MiniMax's M3.1 Flash sits ahead of several open-weight rivals on independent scores — at half its claimed speed, and still without weights or a license

The launch arrived with a masked model and left with an argument. MiniMax's new agent model now sits ahead of several open-weight rivals on independent scoring — at roughly half the speed the company advertised, with no weights in sight and no license named.

For well over a week, an anonymous model ran on OpenRouter under the name "Space Bunny." Nobody knew whose it was. The community tallied more than 200 trillion tokens of traffic and watched it climb toward the top of global leaderboards. On October 19, MiniMax stepped forward and admitted the mask was its own: an early build of M3.1 Flash, the company's new code-and-agent model [1][2][15]. Since then it has been live through the MiniMax API, the M Plan subscription and the MiniMax Code app [1][9]. The weights are nowhere to be found. The company says they "will be released later" and has not uttered a word about licensing [1][7]. Any of that would make a serviceable launch post. The interesting part is the shape of the debut: a closed-weight model walked on stage at the exact moment it was about to overtake the open-weight league on independent scoring — then tethered its first week of access to a cloud promotion.

1. The lab behind the mask

MiniMax is a publicly listed Chinese company (HKEX: 00100), and the playbook here is a familiar one: release the model anonymously, let curiosity compound, then drop the official blog post. OpenRouter's community spent weeks guessing who "Space Bunny" belonged to; when the announcement landed, the surprise itself became the press release [1][8]. The same day, MiniMax's X account celebrated the model shipping with "Day0" support on Ollama [8]. That pairing isn't new either — M2 already joined Ollama's cloud back in October 2025 [6].

On the numbers side, the company is positioning the whole "Flash" segment from scratch. The old M3 sat at 29 points on Artificial Analysis' Intelligence Index, costing about $0.51 per task; M3.1 Flash lands at 43 points and roughly $0.22 [11]. MiniMax's "half the price, three times the speed" pitch rests entirely on the distance between those two rows — and for now it is the company's own claim; nobody outside has reproduced it [1][15].

The company's benchmark sheet falls in the same category: 62.2 on Automation-Bench, 73.7 on DeepSWE, 76.2 on BankerToolBench. Then there is Terminal Bench 4.0, where it drops to 38.1 against Opus 5's 51.8 — the quiet corner of that table [1][12][13]. Worth remembering that all of these arrive from a marketing document. The independent table below keeps a different set of books.

2. One model, two speed numbers, one honest read

Speed is the messiest line of this launch. MiniMax's blog post cites a peak above 200 TPS and a P50 north of 170 [1] — measured on the company's own infrastructure, by the company's own instruments. Artificial Analysis, an independent evaluator, had output speed at roughly 93 tokens per second on the evening of October 21, against a class median of 113 [17]. That is about half the advertised rate, sitting below the middle of the pack. (Housekeeping note: the retrievable snapshot of AA's model page is frozen at an older speed value, so the current number comes from its comparison page [10][17]. These measurements drift; this article says "~93 tok/s, accessed Oct 21" and leaves it there.)

The context window carries a similar marketing-versus-practice gap. MiniMax's documentation and Artificial Analysis both cite 1,000,000 tokens [7][10]; Ollama lists the actually usable ceiling at 976K [3]. "1M context" is the product name. 976K is the number left in your account.

Here is the model's pedigree as far as we can actually establish it. Ollama's page shows 428B parameters [3]. That it is a mixture-of-experts architecture with roughly 23B active parameters, sparse attention and a built-in vision encoder comes from MarkTechPost's reporting — MiniMax has published no technical report, so we cannot verify it [12]. Text and image inputs are safe claims; the docs also mention video, but no data shows video working at the Ollama end [3][7][10]. Effort spans five levels from low to max, and reasoning stays on throughout [7]. Integrations written against the old M3 will trip immediately: calls that set thinking.type=disabled or effort=none now return errors, and reasoning text arrives in a separate reasoning_content field — anyone migrating meets this break on the very first call [12].

