Model Alert... GPT-5.1 Launched... will be 'smarter, more conversational'

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ChatGPT powered by new GPT-5.1 will be “smarter, more conversational,” says OpenAI

OpenAI said that GPT‑5.1 Instant would be warmer and more conversational, while GPT‑5 Thinking would become more efficient and easier to understand

OpenAI on Wednesday (November 12, 2025) announced an upgrade to its GPT-5 AI model, with the “warmer” and “more intelligent” GPT‑5.1 Instant model, and an easier to understand GPT‑5.1 Thinking model.

The company noted that GPT-5.1 Instant was its most used model, while the GPT-5.1 Thinking model was better calibrated to address both simple and complex queries, to enable both fast and slow answers based on the context.

OpenAI further said that GPT-5.1 would deliver a “smarter, more conversational ChatGPT.”

OpenAI CEO Sam Altman hailed the new releases, and pointed out how users could also customise the AI models to fit different modes and communication styles.

"GPT-5.1 is out! It’s a nice upgrade. I particularly like the improvements in instruction following, and the adaptive thinking. The intelligence and style improvements are good too,” posted Altman on X (formerly Twitter) on Thursday, adding, “Also, we’ve made it easier to customize ChatGPT. You can pick from presets (Default, Friendly, Efficient, Professional, Candid, or Quirky) or tune it yourself.”

OpenAI provided examples of the new models answering prompts and compared them to responses generated by the earlier GPT-5 model. For example, while answering a stressed-out user, GPT-5 offered relaxation tips while GPT-5.1 Instant addressed the user by name and empathised with what they had been going through in the recent past, before offering similar tips.

“For the first time, GPT‑5.1 Instant can use adaptive reasoning to decide when to think before responding to more challenging questions, resulting in more thorough and accurate answers, while still responding quickly,” said OpenAI in its blog post.

GPT-5.1 Thinking also used a similarly casual style of conversation when explaining a technical concept.

GPT‑5.1 Instant and Thinking have started rolling out to paid users (Pro, Plus, Go, Business plans) before coming to free and logged-out users. The rollout is happening gradually over the coming days, with OpenAI highlighting that it would give users sufficient notice to switch to a new model before removing an older one.

This was previously a sore point for the company when it released its GPT-5 model, with many users taking to social media to complain that they missed the older models that felt “warmer” and more “friendly.” Others were upset by a sudden upgrade in models, complaining that they did not have enough time to transfer their projects or adjust their workflow.

Altman acknowledged the criticism but flagged the often deep emotional attachments that many ChatGPT users had to specific AI models.

“GPT‑5 (Instant and Thinking) will remain available in ChatGPT under the legacy models dropdown for paid subscribers for three months, so people have time to compare and adapt at their own pace,” said the company.

Ref

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From Star Trek to Quantum Reality -- How Uncertainty Fuels Discovery

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Image generated using ChatGPT for illustration purpose

By Dr. David Awschalom

When I look out and see students in the audience, I can’t help but think back to my own student days.
And yes — I, too, was once a student.

But unlike many of you, I struggled a bit during my first semester in college. Not because of the coursework — but because I discovered something far more captivating: television.

Growing up, my parents kept a tight leash on screen time. But suddenly, I had freedom — and that’s when I stumbled upon reruns of the original Star Trek.

It was more than a show. It was a revelation.

Led by a courageous captain and a hyper-logical science officer, the Starship Enterprise wasn’t just exploring space — it was exploring possibility. Their tools — communicators, holograms, universal translators — looked like science fiction back then. Today, they’re everyday reality (well, except for teleporters… we’re working on it).

What fascinated me most wasn’t the technology. It was the mindset.
The embrace of uncertainty.

While our brains are wired to avoid the unknown — to fear ambiguity — the crew of the Enterprise ran toward it.
And somehow, that spoke to me deeply. I didn’t have the words for it back then, but I found uncertainty exciting. It represented potential.

Years later, I’d come to realize that uncertainty is not just the foundation of science — it’s the foundation of quantum physics.

