There are moments in the world of artificial intelligence when everything feels unusually still — when new releases trickle in slowly and you have enough time to digest each announcement before the next one lands. And then there are weeks like this, where the pace of news becomes almost cinematic, unfolding in real time with overlapping narratives, surprise model leaks, philosophical debates, and billion-dollar moves shaping themselves around one another.
This week belonged to the second category.
A week where OpenAI’s internal concerns collided with Anthropic’s rising momentum… where hints about a model named Garlic fueled speculation… where developers began whispering that Opus 4.5 might be the biggest coding leap they’ve ever seen… and where Mistral stepped into the arena with a new family of models trained under conditions that make their performance even more impressive.
Let’s walk through it slowly, with care — the way a story like this deserves to be understood.
🌱 1. OpenAI’s Code Red & The Emergence of “Garlic”
Every major shift begins with a ripple, and in this case, that ripple was the phrase “Code Red.”
Yesterday’s headlines focused heavily on OpenAI’s internal urgency to re-establish momentum after Google’s Gemini 3 reveal and an increasingly competitive AI landscape. But as the dust settled, a new piece of the puzzle emerged: a model whispered about inside OpenAI under the codename Garlic.
🌿 A new model enters the arena — quietly, then suddenly
According to sources quoted by The Information, Garlic represents more than just another internal checkpoint. Chief Research Officer Mark Chen reportedly told staff that Garlic was performing exceptionally well across internal benchmarking suites. And these weren’t lightweight comparisons — they included direct evaluations against highly regarded models like Gemini 3 Pro and Anthropic’s Opus 4.5.
The part that caught everyone’s attention wasn’t simply that Garlic was strong.
It was how it was strong.
Coding.
Reasoning.
Two of the most coveted benchmarks in modern AI development — and Garlic was reportedly surpassing OpenAI’s previous best, including the much larger GPT-4.5.
🌿 The context behind Garlic’s significance
For months, industry observers suspected that OpenAI had been dealing with a deeper issue: an inability to complete a full-scale pre-training run after the GPT-4o family. SemiAnalysis even stated this directly in a research note. If true, that would mean OpenAI’s primary engine of innovation — the training pipeline — had stalled.
Yet Garlic changes the tone of that conversation.
Chen suggested that Garlic incorporates bug fixes originally tested during the “Shallow Pete” training run. The implication is subtle but powerful:
👉 OpenAI has finally resolved the bottlenecks blocking its next-generation models.
👉 Large-scale pre-training may now be functioning smoothly again.
And if that is indeed the case, OpenAI’s roadmap for 2025 and early 2026 may look very different from the cautious tone analysts were expecting even a month ago.
🌿 When will Garlic appear?
Here is where narratives diverge.
Some insiders believe Garlic could be part of the release scheduled for next week — a piece of the Code Red puzzle intended to reset momentum immediately.
Others, like analyst Chris (@ChatGPT21), insist that Garlic is separate, slated for early 2026, possibly branded as GPT-5.2 or GPT-5.5.
According to these sources:
- The model releasing next week is a reasoning-oriented update.
- Garlic is the more heavily refined pre-trained model coming later.
- And the true next-generation foundation model, built on extensive innovation, is still in the oven.
In a field where naming conventions often obscure more than they reveal, the details matter less than the emotional temperature of the room: OpenAI feels behind — but maybe, just maybe, they’re catching up again.
🌊 2. Anthropic’s Opus 4.5 Takes the Crown in Developer Sentiment
If Garlic is the whisper in the background, then Opus 4.5 is the thunderclap that’s still echoing.
Day after day, developers have been posting overwhelmingly positive, almost astonished reactions to Anthropic’s newest model. The pattern is unmistakable: Opus 4.5 isn’t just good — it feels transformative.
🌿 The posts speak for themselves
Everywhere you look — Twitter threads, Discord servers, developer forums — you see variations of a single theme:
- “The best coding model I’ve ever used.”
- “It brute-forces solutions like a senior engineer.”
- “The gap between Opus 4.5 and everything else is insane.”
Even the tone has shifted.
It’s not “Opus is slightly better.” It’s “Opus is operating in a different league.”
One developer put it bluntly:
“Opus 4.5 is alien tech.”
And remarkably, this sentiment isn’t coming from casual users.
It’s coming from people who live inside GitHub issues and CI pipelines.
🌿 Claude Code hits $1B ARR — in six months
Anthropic also confirmed a staggering milestone: Claude Code has reached a $1 billion annual run rate, and it did so in just half a year.
This isn’t just success — it’s velocity.
🌿 The acquisition of Bun changes the software equation
Anthropic’s decision to acquire Bun, the ultra-fast JavaScript runtime, deepens this story. Bun has become a beloved tool in developer circles, offering a near-frictionless way to run, test, and bundle code.