3. The scorecard: cheap, not fast, and not exactly trustworthy

The class photo, with Artificial Analysis data accessed October 21:

Model	Intelligence Index	Cost per task	Notes
MiniMax M3.1 Flash (max)	43 (42 at launch)	~$0.22	4/173; 48% HLE, 22% CritPt; 93 tok/s, below median
GLM-5.3-Flash (max)	42	~$0.28	Neck and neck with M3.1 in launch analysis
DeepSeek V4.1 Flash 1017 (max)	45	~$0.26	Class leader on AA; MIT-licensed open weights
Gemini 3.8 Flash (high)	41	~$1.24	Roughly 5.6x M3.1's task cost
MiniMax M3 (previous gen)	29	~$0.51	The generation before
Class median	13	—	—

Table: Artificial Analysis model and comparison pages plus launch analysis [10][11][17] · accessed Oct 21, 2026.

The rows need reading between the lines. The GLM entry is GLM-5.3-Flash (42 points, ~$0.28); the similarly named "GLM-5.3 (Max)" is a different, much bigger model that charges $2.01 per task [11][17]. DeepSeek V4.1 Flash both leads the class and ships MIT-licensed — you can pull the weights and run them on your own metal [11][12]. M3.1 Flash's 43 is genuinely impressive; the price for it is standing in a cloud service's queue at $0.15 per million input tokens and $0.60 per million output [1][3][7].

Then there is the hallucination metric nobody is advertising. On AA-Omniscience, M3.1 Flash attempts 98% of questions and stays 56% correct; among the ones it does not know, it answers wrongly rather than saying "I don't know" in 93% of cases (overall score 16) [11]. As VentureBeat also points out, that does not mean "93% of its answers are wrong" [18]. What it does mean: the model fabricates where it is unsure. If your agent chains cannot afford a silently wrong link, building the verification layer is your job, not the model's.

4. The marketing calendar arrived before the licensing desk

Why cloud-first rather than open weights? No official answer exists; the competitive table tells its own story. DeepSeek publishes under MIT while MiniMax defers its weights to "later" and opens its first week as a three-day free campaign on Ollama's cloud [1][11]. Ollama's model page reads "free until Oct 22, 2026 11:59 PM UTC" — 02:59 Turkey time on October 23 — after which normal metered pricing resumes [3][5]. Not a single extra condition, no quota, no terms, is printed anywhere for the campaign [3][5]. A small note for the record: Ollama's blog carries exactly zero posts about this launch. The entire announcement consists of a model page and two X posts [4][5][6].

"Model weights will be released later." — MiniMax official blog announcement, Oct 19, 2026 [1]

The journalistic weight of that detail fits in one sentence: the free window is a marketing budget, not the model. The actual argument about this model lives in the licensing gap. MiniMax's platform documentation calls the model "open-weight" [7]; Venice's registry lists it as closed/proprietary [13]. Both cannot be true, and no official source will name a license. The previous M3 shipped under a Community License requiring attribution and written permission from anyone earning above $20 million a year; what governs M3.1 has not been stated [13][14]. On Ollama the only tag is :cloud, and the model runs on Ollama's US infrastructure; "zero data retention" stands as Ollama's policy statement [3][4]. "Run it locally" is simply not a sentence you can say today.

What you can responsibly do today is a measured test. At the time of writing, the free window has roughly a day left (closing October 23, 02:59 TRT). A sensible sequence: run one end-to-end coding task through ollama run minimax-m3.1-flash:cloud, then wire it into Claude Code with ollama launch claude --model minimax-m3.1-flash:cloud and hand it a PR-sized job in a real repository [3][4]. Run the same tasks against MIT-licensed DeepSeek V4.1 Flash for comparison. Watch the terminal-heavy jobs and the factual questions in particular — those are the two known weak spots [11][12]. A weight release is expected around October 23; if it lands, the license text should be the first thing you read [13][15].

