Fugu Ultra, the latest model from Sakana AI, promises energy efficiency and competitive performance at a low cost. But can an open model really match the closed-source giants? We examine what we know about its parameters, availability, and development roadmap — and where hard data is still missing.
In mid-2026, with the artificial intelligence market dominated by closed-source models like GPT-4o and Claude 3.5 Sonnet, a new hope emerges for proponents of openness: Fugu Ultra from Sakana AI. The Japanese startup, founded by former Google Brain researcher David Ha and an undisclosed co-founder, promises a model that combines high performance with low operational costs. But is that enough to compete with industry leaders?
In this post, we analyze what we know and what is still missing regarding Fugu Ultra — from technical parameters and availability to development plans. We focus on hard data, and where it is lacking, we clearly point it out.
1. What do we know about Fugu Ultra's technical parameters?
The official Sakana AI website describes Fugu Ultra as a "high-performance open model," emphasizing energy efficiency and low inference costs. However, specific technical data — such as parameter count, context window size, or benchmark results — is not provided on the model's main page.
Based on available information, one can only speculate that Fugu Ultra belongs to the class of models with 70 billion parameters or more, which would place it among the most powerful open models available in 2024. For comparison:
- Llama 3 70B (Meta, April 2024): approximately 70 billion parameters, 8K token context (in MoE version).
- Qwen 2 72B (Alibaba, June 2024): 72 billion parameters, 128K token context.
- Mistral Large (Mistral AI, 2024): approximately 12 billion active parameters (in Mixture of Experts architecture), 32K token context.
The lack of official benchmark data for Fugu Ultra makes direct comparison with the above models impossible. On the Sakana AI website, we also did not find information regarding:
- parameter count,
- context window size (e.g., 32K, 128K, 256K tokens),
- results in standard tests (e.g., MMLU, GSM8K, HumanEval),
- token generation speed (tok/s).
Note: The lack of data on the Sakana AI website means that, to date, Fugu Ultra has not undergone independent benchmarking. Any claims regarding its performance should be treated with caution.
2. How does Fugu Ultra compare to the closed-source giants? A 2024 benchmark overview
Without official Fugu Ultra results, a direct comparison with closed-source models like GPT-4o, Claude 3.5 Sonnet, or Gemini 1.5 Pro is not possible. However, we can refer to the results of competing open models from 2024, which are frequently compared to AI giants.
Based on the LMSYS Chatbot Arena (a ranking platform comparing models on general tasks) and publications on arXiv, here are the estimated results for key open-source models:
| Model | Parameter Count | Context (tokens) | MMLU Score | GSM8K Score | Price / 1K tokens (input/output) |
|---|---|---|---|---|---|
| Llama 3 70B | 70B | 8K | ~80% | ~75% | $0.0006 / $0.0027 (API) |
| Qwen 2 72B | 72B | 128K | ~82% | ~85% | Free (locally) |
| Mistral Large | 12B (MoE) | 32K | ~79% | ~70% | $0.0004 / $0.0015 |
| GPT-4o | ~175B | 128K | ~88% | ~92% | $0.005 / $0.015 |
| Claude 3.5 Sonnet | ~200B | 200K | ~85% | ~90% |
Sources: LMSYS Chatbot Arena, arXiv (2024 preprints), manufacturer documentation.
The comparison above shows that open models with 70B+ parameters are capable of matching or exceeding closed-source giants in certain benchmarks, particularly in mathematical tasks (GSM8K) and general knowledge (MMLU). However, in tasks requiring deep complexity, such as programming or long-document context analysis, closed models still hold an advantage.
Note: The table above presents estimated data. Actual results may vary depending on implementation, fine-tuning, and testing conditions.
3. Availability and licensing: Is Fugu Ultra truly open?
The Fugu Ultra page does not specify the license covering the model. Based on the naming ("open model") and industry context, it can be assumed that it is an open-weight model — meaning the model weights are released, but with restrictions on commercial use or modification. Alternatively, it could be an Apache 2.0 or MIT license, which are commonly used for open AI models.
There is no information regarding:
- model availability in a GitHub repository,
- the ability to run it locally,
- availability via API,
- commercial restrictions.
While models like Llama 3 (Meta) or Qwen 2 (Alibaba) are available under open-source licenses, Fugu Ultra remains a "black box" in terms of usage terms. This is a significant drawback, especially for developers and companies planning to integrate the model into their products.
4. Who is behind Sakana AI and what is their business model?
Sakana AI was founded by David Ha — a former Google Brain researcher known for his work on models such as VQ-VAE and GAU (Gated Attention Unit). The second founder, whose identity has not been disclosed, is associated with AI projects, but their role remains private.
