@p0

Infrastructure for AI to search, extract, monitor, and reason over the whole web. Free Web Search → https://xitter.cf/t.co/GRCBKyuAj2

 San Francisco, CA
parallel.ai
Joined August 2011
  • Tweets997
  • Following60
  • Followers23K
361Media
Capable models have gotten cheaper. Search hasn’t, until now. Today we’re introducing Fast mode for Parallel Search: frontier-quality web search for AI that’s 10x cheaper than the default from model labs. - $1 per 1,000 results - 700ms p50 latency - #3 on Artificial Analysis Search Index for intelligence Built for task-efficient models like GPT-5.6 Luna and DeepSeek V4 Pro. 🧵
Parag Agrawal
@paragaAug 21

Introducing Parallel Search Fast

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Everywhere there's inference, there's a need for accurate, fresh, and token-efficient context from around the world.
Brex
@brexHQ18h
BREX DATA: 31% of companies paying for a coding agent now also pay for a tool that reads the web for it.
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Parallel Web Systems retweeted
Parag Agrawal
@paraga20h
This project was the result of deep obsession by a very talented team. And executed under extreme time constraint. Across all disciplines, that is how we operate at @p0
yang you
@yangyou23h
we used Parallel to build Parallel’s first brand campaign. here's a look at the research, connections, and creative decisions behind the artwork.

See the whole picture: A brand campaign built with Parallel

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Travers
@travers0020h
agents need to see the whole picture to be useful @p0 helps give them that context, and we put up some billboard in nyc to tell that story
yang you
@yangyou23h
we used Parallel to build Parallel’s first brand campaign. here's a look at the research, connections, and creative decisions behind the artwork.

See the whole picture: A brand campaign built with Parallel

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MiniMax M3.1 Flash is now #1 on our search capabilities leaderboard for efficiency. It delivers web research performance with Parallel Search similar to DeepSeek V4.1 Flash (1017) at ~3x lower cost.
MiniMax (official)
@MiniMax_AIOct 19
MiniMax M3.1 Flash: Space Performance + Bunny Speed 🚀🐰 Yes, Space Bunny - which has topped global leaderboards with 200T+ tokens - was an earlier version of M3.1 Flash. 💰Redefining the performance economics of agentic AI. M3.1 Flash achieves frontier-level intelligence across key tasks such as Cowork (Automation-Bench: 62%; ALE: 30%), Coding (DeepSWE: 74%) and Visual Reasoning (BabyVision: 91%), at 1/3 the cost of M3 and 3x the speed. API pricing (flat rate): $0.15 / $0.60 per 1M input/output tokens, with cache reads at $0.015 / 1M tokens. Model weights will arrive on Oct 23. API: platform.minimax.io M Plan: minimax.io/m-plan MiniMax Code for desktop: agent.minimax.io/download
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Efficiency is a measure of how cost-effectively a model achieves a good answer relative to all other models on the list. With Parallel Search (fast), MiniMax M3.1 Flash delivers high-quality answers at less than half the cost of the second-best model.
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parallel.ai/leaderboard/mini…

MiniMax M3.1 Flash · Search Capability Leaderboard

MiniMax M3.1 Flash scores 68.8 with Parallel Search, +41.7 over the same model without search, at $43.8 per 1K tasks. #11 in Search Intelligence.

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Mercury 3 is the first diffusion LLM we've tested with Parallel Search. It's Pareto-optimal, delivering capable web research at a price similar to Claude Haiku 5.5, GPT-6 Luna, and DeepSeek V4.1 Flash (base).
Inception
@_inception_aiOct 19
Mercury 3 is live: the most capable diffusion LLM released to date. It outperforms GPT-6 Luna and Claude Haiku 5.5 across a range of benchmarks while running at over 1,000 tokens per second. More than 40x the size of Mercury 1, 3x more intelligent across standard benchmarks, and just as fast, with a 1M-token context window. Available today on the Inception API, @baseten, and @OpenRouter .
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More information: parallel.ai/leaderboard/merc…

Mercury 3 · Search Capability Leaderboard

Mercury 3 scores 63.6 with Parallel Search, +35.5 over the same model without search, at $43.4 per 1K tasks. #25 in Search Intelligence.

parallel.ai
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rayane
@rboussanniOct 19
Will be judging at this hackathon today! @composio @cline @p0
Composio
@composioOct 12
Have you registered yet?! One week from today, we're hosting a hackathon with our friends from @cline and @p0 - come and build an agent with us, open source it, and win some prizes! Register here: luma.com/m8euvq9n?v=2