Murat Karakaya Academy verdict

What the data says: an Intelligence Index of 43 at ~$0.22 per task is a very low price for performance more than triple the class median of 13 — and that is independent measurement speaking [10][11]. The same data draws an equally clear boundary: 56% factual accuracy and a habit of fabricating in 93% of the questions it doesn't know rule this model out of any workflow that lacks verification [11][18]. The company's own benchmark sheet is a bundle of claims with no independent replication; telling the two apart is hard but possible, and we have kept them apart throughout this piece.

Our opinion: this launch is a story about distribution, not model competition. A lab marketed its strongest code model through a masked stealth run, keeps its weights locked, and tied first-week access to a cloud partner's free campaign — functionally neutralizing, for the first week, the run-it-locally advantage that gives the open-weight community any leverage at all. Until a license text exists, this model is a product, not infrastructure. Our advice to readers: blind-test it on a free day, calibrate your impressions against DeepSeek, whose numbers are measurable and whose license is clean, and make the production decision once the weights and the license actually surface.

References

1. minimax.io — "MiniMax M3.1 Flash: Redefining Agent Performance and Cost" (Oct 19, 2026) · accessed Oct 21, 2026
2. ir.minimax.io — "New Releases" announcement list (Oct 19, 2026) · accessed Oct 21, 2026
3. ollama.com — "minimax-m3.1-flash" model page (428B, 976K ctx, pricing, free-until date, commands) · accessed Oct 21, 2026
4. x.com/@ollama — Official launch post (Oct 19, 2026; 1M ctx, US-hosted, zero data retention) · accessed Oct 21, 2026
5. x.com/@ollama — "3 days free on Ollama's Cloud" (Oct 20, 2026) · accessed Oct 21, 2026
6. ollama.com/blog — Blog archive (Oct 2025 M2 post; absence of any M3.1 post) · accessed Oct 21, 2026
7. platform.minimax.io — Text generation documentation (1M ctx, effort levels, "open-weight" phrasing) · accessed Oct 21, 2026
8. x.com/@MiniMax_AI — Day0 Ollama support + Space Bunny confirmation (Oct 19, 2026) · accessed Oct 21, 2026
9. agent.minimax.io — MiniMax Agent product page (M3.1 Flash GA announcement) · accessed Oct 21, 2026
10. artificialanalysis.ai — "MiniMax-M3.1-Flash" model page (Index 43, 4/173, ~$0.22/task, 1M ctx; stale cached speed value — current speed per [17]) · accessed Oct 21, 2026
11. x.com/@ArtificialAnlys — Launch benchmark analysis (48% HLE, 22% CritPt, AA-Omniscience: 98% attempt rate, 93% wrong answers among unknowns, overall score 16) · accessed Oct 21, 2026
12. marktechpost.com — "MiniMax M3.1 Flash" (428B MoE, ~23B active claim, benchmark table, migration notes; Oct 19, 2026) · accessed Oct 21, 2026
13. pc.watch.impress.co.jp — Space Bunny reveal, license analysis, M3 Community License terms · accessed Oct 21, 2026
14. faradaymachines.com — License gap and cost-per-task analysis · accessed Oct 21, 2026
15. unite.ai — "MiniMax Launches M3.1 Flash, Revealing Space Bunny Stealth Model" ("topped global leaderboards" phrasing) · accessed Oct 21, 2026
16. daily.dev — Roundup of the free week and the mixed benchmark picture · accessed Oct 21, 2026
17. artificialanalysis.ai — "M3.1-Flash vs GLM-5.3 (Max)" comparison page (live output speed; GLM-5.3 (Max) distinct from the Flash variant) · accessed Oct 21, 2026
18. venturebeat.com — "MiniMax's new M3.1 Flash beats DeepSeek and Opus 5 on a key business benchmark at lower cost, but with a high hallucination rate" (hallucination metric definition) · accessed Oct 21, 2026