---

1969: The Year the World Changed

The final episode of Star Trek aired in 1969 — a year that changed everything.

The Beatles gave their last concert on the rooftop of Apple Records.
The Boeing 747 took its first flight.
Hundreds of thousands gathered at Woodstock.
And Neil Armstrong and Buzz Aldrin walked on the Moon.

But another, quieter event that same year would reshape the world in ways no one could imagine.

A group of engineers, funded by ARPA, linked two computers — one in Los Angeles and one in Palo Alto. They typed “LO”… and the system crashed.

An hour later, they tried again.
This time, they succeeded: “LOGIN.”
The first word ever sent across what would become the internet.

That small, failed experiment changed everything.

Today, over 5 billion people are online. 25 billion devices are connected — more devices than humans on the planet. That one crash in 1969 became the first spark of a global transformation.

---

When Failure Leads to Revolution

The internet wasn’t the only technology dismissed early on.
When the laser was first proposed, leading scientists — even Nobel laureates — called it “impractical.”
The paper was rejected by major journals.

And yet today, lasers are everywhere: in surgery, grocery scanners, communications, and even space exploration.

Innovation thrives in uncertainty.
Failure is often the first step toward transformation.

---

The Quantum Leap: Exploring Inner Space

Today, we stand at another such frontier — not in outer space, but in inner space.
The world of atoms, electrons, and photons.

At the quantum scale, nature behaves in ways that defy intuition.

In our everyday digital world, information is binary — zero or one.
But in the quantum world, information exists as a superposition — an infinite combination of zero and one.

Think of it as moving from black-and-white to full color.
A world of probabilities and entanglements — where measuring one particle can instantly affect another, even miles apart.

It sounds like science fiction.
But for the first time in history, we can create, control, and engineer quantum behavior at the human scale.

---

From Steel to Quantum: Chicago’s Bold Bet

Here in Illinois, about 50 miles south of Chicago, scientists are building quantum computers atom by atom using focused lasers.

They’re developing single-atom memories capable of storing billions of bits of data in a space smaller than a grain of sand.
Others are using quantum particles to detect disease within living cells — enabling early diagnostics far beyond what MRI can achieve.

And across the Midwest, we’re building entangled quantum networks, laying hundreds of miles of fiber to connect quantum computers and sensors — forming the backbone of a future quantum internet.

It’s happening faster than most can keep up.
As one researcher put it, “We’re driving 100 miles an hour in the fog — and building the road as we go.”

---

Quantum in Everyday Life

So how will this change your life?

Imagine airports like O’Hare.
Quantum algorithms could optimize the routing of thousands of planes and gates in real time — problems too complex for classical computers.

Quantum encryption could make our financial transactions unhackable.
Quantum sensors could safeguard pilots from GPS spoofing.
From transportation to healthcare to cybersecurity — quantum technology will touch every corner of our lives.

---

The Race for Quantum Leadership

This is a once-in-a-generation moment for Chicago — and for the world.

The U.S. passed the National Quantum Initiative Act, launching 10 national centers — four of which are based here in Illinois.
The state is investing hundreds of millions in labs and the Illinois Quantum & Microelectronics Park, transforming the old U.S. Steel site into a hub of the future.

From steel to quantum — thinking big to think small.

But this isn’t just a competition between labs or countries.
It’s about people.

Over the next decade, we’ll need more than 800,000 quantum engineers — and 70% of these roles will be filled by those with associate or undergraduate degrees.

Our community college system is our greatest asset in building this quantum workforce.
As one executive told me, “The last thing we need is more people like you.”
(He meant professors, by the way — not that I took it personally.)

---

The Final Frontier

Mark Twain once said, “History doesn’t repeat itself, but it often rhymes.”
Just like past revolutions — from the laser to the internet — global collaboration will be key.

We must attract brilliant minds, nurture them, and build together.

Because what’s happening now isn’t just the next step in science — it’s the beginning of a new era.

