Model March Madness (Part 1)

64 top LLMs compete in a tournament to see who comes out on top!

Mar 24, 2026

March Madness is upon us, and as great as college basketball is, I know almost nothing about it. So, let’s replace ballers with models. NCAA with LLM. In this edition of The AI Loop, 64 of today’s highest performance models go head-to-head to see which will be crowned the champion of Model March Madness 2026.

I’ll get into the highly scientific (not really) methodology below. But as is always the case with analyses like these, a lot of it is subjective. I get to decide the criteria, how important they are, and how to score each model based on them. You might have a totally different opinion about how to evaluate this stuff. And to that I say, “You think you’re better than me??”

In any case, at the end of this article, I’m alley-ooping you an HTML file of the interactive bracket so you can do your own analysis and pick your own winner.

With that, let’s get into it!

The Competitors

64 models. Four regions. One champion.

To build the field, I pulled the top 64 models from Arena.ai’s leaderboard, which is arguably the most credible ranking system we have right now for comparing LLMs. Rather than relying on cherry-picked benchmarks from the labs themselves, Arena.ai uses over 5 million real human preference votes from side-by-side model comparisons. Models get scored using a format like chess’s ELO system, where defeating higher-ranked competitors earns more points and losing to lower-ranked models costs more points.

The 64 models were randomly divided into four regions (East, West, South, and Midwest) and seeded 1 through 16 within each region based on their overall Arena.ai ranking. I also made sure each top-seeded model came from a different provider. More fun that way.

Notable snubs

Potentially the most glaring omission from the field? Meta’s Llama models. Since releasing Llama 4 back in April 2025, Meta has been slow to share anything noteworthy enough to find itself in the top 64 as of this analysis. On top of that, last year, Meta was accused of a “bait-and-switch” to improve its ranking on arena.ai.

Other notable absences include Cohere, Microsoft’s Phi, AI21, and most of the smaller open-weight models.

The field we do have is stacked:

Anthropic (10 models)
Google (6 models)
OpenAI (13 models)
xAI (6 models)
DeepSeek (9 models)
Alibaba (6 models)
Moonshot AI (5 models)
Zhipu AI (3 models)
Baidu (3 models)
Mistral, Amazon, and ByteDance (1 model each)

If the bracket is missing something egregious, I’d kindly like to point your outrage to arena.ai 😀

The Methodology

Every model in the bracket was scored on three criteria, each weighted differently. The total score determines who advances in each matchup. Simple enough. Here’s how the weights break down:

Benchmark Performance

(weighted 50%)

This is the raw capability score. I looked at performance across coding benchmarks (SWE-bench Verified), reasoning benchmarks (GPQA Diamond), and overall Arena.ai ELO as a sanity check. Coding and expert reasoning benchmarks were especially helpful.

Real-Life Utility

(weighted 30%)

This is the “can I actually ship with this today” score. It covers API availability, pricing per million tokens, latency, tool reliability, and whether the model is GA or still in preview/beta. That last point matters more than people give it credit for. A model without a stable API, an SLA, or published benchmarks is a model you can’t responsibly put in a production. Several models took meaningful hits here for still being in preview at the time of writing, including some high seeds that benchmark beautifully but aren’t production-ready yet.

Versatility

(weighted 20%)

This covers use case range. How well does the model perform across coding, reasoning, long-context analysis, and multimodal inputs? Context window size, native support for vision and audio, and whether the model handles different task types consistently all factor in here.

With all that out of the way, let’s play ball!

Round of 64: First Blood

In a field this stacked, you’d expect chalk (i.e., the favored seeds winning) to mostly hold in the first round. And for the most part, it did. Top seeds advanced, bottom seeds went home. But a few interesting results are worth calling out.

The East

The most well-behaved region. The top seeds persevered, naturally: claude-opus-4-6 and claude-opus-4-6-thinking both cruised through with scary scores across the board, gpt-5.3-chat-latest and grok-4.1 handled their business, too.

But the 8-9 matchup delivered our first upset (i.e., when a lower seed beats a higher seed): gpt-5.2-high knocked out claude-opus-4-1-20250805, the older Claude model simply couldn’t keep up on the utility side of the ledger (a theme you’ll see repeat throughout this bracket).

The West

Where things got spicy early. Two upsets in the same region in the first round: dola-seed-2.0-preview (a questionable 5-seed from ByteDance) got bounced immediately by deepseek-v3.2-exp-thinking. The 12-over-5 classic March Madness upset came down to utility – the dola model is in preview (lower utility) while the deepseek model is open-weight (higher utility).

Meanwhile, ERNIE models are a case study in what happens when benchmark performance doesn’t translate into real-world accessibility. Despite their impressive technical specs, both ERNIE models were upset for their lack of utility.

The South

Chalky but some interesting dynamics. The 1-seed grok-4.20-beta survived a tighter-than-expected first round. When you apply a utility penalty for beta status and unverified benchmarks, even a 1-seed starts looking mortal. Also notable: Amazon’s lone model in the fight fell to a Claude Sonnet model. Not so Prime after all.

The Midwest

The round’s most electric upsets. The 7-seed, claude-opus-4-1-thinking-16k, lost to o3-2025-04-16 in the 7-10 matchup. Capped reasoning budgets hurt on versatility, and o3 has enough residual credibility to capitalize. And in the 6-11 matchup, gpt-5-chat knocked out claude-sonnet-4-5-thinking-32k, another thinking variant biting the dust early, penalized for the same reasons as its cousins in other regions. I think, therefore I’m eliminated.

Round of 64 Takeaways

Preview models are paying the price for lacking maturity.
Thinking variants with capped budgets are underperforming.
We lost Amazon, Baidu, ByteDance, and Mistral

Round of 32: A 1-Seed Has Fallen

32 models become 16, and the competition heats up. Chalk keeps holding in most places, but some high seeds are showing nasty cracks, including our first 1-seed elimination.