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Labels: Murat Karakaya Academy, MiniMax M3.1 Flash, Ollama, AI, Open Weights

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Murat Karakaya Academy · muratkarakaya.net

The "Early Specialization" Fallacy in AI Careers: Why New Grads Should Embrace a Broad Profile

Recently, I received an email from a highly talented senior student whose portfolio I reviewed. It perfectly summarized the immense anxiety felt by almost everyone stepping into today's artificial intelligence ecosystem. She wrote:

"I want to get into the AI field. I've experimented with a lot of different things—local LLM integration, RAG (Retrieval-Augmented Generation), Edge AI projects... But when I look at my CV, I feel scattered. Should I narrow my portfolio and specialize in a single area before getting a job?"

This question is the clearest example of a fallacy inherited from traditional software engineering teachings hitting a brick wall in the "Age of AI". In the traditional world, the "T-shaped person" (deep in one area, broad in others) was the ideal. But when it comes to AI, the rules of the game change entirely.

The Illusion of "Maturity" Destroyed by Speed

Almost all the topics we lose sleep over today, wondering "should I specialize in this?" (Transformer architectures, Agentic RAG systems, vector databases, SLMs), are products of the last 5 to 6 years. More importantly, none of these technologies are fully mature yet.

Right now, the lifespan of a technique in the industry is measured in months. Yesterday, Prompt Engineering was an area of expertise; today, libraries like DSPy make it algorithmic. Yesterday, massive cloud models were essential; today, 8-billion parameter models running locally via Apple MLX or Ollama work wonders.

The morning after you declare yourself an "expert" in a framework or specific architecture, a brand new paper or open-source code is published that renders that architecture completely obsolete. In such a hyper-dynamic landscape, someone without industry experience making a decision for premature specialization is essentially cutting off the branch they are sitting on.

The True Expectation of Startups and Companies

When you enter the industry's kitchen, you see that reality is much more pragmatic. Most companies and agile startups aren't looking for laboratory perfection. They are looking for profiles that can integrate these rapidly changing new tools, models, and libraries into existing business processes without wasting time.

Therefore, employers prefer a "technology integrator"—someone who has dirtied their hands with RAG systems, tried running models on edge devices (Edge AI) when necessary, and experienced those constraints firsthand—over a "narrow specialist" who has academically deepened in a single algorithm but remains blind to the rest of the system. That broad spectrum you see as being "scattered" is actually proof of your “adaptation muscle,” which is your biggest competitive advantage.

Where Does True Depth Begin?

So, will there be no specialization at all? Of course there will be. But not through "choices" made from the outside before getting a job.

True specialization begins when you sit at the desk. You deepen when you try to build a RAG system on your company's 50 million lines of dirty corporate data and your system starts "hallucinating." You specialize in that area when you are forced into model quantization to increase inference speed. The needs of the project naturally and inevitably pull you into a deep well in a specific area. Sometimes this focus comes at the cost of staying away from other hot trends, but the depth you acquire is now a "useful" depth; not a hypothetical one.

My Advice to New Graduates

In summary; do not fear the diversity in your portfolio. Instead of becoming a disciple of a single topic in the AI field, build plenty of prototypes with different techniques. Make mistakes, see the limits. Master the breadth of the ecosystem so that tomorrow, when a problem arises at the company you work for, you have the vision to say, "It would be more effective if we solved this with that newly released agent architecture, rather than LangChain."

Let the epaulet called specialization settle on your shoulders naturally while you are wrestling with real-world problems.

What do you think?

How is this dizzying speed in the AI ecosystem affecting your career planning? Specialize in a narrow field, or become an integrator with a broad perspective? Let's discuss your ideas in the comments.

