MAROKO133 Hot ai: New Gemini 3.1 Pro crushes previous benchmarks, outperforms GPT 5.2 reas

📌 MAROKO133 Eksklusif ai: New Gemini 3.1 Pro crushes previous benchmarks, outperfo

Google has rolled out Gemini 3.1 Pro, the latest update to its flagship AI model, just months after releasing Gemini 3 in November.

The new version enters preview today for developers, enterprises, and consumers, with Google promising stronger reasoning, better coding performance, and improved handling of long documents.

The company says Gemini 3.1 Pro powers the “core intelligence” behind recent upgrades to its Deep Think tool.

While benchmark gains appear modest in some areas, Google claims the update delivers more consistent and reliable performance in real-world tasks.

Stronger reasoning benchmarks

Google highlighted performance gains across several industry tests.

In Humanity’s Last Exam, which measures advanced domain knowledge, Gemini 3.1 Pro scored 44.4 percent. Gemini 3 Pro reached 37.5 percent. OpenAI’s GPT 5.2 scored 34.5 percent.

The company also pointed to a sharp improvement in ARC-AGI-2, a benchmark designed to test novel reasoning problems. Gemini 3 scored 31.1 percent in earlier testing.

Gemini 3.1 Pro jumped to 77.1 percent, more than doubling the previous result.

However, Gemini 3.1 Pro does not top every leaderboard. On Arena, formerly LM Arena, Claude Opus 4.6 leads Gemini in text tasks. It edges Gemini by four points at 1504.

In coding categories, Opus 4.6, Opus 4.5, and GPT 5.2 High also rank ahead.

Arena rankings rely on user voting. Participants choose outputs they prefer.

That format can reward answers that appear correct, even if they contain subtle flaws.

Google designed Gemini 3.1 Pro with developers in mind. The model generates code, explains complex functions, and helps debug errors. It now handles larger code blocks in a single session.

That reduces interruptions during development workflows.

The update also expands long-context capabilities. Gemini 3.1 Pro supports up to one million input tokens and 64,000 output tokens.

Businesses can upload lengthy contracts, reports, or research documents and ask detailed questions without splitting files.

Google kept API pricing unchanged at $2 per million input tokens and $12 per million output tokens.

That stability may appeal to startups and enterprise teams building AI-driven products.

The model also showed gains in the APEX-Agents benchmark, nearly doubling its earlier score.

That benchmark measures performance in agentic workflows, where AI systems execute multi-step tasks.

Enterprise AI push

Google is deploying Gemini 3.1 Pro across its ecosystem. Developers can access it in AI Studio and the Antigravity IDE.

Enterprise customers will see it in Vertex AI and Gemini Enterprise. Consumers can use it through the Gemini app and NotebookLM.

The company says it improved safety controls and monitoring systems.

Businesses handling sensitive data demand stable and predictable outputs.

Google aims to position Gemini 3.1 Pro as a dependable tool for customer support, automation, and document review.

The broader AI market continues to accelerate in the United States. Companies now compare models on reasoning strength, coding depth, and long-context performance.

Gemini 3.1 Pro may not dominate every leaderboard, but Google appears focused on practical gains that matter inside real workflows.

If past patterns continue, Google could soon release a 3.1 update for its faster and lower-cost Flash model.

For now, Gemini 3.1 Pro signals Google’s intent to compete aggressively in enterprise AI.

🔗 Sumber: interestingengineering.com

📌 MAROKO133 Breaking ai: Railway secures $100 million to challenge AWS with AI-nat

Railway, a San Francisco-based cloud platform that has quietly amassed two million developers without spending a dollar on marketing, announced Thursday that it raised $100 million in a Series B funding round, as surging demand for artificial intelligence applications exposes the limitations of legacy cloud infrastructure.

TQ Ventures led the round, with participation from FPV Ventures, Redpoint, and Unusual Ventures. The investment values Railway as one of the most significant infrastructure startups to emerge during the AI boom, capitalizing on developer frustration with the complexity and cost of traditional platforms like Amazon Web Services and Google Cloud.

"As AI models get better at writing code, more and more people are asking the age-old question: where, and how, do I run my applications?" said Jake Cooper, Railway's 28-year-old founder and chief executive, in an exclusive interview with VentureBeat. "The last generation of cloud primitives were slow and outdated, and now with AI moving everything faster, teams simply can't keep up."

