Compare GPT-6.1 Sol vs. Sakana Fugu Ultra in 2026

Sakana Fugu Ultra

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Description

GPT-6.1 Sol is an upgraded OpenAI model that combines advanced intelligence with lower operating costs for coding, professional knowledge work, computer use, scientific research, and autonomous agents. OpenAI positions it as offering near-GPT-6 Astra intelligence at one-fifth of Astra's standard input and output token prices. The model delivers substantial improvements over GPT-6 Sol in software engineering, complex document understanding, business automation, and long-horizon computer-use workflows. On DeepSWE v1.1, GPT-6.1 Sol matches GPT-6 Astra at approximately one-fifth of the cost and surpasses GPT-6 Sol's highest score by 6.4 percentage points at lower reasoning effort. On AutomationBench, it scores 4.8 percentage points higher than GPT-6 Sol at the same reasoning setting and 2.2 points above Opus 5.5 at medium reasoning effort. GPT-6.1 Sol also improves computer use, coming within 2.1 percentage points of GPT-6 Astra on the OSWorld 2.0 offline set at maximum reasoning effort while costing roughly one-seventh as much per task. For scientific workflows, the model more than doubles GPT-6 Sol's Terminal-Bench Science 0.1 score at maximum effort while reducing average task cost by more than half. Factuality has also improved, with the share of responses containing a factual error at low reasoning effort falling from 11.4% with GPT-6 Sol to 7.7% with GPT-6.1 Sol on OpenAI's difficult error-focused evaluation. Developers can access GPT-6.1 Sol through the OpenAI API for $2 per million input tokens, $0.10 per million cached input tokens, and $10 per million output tokens, while eligible users can access it through ChatGPT Work and Codex.

Description

Sakana Fugu Ultra is a performance-optimized multi-agent AI model designed for hard technical, research, security, and analytical workloads. It coordinates a deeper pool of expert agents than the standard Fugu model, allowing it to focus on maximum answer quality for complex tasks. The model is available through the same OpenAI-compatible API as Sakana Fugu, making it easier to integrate into existing tools, developer workflows, and AI applications. Fugu Ultra is especially useful for coding, advanced code review, Kaggle competitions, paper reproduction, cybersecurity assessments, literature reviews, patent research, and long-running autonomous workflows. Instead of requiring users to choose individual models or define agent roles, Fugu Ultra dynamically assembles and coordinates the agents that are best suited for each task. Its approach is grounded in learned model orchestration research, including TRINITY and the Conductor, which explore how multiple AI systems can collaborate more effectively. Organizations can also control which providers or models participate in the agent pool to support privacy, compliance, and internal policy requirements. Fugu Ultra is positioned for high-value tasks where deeper analysis, stronger reasoning, and better reliability matter more than speed alone. Sakana Fugu Ultra gives developers, researchers, and enterprises a way to use frontier-level multi-agent intelligence through one managed endpoint.