March 2026 got a little out of hand.
Between March 10 and 16, six major AI labs released twelve distinct models. That's two per day, one per company, all in the same week. Then Google dropped Gemini 3 Deep Think on March 26. Then Anthropic's next major model — reportedly called "Mythos" and described by insiders as "a step change in reasoning capability" — leaked through what the press generously called a basic security blunder. OpenAI shipped GPT-5.4 in three flavors. DeepSeek V4 arrived at a trillion parameters while somehow using fewer active parameters than its predecessor. Mistral released a 22-billion parameter model that outperformed closed models three to five times its size on reasoning benchmarks.
If you felt overwhelmed by AI news in March, you were paying attention.
Everyone Claims to Be the Best
Here's the thing every one of these releases has in common: they all came with the same claim. This one is the best. The benchmarks prove it. The demos show it. The blog post is confident. The press coverage is breathless.
They can't all be right.
And yet — here we are, looking at a field where the "best" model changes every few weeks, where each lab runs different training approaches, different safety calibrations, different benchmarks they chose to emphasize. Gemini's Deep Think variant is optimized for extended reasoning chains. Claude tends to flag ethical and risk considerations that other models gloss over. GPT models often have broader factual recall but can be overconfident. DeepSeek brings different priors from genuinely different training data. Mistral Small 4 punches so far above its weight class that the whole idea of "bigger is better" is now openly in question.
None of them are the same AI. None of them are the complete AI.
They're all brilliant in specific ways, and quietly mediocre in ways you don't discover until you needed the thing they were mediocre at — when the decision was already made.
The real problem with the model avalanche isn't that there are too many models. It's that we keep asking the wrong question: which one should I pick?
What Actually Happens When You Run Multiple Models on the Same Question
There's a different way to think about this.
At Shingikai, we run what we call an AI council — multiple models given the same question, deliberating together, with their reasoning visible and their disagreements on the table. Here's what consistently shows up when you do that:
Different models catch different things.
Ask a council to evaluate a business decision, and you'll see two models converging quickly on the obvious risks while a third quietly flags the risk they're both treating as negligible but probably shouldn't. Ask a council to stress-test a marketing angle, and one model will challenge the assumption buried in your brief that you forgot was even an assumption. Run a Red Team vs Blue Team session on a pricing strategy, and the model playing devil's advocate will find the objection your favorite model was too agreeable to raise.
These aren't exotic edge cases. This is what happens when you run the same hard question through models with genuinely different training approaches — they have different blind spots, and those blind spots don't all overlap.
That's exactly what you want when the question is expensive to get wrong.
The Avalanche Makes the Argument Stronger
The model explosion of March 2026 actually strengthens the case for councils, not weakens it.
Twelve new models in one week means twelve distinct AI perspectives trained on different data with different objectives. If you're only using one of them, you're leaving eleven potential sources of disagreement on the table — and one of those might be the one that catches the thing you missed.
Think of it like diversification. In finance, you don't put everything in a single asset because you're confident about which one will win. You diversify because uncertainty about which one wins is the whole point. The portfolio hedges against your own blind spots about the future.
AI models work similarly. Not because they're interchangeable — they're very much not — but because no single model has a monopoly on good judgment. Each one has different training, different calibrations, different tendencies toward overconfidence. Running multiple models against your question isn't inefficient; it's the intellectually honest move when the stakes are real.
The Mythos Problem
The Anthropic leak is the clearest recent illustration of the pace problem. A model that insiders are calling a "step change in capabilities" was accidentally exposed before launch — which means that by the time it actually ships and you've decided whether to trust it, something else will be right behind it claiming to be better.
The "pick one trusted oracle" strategy keeps breaking because the frontier keeps moving. You pick GPT-5.4 as your go-to. Three weeks later, Gemini 3 Deep Think runs circles around it on reasoning tasks. You switch. Then Mythos arrives. Then DeepSeek V5, presumably.
An AI council structure, on the other hand, is durable. New model released? Add it to the council. Better reasoning capability? Great — let it argue with the models that have better factual recall or more cautious risk framing. The council adapts; the single-model trust strategy keeps requiring you to start over.
Which Model Should You Trust?
Honestly? All of them, a little. None of them, completely.
The interesting thing about March's model avalanche isn't which lab won — it's that the divergence between top models is evidence that we're not converging on one correct AI yet. These models were trained differently and they think differently. That diversity of perspective is either a problem to solve (pick one) or a resource to use (make them deliberate together).
If you've been watching the releases with a vague sense of anxiety — like you're supposed to be tracking which model "won" this week and you're already falling behind — that feeling is pointing at the right problem with the wrong frame.
The question isn't which model to trust. The question is whether your hardest decisions deserve more than one opinion.
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