gertlabs

Google's major releases have usually done pretty well on our benchmarks because they're good at hard problems and open-ended decision making (with a well specified prompt), which obviously doesn't always translate to user experience. In particular, older Google models struggled to work efficiently in a harness. They seem to have solved that with Argon.

We did see a new problem which I hope they can fix. This is the first near-frontier model we've caught in infinite reasoning loops in a long time. We also saw a few examples of it failing to properly delineate reasoning blocks, so it has a higher failure rate than its competitors.

My overall impression is that Argon is a near-frontier model for engineering design, code analysis, and chat. It definitely has the intelligence to be a competitive coding assistant product, but a good coding assistant needs to be polished, so I hope they can clean up the CoT bugs.

This has the least measured skill differentiation of all of our environments, and not because forecasting/markets don't require skill or intelligence. Even the best models are so far from anticipating the behavior of the other agents and understanding the emergent effects that a 2025 model with a naive strategy can often outperform over the timeframes of the simulation simply because some other models in the simulation chose a similar self-reinforcing strategy. This likely happens to some degree in real markets.

- Because it's a heavy reasoner, it sits near Gemini 3.7 Flash on the Pareto front (not as cheap as the price suggests in practice).

- Closer than expected to the top open weights models (GLM 5.3 and Kimi K3) in agentic coding, at lower cost.

- Chinese models have always been strong iterators in an agentic harness. This model is no different, reaching an average percentile ~20% higher when given a harness vs a one-shot solution. That one-shot fluid intelligence is what makes a model feel smart, though, and typically results in fewer attempts/tokens to solve a problem, and American frontier models are still far ahead in that department.

The new architecture is interesting. It puts pricing between their old Flash and Pro lineups, suggesting they might be abandoning their super-cheap flash models (which weren't that fast due to heavy reasoning) and their pro models (which sort of flopped and weren't consistently better than their flash models, despite the size/cost) and shipping a strong intermediate that competes with the Gemini Flash series.

I think we're very close to the point where AI-driven breakthroughs outside of pure math and software start to really affect the world.

We evaluated GPT-6 Astra in 100 complex, unsaturated multi-agent coding environments, competing and cooperating with other models in open-ended tasks.

It's the new frontier model by a landslide. It's even more dominant than the Fable 5 release, because not only does it wipe the floor with the second best model (Fable 5.1), it was also ~80% cheaper and 30% faster in agentic coding[1].

Astra is a groundbreaking model. The biggest breakthrough since Opus 4.5, maybe even since GPT 4. It broke AAII, which is hitting the limits of what most popular benchmarks can measure -- it's definitely fair to call it AGI.

We just published GLM 5.3 results on our multi-agent coding evaluations and it's definitely an impressive model, coming in around #6. For the price, it's actually not Pareto optimal, falling slightly behind Grok 4.6 (which is faster and the same price) and Sol 5.6 (which uses far fewer thinking tokens for comparable results). As with most Chinese models, it excels at iterating in a harness while its first answer/base fluid intelligence is below the American frontier.