The funding is a dramatic acceleration for a company that has charted an unconventional path through the cloud computing industry. Railway raised just $24 million in total before this round, including a $20 million Series A from Redpoint in 2022. The company now processes more than 10 million deployments monthly and handles over one trillion requests through its edge network — metrics that rival far larger and better-funded competitors.

Why three-minute deploy times have become unacceptable in the age of AI coding assistants

Railway's pitch rests on a simple observation: the tools developers use to deploy and manage software were designed for a slower era. A standard build-and-deploy cycle using Terraform, the industry-standard infrastructure tool, takes two to three minutes. That delay, once tolerable, has become a critical bottleneck as AI coding assistants like Claude, ChatGPT, and Cursor can generate working code in seconds.

"When godly intelligence is on tap and can solve any problem in three seconds, those amalgamations of systems become bottlenecks," Cooper told VentureBeat. "What was really cool for humans to deploy in 10 seconds or less is now table stakes for agents."

The company claims its platform delivers deployments in under one second — fast enough to keep pace with AI-generated code. Customers report a tenfold increase in developer velocity and up to 65 percent cost savings compared to traditional cloud providers.

These numbers come directly from enterprise clients, not internal benchmarks. Daniel Lobaton, chief technology officer at G2X, a platform serving 100,000 federal contractors, measured deployment speed improvements of seven times faster and an 87 percent cost reduction after migrating to Railway. His infrastructure bill dropped from $15,000 per month to approximately $1,000.

"The work that used to take me a week on our previous infrastructure, I can do in Railway in like a day," Lobaton said. "If I want to spin up a new service and test different architectures, it would take so long on our old setup. In Railway I can launch six services in two minutes."

Inside the controversial decision to abandon Google Cloud and build data centers from scratch

What distinguishes Railway from competitors like Render and Fly.io is the depth of its vertical integration. In 2024, the company made the unusual decision to abandon Google Cloud entirely and build its own data centers, a move that echoes the famous Alan Kay maxim: "People who are really serious about software should make their own hardware."

"We wanted to design hardware in a way where we could build a differentiated experience," Cooper said. "Having full control over the network, compute, and storage layers lets us do really fast build and deploy loops, the kind that allows us to move at 'agentic speed' while staying 100 percent the smoothest ride in town."

The approach paid dividends during recent widespread outages that affected major cloud providers — Railway remained online throughout.

This soup-to-nuts control enables pricing that undercuts the hyperscalers by roughly 50 percent and newer cloud startups by three to four times. Railway charges by the second for actual compute usage: $0.00000386 per gigabyte-second of memory, $0.00000772 per vCPU-second, and $0.00000006 per gigabyte-second of storage. There are no charges for idle virtual machines — a stark contrast to the traditional cloud model where customers pay for provisioned capacity whether they use it or not.

"The conventional wisdom is that the big guys have economies of scale to offer better pricing," Cooper noted. "But when they're charging for VMs that usually sit idle in the cloud, and we've purpose-built everything to fit much more density on these machines, you have a big opportunity."

How 30 employees built a platform generating tens of millions in annual revenue

Railway has achieved its scale with a team of just 30 employees generating tens of millions in annual revenue — a ratio of revenue per employee that would be exceptional even for established software companies. The company grew revenue 3.5 times last year and continues to expand at 15 percent month-over-month.

Cooper emphasized that the fundraise was strategic rather than necessary. "We're default alive; there's no reason for us to raise money," he said. "We raised because we see a massive opportunity to accelerate, not because we needed to survive."

The company hired its first salesperson only last year and employs just two solutions engineers. Nearly all of Railway's two million users discovered the platform through word of mouth — developers telling other developers about a tool that actually works.

"We basically did the standard engineering thing: if you build it, they will come," Cooper recalled. "And to some degree, they came."

From side projects to Fortune 500 deployments: Railway's unlikely corporate expansion

Despite its grassroots developer community, Railway has made significant inroads into large organizations. The company claims that 31 percent of Fortune 500 companies now use its platform, though deployments range from company-wide infrastructure to individual team projects.

Notable customers include Bilt, the loyalty program company; Intuit's GoCo subsidiary; TripAdvisor's Cruise Critic; and MGM Resorts. Kernel, a Y Combinator-backed startup providing AI infrastructure to over 1,000 companies, runs its entire customer-facing system on Railway for $444 per month.

"At my previous company Clever, which sold …

Konten dipersingkat otomatis.

🔗 Sumber: venturebeat.com