Sakana AI's business model has not been officially announced. Based on the founders' profiles and industry context, several scenarios can be assumed:
- Venture capital funding: Sakana AI may have raised funds from Silicon Valley investors, allowing for model development and competition with giants.
- Freemium model: Releasing the model as open-weight, with a paid API for advanced users or enterprises.
- Strategic partnerships: Collaboration with hardware manufacturers (e.g., NVIDIA, AMD) or cloud platforms (e.g., AWS, Google Cloud).
The lack of official information on revenue or partnerships makes it difficult to assess Sakana AI's long-term prospects. However, it is worth watching to see if the startup adopts a more transparent approach in the future.
5. Benchmarks and metrics: Where is the hard data missing?
One of the most serious issues with Fugu Ultra is the lack of independent benchmarking. The Sakana AI website does not contain:
- results in standard benchmarks (MMLU, GSM8K, HumanEval, Big-Bench Hard),
- information about context window size (e.g., 32K, 128K, 256K tokens),
- token generation speed (tok/s),
- inference costs (per token or per request),
- electricity consumption (e.g., in kWh per million tokens).
Some industry experts suggest that Fugu Ultra may excel in energy efficiency, which is a key factor in an era of growing environmental awareness. However, the lack of reliable data makes it impossible to verify these claims.
If you are interested in energy efficiency in AI, we recommend reading our post: Ubuntu 26.04 LTS as an operating system for AI and machine learning.
6. Independent reviews: Has anyone tested Fugu Ultra yet?
At the time of writing (June 2024), we have found no independent reviews or analyses of Fugu Ultra in reliable sources such as:
- arXiv (no preprints),
- Hugging Face (no model in the repository),
- developer forums (r/LocalLLaMA, no threads),
- AI expert blogs (The Gradient, Towards Data Science).
This means Fugu Ultra is too new to assess its real-world performance and utility. The AI community may need several weeks or even months before the first independent tests appear.
7. Development roadmap: What does the future hold?
No development roadmap or plans for future versions of Fugu Ultra have been published on the Sakana AI website. One can only speculate on the priority development directions based on trends in the AI industry:
- Larger models: Development of models exceeding 100 billion parameters, e.g., 175B+ (similar to GPT-4o).
- Better performance in specialized tasks: Optimization for programming, mathematics, or text analysis.
- Optimization for edge devices: Models capable of running on smartphones or IoT devices.
- Better energy efficiency: Reducing energy consumption while maintaining high performance.
Without official announcements, it is difficult to judge whether Fugu Ultra will be a one-off model or part of a longer Sakana AI strategy. However, it is worth following their social media and website to stay up to date with the latest news.
8. Fugu Ultra in the context of open AI: Why does it matter?
The release of Fugu Ultra is part of a broader trend of AI democratization. Open-source and open-weight models, such as Llama 3, Qwen 2, or Mistral, give users and companies greater control over technology and the ability to adapt models to their own needs. This is the opposite of closed-source models, which are only available via APIs from companies like OpenAI or Anthropic.
The advantages of open models include:
- Transparency: Ability to inspect the model's architecture and weights.
- Cost control: No license or subscription fees (in the case of local execution).
- Customization: Ability to fine-tune the model for specific tasks.
- Ethics and privacy: No need to send data to external servers.
However, open models also have drawbacks:
- Higher hardware requirements: 70B+ parameter models require powerful GPUs (e.g., NVIDIA A100, H100).
- Lower performance in certain tasks (e.g., programming, context analysis).
- Lack of manufacturer support: Unlike closed-source, there is no guarantee of technical support.
Fugu Ultra, if it truly proves to be an open and efficient model, could become an important player in the AI market. However, until independent tests and official data appear, it remains a model "to be verified."
9. Summary: Is Fugu Ultra an AI breakthrough?
Fugu Ultra is a promising model that could bring fresh quality to the world of open AI. Its greatest strengths are:
- Energy efficiency (if Sakana AI's claims prove true),
- Low inference costs,
- Openness (if the model is indeed open-source).
However, the lack of technical data, benchmarks, and a clear license makes it difficult to assess its actual market position. Compared to closed-source giants like GPT-4o or Claude 3.5 Sonnet, Fugu Ultra will likely not match them in general performance, but it may provide an attractive alternative for users who value openness and low costs.
Until independent tests and reviews appear, Fugu Ultra remains a model "to watch." If, however, Sakana AI decides to be more transparent, this model could become an important player in the field of open AI.
We invite you to join the discussion in the comments: Do you believe in the potential of Fugu Ultra? What features would you consider most important in a new open model?
Comments