Quantum teleportation already allows us to transmit atomic information across miles — not people yet, but the principle is the same.

The world once imagined by Star Trek is no longer fiction.

And here in Chicago, we stand ready — engineers, dreamers, and explorers — to boldly go where no one has gone before.

---

Author’s Note:
Dr. David Awschalom is a professor of spintronics and quantum information at the University of Chicago and Director of the Chicago Quantum Exchange.

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The Week AI Changed Science Forever -- Launch of AI Researcher and AI Data Scientist

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In one breathtaking week, three announcements signaled a new era for artificial intelligence — and for humanity itself.

Microsoft unveiled Kosmos, an autonomous AI scientist that works 12-hour research shifts and actually makes real scientific discoveries.
At the same time, Microsoft’s AI chief Mustafa Suleyman revealed plans for a Humanist Super Intelligence, designed not to replace humans, but to serve them.
Google quietly dropped DS Star, an autonomous data scientist that writes, tests, and fixes its own Python code.
And from across the globe, China’s Moonshot AI launched Kimi K2 Thinking, an open source reasoning model that can plan and think across hundreds of steps.

All of this — in just a few days.
Let’s unpack what it means.

---

🌌 Microsoft’s Kosmos: The AI That Actually Does Science

Meet Kosmos, the project shaking up research labs everywhere.

Backed by Microsoft Research, Kosmos is the first AI scientist that conducts research from start to finish — autonomously.
You give it a dataset and a goal (say, analyzing brain scans or studying new materials), and it goes into a 12-hour deep dive.

In that time, it:

• Reads 1,500+ research papers

• Writes ~40,000 lines of Python code

• Runs analyses and tests hypotheses

• Produces a full research report with citations and executable code

No human steering it midway — just pure autonomous science.

And the results? Stunning.
Kosmos has already made new discoveries in biology, neuroscience, and clean energy:

• It revealed how cooling protects the brain by triggering an energy-saving mode in neurons.

• It discovered that high humidity destroys perovskite solar cells during manufacturing — later confirmed by human scientists.

• It even found a shared wiring rule across species — from flies to humans — suggesting all brains might follow the same mathematical pattern.

That’s not all. Kosmos identified a heart-protecting protein (SOD2), a diabetes-resisting DNA variant, and mapped the exact moment neurons collapse in Alzheimer’s disease.

How Kosmos Works

Kosmos runs on a swarm of AI agents, each with a specific role — paper reading, data analysis, coding, and hypothesis testing — all linked by a shared World Model, a collective memory that tracks context and progress.

Think of it as a brain made of sub-brains, coordinating long, multi-step scientific investigations.

In independent reviews, 80% of Kosmos’ findings were scientifically accurate — a staggering rate for a fully autonomous system.
One 12-hour Kosmos run produced the equivalent of six months of human research output.

Still, Kosmos isn’t perfect. It struggles with messy datasets and can’t yet process files larger than 5GB. And it can’t change course mid-run — once it starts, it commits.
But the biggest challenge? Judgment. Teaching an AI to know which discoveries matter.

Even so, this marks a historic moment: AI is now conducting real, verifiable research.

---

🤝 Microsoft’s Humanist Super Intelligence

While Kosmos pushes the boundaries of AI research, Microsoft’s Mustafa Suleyman is charting a different path — toward Humanist Super Intelligence (HSI).

This isn’t about building an AGI that replaces humans.
It’s about creating a super-intelligent system that serves them.

Suleyman describes it as a bounded, values-driven AI, designed to stay contextual, controllable, and subordinate.
A kind of deeply integrated AI companion — one that helps people learn, create, and think more clearly, while remaining ethically constrained.

Microsoft’s approach contrasts sharply with OpenAI and Anthropic’s open-ended AGI ambitions.
In Suleyman’s words: “Humans matter more than AI.”

With Microsoft now legally able to develop AGI independently using OpenAI’s IP, this philosophical divide could soon define the next great AI rivalry.

