on background agent to proactively support customers](https://parallel.ai/blog/customer-watch-background-agent)

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

- [Building a vendor intelligence system with Parallel](https://parallel.ai/blog/vendor-intelligence-system)

- [Parallel and Google Cloud Announce Partnership for Agentic Web Search on Gemini Enterprise Agent Platform](https://parallel.ai/blog/google-cloud-partnership)

- [$5 in free Parallel credits, every month](https://parallel.ai/blog/free-tier-parallel)

- [Introducing Turbo mode for Parallel Search](https://parallel.ai/blog/parallel-search-turbo)

- [Building a realtime voice agent with GPT-Realtime-2.1 and Parallel Search Turbo](https://parallel.ai/blog/gpt-realtime-parallel-turbo)

- [How Nooks cut web search costs 70.5% by switching to Parallel](https://parallel.ai/blog/case-study-nooks)

- [How Build created live geofenced alerts powered by Parallel for institutional real estate](https://parallel.ai/blog/case-study-build)

- [OpenClaw now has free, LLM-optimized web search by default powered by Parallel](https://parallel.ai/blog/free-web-search-openclaw)

- [Introducing real-time Entity Search](https://parallel.ai/blog/entity-search-company)

- [How we enrich & triage inbound leads using the Parallel Task API](https://parallel.ai/blog/enrich-triage-inbound-leads-parallel-task-api)

- [How AirOps creates citation-worthy content at scale, powered by Parallel](https://parallel.ai/blog/case-study-airops)

- [Introducing Index by Parallel](https://parallel.ai/blog/introducing-index-by-parallel)

- [Parallel Monitor API: New processor tiers, snapshots and event streams, and Basis on every event](https://parallel.ai/blog/monitor-api)

- [How we built parallelmpp.dev](https://parallel.ai/blog/parallel-mpp-dev)

- [How Actively's Per Account Agents use Parallel to turn the entire web into a proactive sales intelligence layer](https://parallel.ai/blog/case-study-actively)

- [Parallel Raises at $2 Billion Valuation to Scale Web Infrastructure for Agents](https://parallel.ai/blog/series-b)

- [Building a free CLI agent with Pi, Ollama, Gemma 4, and Parallel](https://parallel.ai/blog/free-CLI-agent)

- [Parallel Search is now free for agents via MCP](https://parallel.ai/blog/free-web-search-mcp)

- [Upgrades to the Parallel Search & Extract APIs](https://parallel.ai/blog/parallel-search-api)

- [How Finch is scaling plaintiff law with AI agents that research like associates](https://parallel.ai/blog/case-study-finch)

- [Genpact and Parallel Web Systems Partner to Drive Tangible Efficiency from AI Systems](https://parallel.ai/blog/genpact-parallel-partnership)

- [How Genpact helps top US insurers cut contents claims processing times in half with Parallel ](https://parallel.ai/blog/case-study-genpact)

- [A new deep research frontier on DeepSearchQA with the Task API Harness](https://parallel.ai/blog/deep-research)

- [How Modal saves tens of thousands annually by building in-house GTM pipelines with Parallel](https://parallel.ai/blog/case-study-modal)

- [How Opendoor uses Parallel as the enterprise grade web research layer powering its AI-native real estate operations](https://parallel.ai/blog/case-study-opendoor)

- [Introducing stateful web research agents with multi-turn conversations](https://parallel.ai/blog/task-api-interactions)

- [Parallel is live on Tempo, now available natively to agents with the Machine Payments Protocol](https://parallel.ai/blog/tempo-stripe-mpp)

- [How Parallel helped Kepler build AI that finance professionals can actually trust](https://parallel.ai/blog/case-study-kepler)

- [Introducing the Parallel CLI](https://parallel.ai/blog/parallel-cli)

- [How Profound helps brands win AI Search with high-quality web research and content creation powered by Parallel](https://parallel.ai/blog/case-study-profound)

- [How Harvey is expanding legal AI internationally with Parallel](https://parallel.ai/blog/case-study-harvey)

- [How Tabstack by Mozilla enables agents to navigate the web with Parallel’s best-in-class web search](https://parallel.ai/blog/case-study-tabstack)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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