#ArtificialIntelligence #CareerStrategy #NewGrad #SoftwareDevelopment #MachineLearning #LLM #RAG #EdgeAI #TechCareer #SoftwareEngineering #FutureOfWork #MuratKarakayaAkademi

Artificial Intelligence • R&D • Project Management

Artificial Intelligence • R&D • Project Management

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🚨 Why Do AI Projects Fail?

Project proposals are becoming increasingly impressive. But can we say the same about the engineering capacity of the teams expected to turn those proposals into working systems?

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Recently, I have been noticing the same problem more and more often during project evaluations.

When you start reading an AI-related project proposal, you often see almost every recent method, technique, and technology somehow included in the project:

Large language models, RAG, AI agents, vector databases, personalization, fine-tuning, multi-agent architectures, real-time analytics, prediction systems, decision support, explainable AI...

On paper, the project looks highly impressive.

However, once you meet the team face to face and begin asking more detailed questions, a very different picture often emerges.

👥 The technology is in the proposal — but is the experience in the team?

Sometimes, the engineers on the project team have never developed even a small-scale application using the technologies described in the proposal.

The company itself may also have no prior experience building, deploying, operating, or maintaining products based on those methods.

After a few questions, the following issues begin to surface:

• Why was this technology selected?
• What real problem does it solve in the project?
• Why is it being used together with the other components?
• Were alternative methods evaluated?
• How will success be measured?
• If the system fails, how will the team determine which component failed and why?

Unfortunately, in some projects, there are no clear answers.

The technology choices appear to be less the result of an engineering analysis and more the result of connecting fashionable concepts to one another.

💡 The problem is not that too many technologies are being used. The problem is that the team cannot explain, from an engineering perspective, why each technology is actually necessary.

🧩 A list of technologies is not a system design

Today, it is relatively easy to produce impressive project documents with generative AI.

You can list the advantages of every technology. You can summarize their benefits from the literature. You can draw an architecture with a few boxes and arrows.

But none of this, by itself, constitutes systems engineering.

In a real system design, every component has a reason to exist.

A component should not be added because it is fashionable. It should be added because it satisfies a specific requirement.

For every selected method, the team should be able to explain:

• which requirement it addresses,
• which alternatives it was compared against,
• which metric it is expected to improve,
• which costs it introduces,
• and which new risks it adds to the system.

Otherwise, what we have is not a system architecture. It is merely a list of current technologies.

🔍 Saying “we will use RAG” is easy. Building a working RAG system is not.

For example, the following sentence has become very common in AI project proposals:

“A RAG-based artificial intelligence system will be developed to generate reliable answers from corporate documents.”

Fine.

But the real engineering problem begins exactly where that sentence ends.

📌 How will system performance be measured?

📌 How will the benchmark dataset be created?

📌 How will real user questions be represented in the evaluation set?

📌 How will retrieval quality be separated from final answer quality?

📌 How will chunk size and overlap be selected?

📌 Which experiment will determine the embedding model?

📌 Which metadata fields will distinguish text chunks from tens of thousands of documents?

📌 If multiple versions of the same document exist, which one will be used?

📌 How will obsolete or withdrawn information be removed from the system?

📌 How will unauthorized users be prevented from retrieving restricted content?

📌 How will hallucination be measured?

📌 How should the system behave when a reliable answer cannot be generated?

📌 How will latency be controlled under thousands of concurrent users?

📌 How will the trade-off between cost, accuracy, and response time be managed?

Every one of these is a real-world engineering problem.

Yet in many project documents, instead of answering these questions, the proposal simply explains the generic benefits of RAG.

That tells us that the project may be conceptually defined, but its operational reality has not been sufficiently considered.

⚠️ Using AI to prepare a project is not the same as developing an AI project

I believe a new risk has emerged in recent years.

Generative AI has made project preparation dramatically easier.

Literature reviews, technology descriptions, methodology sections, and technical documentation that once took weeks can now be produced much faster.

This is not inherently a bad development.

On the contrary, when used properly, it can create enormous productivity gains.