Cline, Parallel, & Composio Hackathon · Luma

A Cline x Composio x Parallel hackathon for Open Source AI Week Everyone's talking about closed personal agents right now. We want to see what you can build in…

luma.com
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yang you
@yangyouOct 19
last week, we launched the first batch of our @p0 brand campaign in NYC every ad is a collage assembled with citations, representing a real research report built on Parallel's APIs more to come on how we built this. in the meantime, share with us if you encounter it in the wild!
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NEW: DeepSeek V4.1 Flash (1017) is the highest-performing open model we've measured. It's the first to crack the top 10 on the Search Capability Leaderboard across open- and closed-source models.
DeepSeek
@deepseek_aiOct 17
🚀 DeepSeek-V4.1-Flash-1017 is here! Stronger benchmark performance, faster task completion, and shorter outputs than the previous version—a Pareto improvement in performance and cost. Now live on our website, app, and API. 🛠️ Optimized for @DeepSeekHarness, Tencent CodeBuddy, WorkBuddy, and more. Upgrade to DeepSeek Harness v0.2.1-rc.1 or later for the best experience with the new model. 1/4
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The new DeepSeek V4.1 Flash remains Pareto-optimal, with improved research capability, albeit at a higher per-task cost than in its original release. It now matches GPT-6.1 Sol's end-to-end cost and capability.
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Explore more data like this: parallel.ai/leaderboard?utm_…

Search Capability Leaderboard

Not all models benefit equally from web search. We use standard and proprietary benchmarks to evaluate how well top models orchestrate searches and synthesize results into correct answers.

parallel.ai
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Lukas Levert
@lukaslevertOct 16
Muse Spark Open is on the @p0 search capability leaderboard. Similar performance to GPT-5.6 Sol and Gemini 3.7 Flash in an open-weights model.
AI at Meta
@AIatMetaOct 15
Today we’re releasing Muse Spark Open, the most capable US open-weight model to date. Built for coding and agentic work, it excels at long-horizon tasks, complex software engineering, and multi-step reasoning. Key Model Specs: → 816B total / 42B active parameters → 1M token context window → Natively multimodal across text, image, video, audio
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Tim Neumann
@TimNeumann84165Oct 15
Typesafe's CEO @CompleteSkeptic does not trust public benchmarks. Neither do I, Diogo. So @p0 and I wrote a blog evaluating 8 decision models on real company training datasets we have personally audited and iterated on. For what it's worth though, Jev is still on top 👑 parallel.ai/blog/are-all-sys…

Are all System One models created equal?

At Parallel, we train a lot of our own classifiers. But for every classifier we train, there are use cases we can’t prioritize, because each classifier…

parallel.ai
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Vlad
@everythingmetaOct 15
parallel.ai/blog/are-all-sys… We spent two weeks extensively testing decision models on real production use cases. It's been super fun, we learned a lot, and we are sharing our findings! 👇
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Just ask @Lovable to build apps with live web data. Congrats on today’s launch, bringing the full open web to everything you build, powered by Parallel.
Lovable
@LovableOct 15
If your app doesn’t have AI features you’re ngmi We made them simple to build: just ask Lovable. Now powered by even more models from @claudeai, @firecrawl, @GeminiApp, @OpenAI, @perplexity_ai, @p0, and @typesafeai.
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Ethan Kam
@ethank_6Oct 14
we're betting on specialized data and wanted to better understand how and why it improves performance two things stood out: agents did best finding sources on the open web and then pulling the records through connectors, and those structured records are much easier for them to read than web pages
Ethan Kam
@ethank_6Oct 14

Using specialized data to improve research agent accuracy

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We hosted the AI Engineer NYC speaker dinner this week with our friends at @AnthropicAI Thanks to @aiDotEngineer for another great event, and to everyone who stopped by to talk about web search for agents. See you at their Code Summit in SF next month! 🌉
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Parag Agrawal
@paragaOct 14
Every browsing session starts with a search. Starting today, Parallel is the default search provider for @browserbase, powering their Search API and Agents. Search and read with @p0 then write with @browserbase - really great to partner with @pk_iv to serve customers.
Browserbase
@browserbaseOct 14
Browserbase Search is now powered by Parallel (@p0) as the default search provider. Parallel Fast returns sub-second results when speed matters. Parallel Advanced goes deeper for multi-step research when quality matters most.
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