Fugu models surpass publicly accessible frontier models and are shoulder-to-shoulder with Fable 5 and Mythos Preview in various rigorous engineering, scientific, and reasoning benchmarks while delivering frontier capability without the risk of export controls.

The company said that in testing, 95 percent of Fable sessions ran entirely on Fable responses, without falling back to Opus 4.8.

Composer 2.5 is exceptionally intelligent & up to 10x more efficient than similarly capable models.

adding a deterministic retrieval layer made model choice much less important

Claude Opus 4.8, achieves a score of only 13.4%. Other models score significantly lower: GPT-5.5 receives 6.3%, Gemini 3.1 Pro 4.7%, and others even less.

As of March 2026, AI systems are able to post-train models to get about half as much of the uplift as ones trained by humans. The specific eval scores are derived by a 'weighted average is taken across all post-trained LLMs... The top-scoring systems as of April get 25%-28% (Opus 4.6, and GPT 5.4), compared to a human score of 51%.'

DeepSeek-V4-Pro-Max beats GPT-5.2 and Gemini 3.0-Pro on standard reasoning benchmarks and lands just behind GPT-5.4 and Gemini 3.1-Pro

Two variants are available: **Sakana Fugu Mini 🐟**, optimized with latency in mind, and **Sakana Fugu Ultra 🐡**, the full orchestration system, optimized for performance for demanding tasks.

GPQAD | 94.4 | 90.9 | 92.7 | 92.4 | **95.1** | LCBv6 | 90.3 | 92.1 | 92.4 | 90.4 | **93.2** | SWEPro | 48.4 | 51.2 | _53.4_ | 51.3 | **54.2**

The best-performing model across these three metrics was a pair of independent linear trends: one for reasoning models and one for non-reasoning models.

GPT-4o operates at roughly 200 billion parameters and outperforms the original 1.8 trillion-parameter GPT-4

It also surpasses all peer-scale dense models by a wide margin.

It also surpasses all peer-scale dense models by a wide margin.

Among the latest frontier models, GPT-5.4 over-edits the most.

Opus 4.7 introduces a new `xhigh` ('extra high') effort level between `high` and `max`, giving users finer control over the tradeoff between reasoning and latency on hard problems.

I-DLM-8B is the first DLM to match the quality of its same-scale AR counterpart, outperforming LLaDA-2.1-mini (16B) by +26 on AIME-24 and +15 on LiveCodeBench-v6 with half the parameters

Cost (USD) to run the evaluation: GPT-5.4 (xhigh): $1,110, Claude Opus 4.6 (max): $1,055

our approach improves Qwen3.5-4B from 63.8 percent to 66.7 percent (+2.9pp) and Qwen3-30B-A3B from 58.0 percent to 69.5 percent (+11.5pp)

the trained 4B model exceeding GPT-4.1 (49.4 percent) and GPT-4o (42.8 percent) despite being 50 times smaller

The most intriguing result in the present study is thepositive effect of white noise on performance for theADHD children. This noise effect was present in boththe non-medicated and medicated children. Thissupports the MBA (Moderate Brain Arousal) model(Sikstro ̈m & So ̈derlund, 2007), suggesting that theendogenous (neural) noise level in children withADHD is sub-optimal. MBA accounts for the noise-enhancing phenomenon by stochastic resonance(SR). The model suggests that noise in the environ-ment introduces internal noise into the neural sys-tem through the perceptual system. Of particularimportance, the MBA model suggests that the peakof the SR curve depends on the dopamine level, sothat participants with low dopamine levels (ADHD)require more noise for optimal cognitive performancecompared to controls.

The MBAmodel predicts that noise enhances memory perfor-mance for ADHD and attenuates performance forcontrols. We will also argue for a link between theeffects of noise, dopamine regulation, and cognitiveperformance.