The problem begins when the process turns into something like this:

🤖 Ask AI to write the project proposal.

💰 Apply for funding.

🧑‍💻 If the funding is approved, ask AI to write the code too.

This mindset can easily turn into a dangerous form of shortcut-driven project development.

The engineering gap between a project proposal and a real system cannot automatically be closed simply by using more AI.

🧠 AI does not automatically create experience that the team does not have

Today’s generative AI systems can write code, suggest architectures, create APIs, generate test scenarios, and produce documentation.

But there is still a critical distinction:

AI can generate a solution for you. But determining whether that solution is actually the right solution for your problem still requires knowledge and experience.

Because real engineering is not simply about generating code.

Real engineering means:

• defining the right problem,
• identifying the requirements,
• comparing alternative approaches,
• defining measurable success criteria,
• anticipating bottlenecks,
• handling unexpected cases,
• testing the system under real user behavior,
• and considering scalability and sustainability.

📉 So what happens in the end?

A system that looked extremely strong in the project proposal fails to deliver the expected performance during implementation.

At first, a few demonstrations are prepared.

The system performs well on carefully selected examples.

But once real data, real users, and real operational conditions enter the picture, problems begin to appear.

Costs increase.

Latency rises.

Data quality problems emerge.

Authorization requirements become important.

The model begins producing unexpected answers.

Real user questions do not resemble the clean examples used in demonstrations.

And because there is no properly designed benchmark, the team may not even know which change actually improves the system.

As time passes, the project schedule slips.

The team changes.

Employees leave.

New team members do not understand why previous architectural decisions were made.

Eventually, the project is either scaled down, diverted from its original objective, or left behind as an unused prototype.

🎯 Perhaps we are asking the wrong question

When evaluating AI projects, we often focus on this question:

“Which technologies will be used in the project?”

I believe there is a more important question:

“Does this team actually have the engineering capacity to build the system described in the proposal?”

This should be followed by several additional questions:

• Has the team solved a similar problem before?
• Has it at least built a small-scale prototype?
• Does the team understand the technical risks?
• Does it know which assumptions may turn out to be wrong?
• Can it design experiments to measure success?
• Does it know how to validate the system under real user load?

🏗️ How should a good AI project begin?

In my view, the process should begin with the problem, not with technology names.

Wrong starting point	A better starting point
“Let’s use RAG.”	“Let’s understand which information users cannot access and why.”
“Let’s add agents.”	“Let’s identify which tasks genuinely require multi-step reasoning and decision-making.”
“Let’s personalize everything with an LLM.”	“Let’s measure which user information actually improves response quality.”
“Let’s use the most powerful model.”	“Let’s experimentally determine the right trade-off between quality, cost, and latency.”

🚀 A strong project proposal does not necessarily mean a strong project

Today, it is possible to prepare an excellent project proposal with the help of AI.

In fact, compared with just a few years ago, proposals can now be more professional, more technical, and far more persuasive.

But the quality of the proposal and the feasibility of the project are not the same thing.

AI can accelerate project preparation.

It can accelerate software development.

It can simplify documentation.

It can generate test scenarios.

It can suggest architectural alternatives.

But it does not magically create engineering experience, domain knowledge, systems thinking, or product development culture that the team does not already possess.

🎯 One of the biggest risks in AI projects may no longer be lack of access to technology. It may be the opposite: because access has become so easy, teams may begin highly ambitious projects without sufficient preparation or experience.

At the end of the day, we still need people who understand the problem, ask the right questions, design meaningful measurements, anticipate failure points, and architect the system from end to end.

💬 Have you also encountered AI projects that look extremely strong in project documentation but fail to translate into real-world systems?

#MuratKarakayaAkademi #ArtificialIntelligence #AI #GenerativeAI #LLM #RAG #AgenticAI #Engineering #SystemsEngineering #ProjectManagement #ResearchAndDevelopment #ProductDevelopment #AIProjects #Innovation #Technology