Everything about Sakana Namazu
Sakana Namazu in numbers: price per token, context window, independent benchmark scores and comparable models, based on public data checked on October 22, 2026.
Data as of October 22, 2026
Key figures
| Developer | Sakana AI |
|---|---|
| Released | August 3, 2026 |
| Context window | 262,144 tokens (≈ 393 pages) |
| Maximum output | 65,536 tokens |
| Input price | $0.95 per million tokens (developer's price) |
| Output price | $4 per million tokens |
| Open weights | No |
| Reasoning mode | Yes |
| Tool calling | Yes |
| Structured output (JSON) | Yes |
| Accepted inputs | text, image, PDF |
| Providers offering it | 5 |
Overview
Released on August 3, 2026, Sakana Namazu is one of the 4 Sakana AI models tracked on this site. Its weights are not published, so it only runs remotely, through the Sakana AI API or partner hosts (5 listed on models.dev). It has a reasoning mode: it can "think" before answering, which helps with multi-step problems but uses more tokens, and therefore more money. That makes it 2 months old as of October 22, 2026.
We file it under mid-range models (34 models in this category on the site).
What do independent benchmarks say?
As of October 22, 2026, Epoch AI publishes no score for Sakana Namazu on our basket's benchmarks. To place it, rely on its specifications and price, then test it on your own cases.
How much does Sakana Namazu cost?
Sakana Namazu costs $0.95 per million input tokens and $4 per million output tokens (developer's price, checked on October 22, 2026).
In practice, an agent task that reads 20,000 tokens and writes 2,000 costs about $0.027, or $27 for 1,000 such tasks.
That is in line with mid-range models, whose median output price is $4.38.
Tokens read back from cache cost $0.15 per million, which helps an agent that keeps resending the same context.
To estimate your own budget, use the agent cost calculator.
Technical specifications
Its context window reaches 262,144 tokens, roughly 393 pages of text at 500 words per page: enough for a long report or several contracts at once. A single answer can run to 65,536 tokens.
It accepts as input: text, image and PDF.
It can call tools (functions, APIs) and return structured JSON output, which makes it a possible foundation for an AI agent.
What is it good for?
Given its category, Sakana Namazu is mainly aimed at a balance between quality and cost, for everyday uses that do not justify a frontier model.
Before adopting it, test it on your own examples: benchmarks measure standard tasks, not your job.
Comparable models
In the same category, the models closest to Sakana Namazu are:
- Mistral Large 4 (Mistral AI, index 79.7)
- Muse Spark 1.2 (Meta, $4.25 per million output tokens)
- Muse Spark 1.1 (Meta, $4.25 per million output tokens)
- Gemini 3.8 Flash (Google DeepMind, index 83.7)
Similar models
- Mistral Large 4 · Mistral AI · index 79.7
- Muse Spark 1.2 · Meta
- Muse Spark 1.1 · Meta
- Gemini 3.8 Flash · Google DeepMind · index 83.7
Other Sakana AI models
- Fugu Max · September 11, 2026
- Fugu Ultra v2 · September 11, 2026
- Fugu Ultra · June 15, 2026
Frequently asked questions
How much does Sakana Namazu cost?
As of October 22, 2026, Sakana Namazu costs $0.95 per million input tokens and $4 per million output tokens (developer's price). A task that reads 20,000 tokens and writes 2,000 costs about $0.027.
What is the context window of Sakana Namazu?
262,144 tokens, roughly 393 pages of text. An answer can reach 65,536 tokens.
Is Sakana Namazu open source?
No. It is a closed Sakana AI model, available only through an API.
When was Sakana Namazu released?
Sakana Namazu was released on August 3, 2026, according to models.dev.
Can you build an agent with Sakana Namazu?
Yes: it can call tools and return structured JSON, the two basic building blocks of an agent.
Data sources
- Models.dev — Model data: models.dev (open-source project)
- OpenRouter — Market prices cross-checked with OpenRouter
Data as of October 22, 2026
Report an error
Does a figure, a date or a fact look wrong? Tell us: every report is checked.