---

🧠 Moonshot AI’s Kimi K2: The Reasoning Machine

Meanwhile, in China, Moonshot AI is taking open source reasoning to a new level.

Their new model, Kimi K2 Thinking, doesn’t just generate text — it thinks, plans, and executes code across hundreds of reasoning steps without human help.

It scored:

• 40.9% on Humanity’s Last Exam (expert-level interdisciplinary benchmark)

• 60.2% on BrowseComp (research and browsing tasks) — double the human average

• 71.3% on SWE Bench Verified (software engineering benchmark)

That’s not just incremental progress — it’s a leap.

In one demo, K2 solved a PhD-level hyperbolic geometry problem, performing 23 nested reasoning loops, running code, and verifying results until it derived the correct formula.

In another, it identified an actor from a vague description — parsing 20+ web sources, combining biographical clues, and assembling the answer.

This ability to reason across long horizons — chaining 300+ tool calls — represents a new frontier in AI.
Moonshot’s bet is that open source reasoning can rival (or even surpass) proprietary Western models.

---

🧩 Google’s DS Star: The Autonomous Data Scientist

Then there’s Google.

Their new system, DS Star, might quietly revolutionize enterprise analytics.
If Kosmos is an AI researcher, DS Star is an AI data scientist that turns messy real-world data into clean Python insights — all by itself.

Most AI tools require clean SQL databases. DS Star? It thrives in chaos:
CSVs, JSON logs, random spreadsheets, unstructured reports — bring it on.

You can ask it a question like:

“Which products performed best in Q3 based on sales and reviews?”

And DS Star will:

1. Find the relevant files

2. Write and test the Python code

3. Debug its own errors

4. Return the correct analysis

It uses a six-agent loop — one reads data, another plans, another codes, a verifier checks, a router fixes issues, and a finalizer packages the output.

If the code fails, it repairs itself automatically by studying the logs.

Powered by Gemini 2.5 Pro, DS Star outperforms every other data reasoning system on major benchmarks — including a 30-point leap on Dabstep, a benchmark for real-world data analysis.

Even more impressive, it’s model-agnostic — meaning the same architecture could work with GPT-5 or Claude 4.5.

In essence, AI no longer just assists the analyst — it is the analyst.

---

⚙️ The New AI Frontier: Long-Horizon Thinking

The thread connecting Kosmos, K2, and DS Star is clear:
AI systems are evolving from reactive assistants into autonomous thinkers.

They plan, code, reason, verify, and self-correct — traits once thought uniquely human.

The next frontier won’t be about larger models.
It’ll be about how long and coherently an AI can think before it loses focus — what researchers now call test-time scaling.

That’s the new battleground for AI supremacy.

---

🚀 The Takeaway

In just one week, we’ve seen:

• Microsoft prove that AI can do real science

• Google show that AI can analyze messy data autonomously

• China demonstrate that open-source reasoning can rival the world’s best

This isn’t hype anymore — it’s happening.
AI isn’t just assisting human intelligence; it’s beginning to extend it.

We’re entering the era where AI doesn’t just help the process — it is the process.

Wild times, indeed.

---

What do you think — should AI be trusted to conduct science independently?
Drop your thoughts in the comments below.

If you enjoyed this deep dive, share it — and follow for more explorations at the edge of AI and human creativity.

Addendum

What is Microsoft Kosmos?

Microsoft Kosmos (Knowledge-based Operating System for Modeling Scientific knowledge) refers to a series of multimodal large language models (MLLMs) developed by Microsoft Research and, in a related but distinct effort, an AI system developed by Edison Scientific designed for scientific research. 

Microsoft Research Kosmos Series

These models are designed to understand and process information from multiple modalities, including language, images, and potentially audio, enabling capabilities beyond traditional text-only models. 

Kosmos-1: The foundational model, introduced by Microsoft Research, can perceive images and language, perform in-context learning, reason, and generate content. It handles tasks like visual question answering (VQA), image captioning, and Optical Character Recognition (OCR)-free text processing.

