Sakana AI

Sakana AI is a leading Japanese AI research startup founded in Tokyo in 2023 by David Ha and Llion Jones, a co-author of the Transformer paper. It focuses on nature-inspired algorithms and foundation models, aiming to build small, efficient, and self-evolving AI systems. On AI, Sakana AI has open-sourced its Japanese-English bilingual LLM Tenyu and developed the AI Scientist, a system for fully automated scientific discovery, establishing itself as a symbol of Japanese AI innovation.

AI Models

Fugu Ultra v2

Sakana AI

Fugu Ultra v2 is the higher-performance model in Sakana AI's Fugu family. Rather than a single monolithic model, Fugu is a learned multi-agent orchestration system: a language model trained to route tasks across a fixed pool of open and specialized models and to recursively call instances of itself. Fugu Ultra v2 prioritizes answer quality on complex multi-step reasoning, autonomous research, and full-stack software development, and does not rely on individual proprietary frontier models in its pool. It supports configurable reasoning effort (high, xhigh, max), function calling, structured outputs, image and PDF input, and built-in web search. Prompts above 272K tokens are billed at a higher rate. Orchestration tokens consumed by the system are billed as standard input/output tokens.

Context
1M
Max output
-
Released
2026-09-11
Input
$5 /M
Output
$30 /M

Fugu Max

Sakana AI

Fugu Max is the cost-performance model in Sakana AI's Fugu family. Rather than a single monolithic model, Fugu is a learned multi-agent orchestration system: a language model trained to route tasks across a fixed pool of open-weights and specialized models, including NVIDIA's Nemotron family, and to recursively call instances of itself. Fugu Max dynamically selects efficient combinations of expert agents to improve quality and cost together, and is priced flat regardless of context length. It supports configurable reasoning effort (high, xhigh, max), function calling, structured outputs, image and PDF input, and built-in web search and web fetch. Orchestration tokens consumed by the system are billed as standard input/output tokens.

Context
1M
Max output
-
Released
2026-09-11
Input
$2 /M
Output
$6 /M

Sakana Namazu

Sakana AI

Sakana Namazu is a Japanese-specialized reasoning model from Sakana AI, based on Kimi K2.6 with additional training for Japanese language and business contexts. It is suited for Japanese instruction following, business writing, mathematics, coding, research, and multi-step agent workflows. Sakana AI may use inputs for model training and improvement by default, with an opt-out available in the Sakana Console. Processing entirely within Japan is not guaranteed.

Context
262.1K
Max output
-
Released
2026-08-11
Input
$0.95 /M
Output
$4 /M

Fugu Ultra

Sakana AI

Fugu Ultra is the higher-performance model in Sakana AI's Fugu family. Rather than a single monolithic model, Fugu is a learned multi-agent orchestration system: a language model trained to route tasks across a swappable pool of underlying models and to recursively call instances of itself. Fugu Ultra prioritizes answer quality on complex, multi-step reasoning, coding, and agentic workflows. It supports configurable reasoning effort, tool calling, and built-in web search. Orchestration tokens consumed by the system are billed as standard input/output tokens.

Context
1M
Max output
-
Released
2026-06-24
Input
$5 /M
Output
$30 /M

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