By bringing Bun into Anthropic, the company is signaling a future where:
- AI doesn’t just write code
- AI becomes part of the runtime itself
- Tooling is redesigned from the ground up with AI as a first-class participant
In other words, Anthropic is not simply building models.
It is building the developer stack of the AI era.
🌿 And now, the IPO rumors
The Financial Times reported that Anthropic is preparing for a 2026 IPO, working with major investment banks and lawyers while negotiating a round that could place their valuation as high as $350 billion.
If true, it positions Anthropic as a direct challenger to OpenAI’s presumed public future.
Suddenly, the model race is only half the story.
The financial race is beginning.
🌄 3. Mistral Enters the Stage With Precision & Purpose
While the giants build sky-high models and billion-dollar revenue machines, Mistral AI takes a different path — one rooted in efficiency, pragmatism, and clever system design.
The announcement of the Mistral 3 family showcases a philosophy unlike any other major lab today.
🌿 A model family built for ubiquity
Mistral 3 includes multiple tiers:
- Small models (3B, 8B, 14B) — tiny enough to run on laptops, phones, or lightweight servers
- A massive 675B MoE model — competitive with leading Chinese LLMs in reasoning and science
- Native multimodality — images, text, and reasoning in one ecosystem
Their benchmarking reveals that:
- The large model performs slightly better in reasoning tasks than Chinese equivalents
- It lags a bit in coding
- But it outperforms significantly in multilingual workloads
This multilingual strength is no accident — Mistral has made it a priority.
🌿 The “small model” argument
In a compelling interview, Chief Scientist Guillaume Lample explained why small models matter so much:
Most companies don’t need the biggest model.
They need:
- Reliability
- Low latency
- Lower cost
- On-device or hybrid deployment
- Privacy guarantees
- Custom fine-tuning
And in more than 90% of enterprise use cases, a tuned small model works better than a massive proprietary one.
Suddenly, Mistral’s vision doesn’t seem like a side path — it feels like the foundation of a new market.
🌿 Skeptics and believers clash
Not everyone was impressed. Some online voices claimed:
- Mistral 3 is weaker than DeepSeek
- It’s slower and more expensive than GPT-5
- It feels like a “warm-up” rather than a breakthrough
But Mistral’s supporters argue the opposite:
This wasn’t meant to be their flagship — it was a training rehearsal.
Because soon, their 18,000 GPU GB200 cluster will come online.
And then we’ll see what Mistral 4 can truly be.
🌙 4. Stepping Back: What This Week Really Means
When you zoom out and look at all three stories together — Garlic, Opus 4.5, Mistral 3 — a pattern emerges.
It becomes clear that the AI world is transitioning into a new phase:
the age of continuous model cycles.
- Models no longer launch every year
- They launch every quarter
- Sometimes every month
- Sometimes quietly, without branding, purely for internal testing
The stakes have escalated.
The companies have matured.
And the industry is now building at a pace where yesterday’s innovation becomes today’s baseline.
OpenAI is fighting to regain its narrative.
Anthropic is riding a wave of developer enthusiasm.
Mistral is carving out a pragmatic, enterprise-ready niche.
And all three are preparing for an era of:
- faster pre-training
- larger clusters
- smaller on-device models
- deeper integrations
- exponential demand
This isn’t just competition.
It’s the early blueprint of what AI in 2026–2028 will look like.
🌕 A Natural, Human Closing After “Next up, the main…”
When the script said “Next up, the main…”, it felt like a doorway left open — a transition waiting to happen. And in many ways, that incomplete line mirrors the state of the AI world right now.
Every announcement feels like the beginning of something bigger.
Every new model feels like a prologue rather than a conclusion.
Every rumor hints at a deeper chapter still being written.
So as we wrap today’s long, winding journey through Garlic, Opus, Mistral, and the industry’s shifting foundations, there’s one truth that feels undeniable:
👉 We are standing at the threshold of the most transformative period AI has ever experienced.
The story isn’t finished.
The competition isn’t settled.
And the models we’re discussing now may, in hindsight, feel like the earliest notes in a much larger symphony.
But for today, that is enough — a moment to breathe, understand, and appreciate the extraordinary momentum shaping the future.
Tomorrow will bring the next chapter.
And we’ll be right here to explore it together.
Disclaimer
This article summarizes public reporting, industry insights, and community reactions. AI model performance, release timelines, and organizational strategies may evolve. Always refer to official sources for confirmed information.
Official websites:
OpenAI — https://openai.com
Anthropic — https://anthropic.com
Mistral AI — https://mistral.ai
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