Great conversation with @20vcFund & @HarryStebbings about the Dubsmash journey and all things consumer products. Lots of learnings over the years packed into an hour. https://t.co/6OH2JU8Gy8
Today, we're announcing our Series B: $200M at a $3.1B valuation, and the release of Arena's Alignment Index.
AI is advancing faster than our ability to evaluate it. The world needs a neutral third party to measure how safe and aligned AI actually is once it's in the hands of real people. That's the role Arena is stepping into today.
The Alignment Index is built from real-world agent traces, starting with three signals: Unauthorized Action, False Attribution, and Deceptive Completion, with results for over 20 frontier models launching today.
Hear more from our CEO @ml_angelopoulos below, and read the full breakdown, and more details about our company growth in thread.
This funding will allow us to go further on safety signals, agentic capabilities, and modalities. It will also allow us to grow the company to match the scale of our mission. Today, we’re still a nimble team of 90, and we're looking for people to join us across research, product, engineering, and more. Come build with us.
As AI gets more capable and autonomous, understanding how well it can safely and effectively work for people only matters more. That's what Arena is built to measure and advance.
Thank you to our community, our investors, and everyone building with us!
Take a look at our open roles if you want to join us (link in bio).
For the first time, China has taken the lead over the US in Frontend Code Arena with the launch of Kimi-K3 by @Kimi_Moonshot.
The last time a Chinese model came close was in early 2025, with DeepSeek-R1.
Seeing this business grow from the early stages to where it is today has been a joy. Proud to be part of this journey, and kudos to the entire team on this milestone.
Arena reached a $100M annual revenue run rate just 8 months after launching our evaluation product. We started as a research project at UC Berkeley with a simple mission: measure AI progress through real-world use. As AI shifts from chatbots to agents taking on longer, higher-stakes work, the problem matters more than ever.
Today, Arena measures real-world AI utility with a community of tens of millions. With Agent Arena, we’re evaluating long-running agents on complex, real-world tasks - how they use tools, adapt to feedback, recover from errors, and accomplish goals set by humans.
We are excited to keep deepening our work in agentic evaluations.
Here’s @ml_angelopoulos on what this milestone means and where we go from here:
Arena reached a $100M annual revenue run rate just 8 months after launching our evaluation product. We started as a research project at UC Berkeley with a simple mission: measure AI progress through real-world use. As AI shifts from chatbots to agents taking on longer, higher-stakes work, the problem matters more than ever.
Today, Arena measures real-world AI utility with a community of tens of millions. With Agent Arena, we’re evaluating long-running agents on complex, real-world tasks - how they use tools, adapt to feedback, recover from errors, and accomplish goals set by humans.
We are excited to keep deepening our work in agentic evaluations.
Here’s @ml_angelopoulos on what this milestone means and where we go from here:
Introducing Agent Arena: real-world agentic evals at scale.
How do you evaluate agents doing actual work? We measure millions of live sessions where real users accomplish real tasks.
On Arena, models now get web search, filesystem, and terminal tools to complete complex workflows: writing code, creating slide deck, researching the web, building apps, and analyzing documents.
Every session produces rich signals. Users iterate with the agent turn-by-turn: approving, editing, correcting, praise or expressing frustration. The environment gives feedback too: shell errors, tool failures, recovery attempts, and more.
Our leaderboard measures each model's agentic performance using causal inference across five signals: task success, steerability, error recovery, user praise vs. complaint, and tool hallucination.
This leaderboard snapshot is built from 300K+ tasks, 2M+ tool calls, and 40M lines of code by agents.
Top labs in Agent Arena:
- #1 @OpenAI: GPT-5.5 (High)
- #2 @AnthropicAI: Claude-Opus-4.7 (Thinking)
- #3 @Zai_org: GLM-5.1
- #4 @GoogleDeepMind: Gemini-3.1-Pro
- #5 @Kimi_Moonshot: Kimi-K2.6
More analysis in the thread, with the full technical blog below.
Introducing Agent Mode: Agentic AI is now measured in the Arena.
Agent Mode can do deep research, create reports, generate images, build websites, debug code, and more.
It completes more complex tasks by using tools like web search, bash in a sandbox environment, image generation, file writing, and asking follow-up questions.
Frontier models are waiting for you in Agent Mode to take on real-world tasks. GPT-5.5, Claude Opus 4.7, Gemini 3.1 Pro, and top open models. Test them yourself.
A huge kudos to Joe & Jared for their persistence and commitment through the HitRecord journey. Their emphasis on community creation is something we deeply respect at Reddit. Excited to see what is next with @MasterClass.
HITRECORD has landed an exit!
Our mission has always been to inspire creativity & I think teaching and learning is a huge part of that.
Last fall, we launched a learning service, & @MasterClass liked it. Now we’re joining their team.
Full post: https://t.co/vDXUFJncH4
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Really excited for @dubsmash to partner with @CashApp to back the first-ever black creator house in Atlanta. Incredible talent, all under one roof.
The New Influencer Capital of America https://t.co/MElsYgg4yi
A great post on @legiontech's Series B funding round by @meganrosedickey: https://t.co/U4GfqHlF5s
Want to help reimagine workforce management and improve the lives of hourly workers? Join the legion! https://t.co/UZL5Saf1oJ
@dubsmash this. Account. Is. On. 🔥 almost at 2k followers. Well over 100k views they just haven’t updated! I’m so happy to be thriving on this platform. Everyone join @dubsmash like now #Verified#dubsmash