Kosmos-2: Building on Kosmos-1, this model introduced the ability of multimodal grounding and referring. It can link specific text spans (like noun phrases) in a caption directly to corresponding regions (using bounding boxes) within an image, essentially creating "invisible hyperlinks" between text and pixels. This allows for more precise human-AI interaction and visual responses.

Kosmos-2.5: This version is a "multimodal literate model" specifically designed for machine reading and understanding of text-intensive images such as academic papers, receipts, and web pages. It excels at generating spatially-aware text blocks (with coordinates) and structured text in markdown format, performing on par with larger models like GPT-4o on document understanding benchmarks. 

Edison Scientific Kosmos AI System

A separate, recent development, this system is described as an "AI scientist" designed for deep scientific research workloads, not general chat. 
It operates using "structured world models" and runs hundreds of smaller AI agents in sync.
It can ingest thousands of papers and data sets to perform complex analyses, generate hypotheses, and produce traceable reports with citations and code references with high accuracy. 

Source: Gemini

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YouTube Academy For Machine Learning

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What is Machine Learning?

1. Machine Learning Crash Course: Intro & What's New
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2. What is ML?
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3. Intro to Machine Learning (ML Zero to Hero - Part 1)
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4. Machine Learning Foundations: Ep #1 - What is ML?
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1. Introduction to ML on Android with MediaPipe
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1. Supervised Machine Learning
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1. Machine Learning FOR BEGINNERS - Supervised, Unsupervised and Reinforcement Learning
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1. Machine Learning Crash Course: Generalization
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1. Unit 5: Exploring bivariate numerical data
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1. Hello World - Machine Learning Recipes #1
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1. Machine Learning Crash Course: Logistic Regression
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2. Lec-5: Logistic Regression with Simplest & Easiest Example | Machine Learning
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1. Decision Tree: Important things to know
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1. Support Vector Machines: All you need to know!
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2. Support Vector Machines (SVM) - the basics | simply explained
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1. Machine Learning Crash Course: Gradient Descent
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3. Gradient descent, how neural networks learn | Deep Learning Chapter 2
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Model Alert... Alibaba-backed Moonshot releases its second AI update in four months as China's AI race heats up (Nov 2025)

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• Beijing-based startup Moonshot released a new AI model Thursday just four months after its prior update.
• Major U.S. companies such as Airbnb have begun to publicly tout how some Chinese AI models as viable — and often cheaper — alternatives to OpenAI’s.
• The new Kimi AI model cost $4.6 million to train, according to a source familiar with the matter.

Chinese startup Moonshot on Thursday released its latest generative artificial intelligence model which claims to beat OpenAI’s ChatGPT in “agentic” capabilities — or understanding what a user wants without explicit step-by-step instructions.

The model, called “Kimi K2 Thinking,” builds on the K2 model released in July by Beijing-based Moonshot, which is backed by Alibaba.

The update comes as Nvidia CEO Jensen Huang this week again urged the U.S. to press ahead in a race against Chinese-developed AI. Some major U.S. companies such as Airbnb have begun to publicly tout how some Chinese AI models are as viable — and often cheaper — alternatives to OpenAI’s.

Despite U.S. restrictions on Chinese businesses’ access to high-end chips, companies such as DeepSeek have released AI models that are open sourced and with user fees a fraction of ChatGPT’s.

DeepSeek also claimed it spent $5.6 million for its V3 model — in contrast to the billions spent by OpenAI.

The Kimi K2 Thinking model cost $4.6 million to train, according to a source familiar with the matter.

It can automatically select 200 to 300 tools to complete tasks on its own, reducing the need for human intervention according to Moonshot. CNBC was unable to independently verify the DeepSeek or Kimi figures.
DeepSeek last month released a new AI model that claims to improve performance by using visual clues to expand the context of information it is processing at once.

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Model Alert... Open-Weights Coding Leader

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An open-weights model from Shanghai-based MiniMax challenges top proprietary models on key benchmarks for coding and agentic tasks.

