News · 2026-07-18
Basalt Labs' 'Best AI Model' Claim Collapses: Its Own Repo Admits Monolith-1.0 Was a Relabeled 7B Model
Basalt Labs published a technical report and a Hugging Face repo claiming its new model, Monolith-1.0, was a 1.57-trillion-parameter system that scored 99.4% on Humanity's Last Exam, a top result on one of AI's hardest benchmarks. Basalt's own Hugging Face model card now says the model it actually made available for public download was 'an inflated version of the original Qwen 2.5 7B Instruct model,' and the weights have been pulled. A 7-billion-parameter model cannot be the 1.57-trillion-parameter system Basalt described, and no independent leaderboard corroborates the claimed score.
Key facts
- Basalt Labs listed Monolith-1.0 as released July 17, 2026, claiming a 1.57-trillion-parameter mixture-of-experts model with 49.5 billion active parameters per token, on its Monolith product page and in its technical report PDF.
- The report and site both claim a 99.4% score on Humanity's Last Exam, evaluated in what Basalt calls 'the Basalt harness' rather than any independent test.
- Basalt's own Hugging Face model card says the model released for public download was an inflated version of Qwen 2.5 7B Instruct, and the weights have since been removed.
- Scale AI's official Humanity's Last Exam leaderboard shows no Basalt or Monolith entry among its ranked results.
Humanity's Last Exam, described in its founding research paper, is a 2,500-question benchmark built specifically because AI models were topping out earlier tests; it's designed to stay hard even for frontier systems, and see how AI is benchmarked for why that distinction matters. A genuine 99.4% would be a landmark result. That's what made Basalt's technical report notable in the first place: it described Monolith-1.0 as a mixture-of-experts model, an architecture that splits work across many specialist sub-networks and activates only a handful for any given input, explained in our lesson on mixture-of-experts — in this case, 128 routed experts plus one shared expert, with only 49.5 billion of the claimed 1.57 trillion parameters active per token, trained on 60 trillion tokens with context windows stretched out to over a million tokens.
What actually happened is simpler and less flattering. Basalt's own Hugging Face repo now states that the experiment concluded and that the model made available for public release was, in its words, an inflated version of the original Qwen 2.5 7B Instruct model — an existing, much smaller open model from Alibaba's Qwen team, not anything Basalt built from scratch. The weights have since been removed from the repo entirely. Think of it like a car dealership advertising a race-tuned engine, then when a buyer pops the hood, finding a stock four-cylinder with a fresh paint job over the badge — a 7-billion-parameter model simply cannot be the 1.57-trillion-parameter system the technical report and marketing page both describe. There is no way to relabel one as the other; they are different orders of magnitude.
Compounding the doubt, Scale AI's official Humanity's Last Exam leaderboard, which ranks models with a stated confidence interval, does not list a Basalt or Monolith entry at all. That means the headline 99.4% figure has only ever existed as a number Basalt reported about itself, evaluated on a benchmark harness Basalt itself built and controlled — never checked against the same independent scoring process used for every other model on the public leaderboard.
The story picked up steam on Reddit's r/LocalLLaMA, where a July 18, 2026 post by user WithoutReason1729 laid out the mismatch and drew heavy engagement from people who'd tried to reproduce or inspect the released weights. Some of that community discussion has gone further, alleging that Basalt's public-facing demo site was actually routing requests to a different company's model, such as DeepSeek, behind the scenes. That specific allegation is unconfirmed — no primary document or artifact backs it up yet — and it should be read as a circulating claim, not a established fact, however the underlying weight-swap admission plays out.
The honest caveat: Basalt Labs has not, as of this writing, published a public statement explaining why the technical report described a trillion-parameter architecture that was never actually shipped, or whether any of the claimed training details (the 60-trillion-token run, the long-context capability) ever existed outside the report itself. What is confirmed, in Basalt's own words on its own repo, is that the publicly released artifact was a relabeled small model and that the benchmark score attached to it was never independently verified. For a field increasingly reliant on the assumption that a Hugging Face repo and a leaderboard number mean what they say, that gap between glossy report and quietly deleted weights is the whole story.
Key questions
What did Basalt Labs claim about Monolith-1.0?
Is the 99.4% score real?
What did Basalt actually release?
Is it true Basalt's site was secretly running DeepSeek?
Cite this
APA
Ground Truth. (2026, July 18). Basalt Labs' 'Best AI Model' Claim Collapses: Its Own Repo Admits Monolith-1.0 Was a Relabeled 7B Model. Ground Truth. https://groundtruth.day/news/basalt-monolith-hle-claim-inflated-qwen.html
BibTeX
@misc{groundtruth:basalt-monolith-hle-claim-inflated-qwen,
title = {Basalt Labs' 'Best AI Model' Claim Collapses: Its Own Repo Admits Monolith-1.0 Was a Relabeled 7B Model},
author = {{Ground Truth}},
year = {2026},
month = {jul},
url = {https://groundtruth.day/news/basalt-monolith-hle-claim-inflated-qwen.html}
}