 

What’s new: MiniMax, which provides voice-chat and image-generation services, released the weights for MiniMax-M2, a large language model that’s optimized for coding and agentic tasks.

• Input/output: Text in (up to 204,000 tokens), text out (up to 131,000 tokens, roughly 100 tokens per second)
• Architecture: Mixture-of-experts transformer, 230 billion parameters total, 10 billion parameters active per token
• Performance: First among open weights models on Artificial Analysis’ Intelligence Index
• Availability: Weights free to download from Hugging Face and ModelScope for commercial and noncommercial uses under MIT license, API $0.30/$1.20 per million input/output tokens via MiniMax
• Undisclosed: Training data, specific training methods

How it works: MiniMax has not published a technical report on MiniMax-M2, so little public information is available about how it built the model.

• Given a prompt, MiniMax-M2 interleaves reasoning steps (enclosed within <think>...</think> tags) within its output. This differs from models like DeepSeek-R1 that generate a block of reasoning steps prior to final output. It also differs from models like OpenAI GPT-5 and recent Anthropic Claude models that also generate reasoning steps prior to final output but hide or summarize them.
• MiniMax advises users to retain <think>...</think> tags in their conversation histories for optimal performance across multiple turns, because removing them (say, to economize on tokens) would degrade the model’s context.

Results: MiniMax-M2 achieved 61 on independent evaluator Artificial Analysis’ Intelligence Index (a weighted average of benchmark performance in mathematics, science, reasoning, and coding), a new high for open weights models, ahead of DeepSeek-V3.2 (57 points) and Kimi K2 (50 points). It trails proprietary models GPT-5 with thinking enabled (69 points) and Claude Sonnet 4.5 (63 points). Beyond that, it excelled in coding and agentic tasks but proved notably verbose. It consumed 120 million tokens to complete Artificial Analysis evaluations, tied for highest with Grok 4.

• On τ²-Bench, a test of agentic tool use, MiniMax-M2 (77.2 percent) ranked ahead of GLM-4.6 (75.9 percent) and Kimi K2 (70.3 percent) but behind Claude Sonnet 4.5 (84.7 percent) and GPT-5 with thinking enabled (80.1 percent).
• On IFBench, which tests the ability to follow instructions, MiniMax-M2 (72 percent) significantly outperformed Claude Sonnet 4.5 (57 percent) but narrowly trailed GPT-5 with thinking enabled (73 percent).
• On SWE-bench Verified, which evaluates software engineering tasks that require multi-file edits and test validation, MiniMax-M2 (69.4 percent) ranked in the middle tier ahead of Gemini 2.5 Pro (63.8 percent) and DeepSeek-V3.2 (67.8 percent) but behind Claude Sonnet 4.5 (77.2 percent) and GPT-5 with thinking enabled (74.9 percent).
• On Terminal-Bench, which measures command-line task execution, MiniMax-M2 (46.3 percent) ranked second only to Claude Sonnet 4.5 (50 percent), significantly ahead of Kimi K2 (44.5 percent), GPT-5 with thinking enabled (43.8 percent), and DeepSeek-V3.2 (37.7 percent).

Behind the news: In June, MiniMax published weights for MiniMax-M1, a reasoning model designed to support agentic workflows over long contexts (1 million tokens). The company had been developing agents for internal use in tasks like coding, processing user feedback, and screening resumes. However, it found that leading closed-weights models were too costly and slow, while open-weights alternatives were less capable. It says it built MiniMax-M2 to fill the gap.

 

Why it matters: Developing reliable agentic applications requires experimenting with combinations and permutations of prompts, tools, and task decompositions, which generates lots of tokens. Cost-effective models that are capable of agentic tasks, like MiniMax-M2, can help more small teams innovate with agents.

 

We’re thinking: MiniMax-M2’s visible reasoning traces make its decisions more auditable than models that hide or summarize their reasoning steps. As agents are applied increasingly to mission-critical applications, transparency in reasoning may matter as much as raw performance.

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