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Governed Profile

Sakana Namazu

Sakana Namazu is a balanced model with long context support, suited to model training and dataset workflows for enterprise teams.

Try Sakana Namazu with your team

Last reviewed: 2026-09-02

Sakana Namazu

Sakana

Stable
Context Window
262,144
Input / 1M
$1.43
Output / 1M
$6.00

What can you do with Sakana Namazu?

Practical ways teams can use Sakana Namazu inside governed AI workflows.

01

Train LoRA adapters with Sakana Namazu

Create style, product, person, or subject adapters from approved training datasets with Sakana Namazu.

02

Prepare training data with Sakana Namazu

Package images, captions, examples, and labels for repeatable model-training runs with Sakana Namazu.

03

Validate training outputs with Sakana Namazu

Review sample generations, quality drift, and unsafe memorization before production use with Sakana Namazu.

04

Govern dataset access with Sakana Namazu

Restrict sensitive training data with access controls, retention rules, and audit logs with Sakana Namazu.

05

Manage model variants with Sakana Namazu

Track trained adapters, versions, prompts, and approval status across creative workflows with Sakana Namazu.

06

Estimate training cost with Sakana Namazu

Compare dataset size, run count, and model usage before scaling training jobs with Sakana Namazu.

Why this model

Sakana Namazu is available in Remova as a long context option with $1.43 per 1M tokens input pricing, $6.00 per 1M tokens output pricing, and text, image, and file input to text output support.

  • Sakana Namazu offers long context capacity for enterprise prompts and documents.
  • Current Remova pricing band is balanced: $1.43 per 1M tokens input and $6.00 per 1M tokens output.
  • Best-fit workloads include: Model training, Dataset workflows, Style adaptation.
  • Keep audit logs enabled for high-impact use cases.

At a glance

Model ID
sakana/sakana-namazu
Context Window
262,144 tokens
Modality
Text, image, and file input to text output
Input Modalities
Text, Image, File
Output Modalities
Text
Input Price
$1.43 per 1M tokens
Output Price
$6.00 per 1M tokens
Provider
Sakana
Listing Date
2026-08-11

Strengths

  • Sakana Namazu is suited for model training.
  • Supports long context for multi-step prompts and larger working sets.
  • Pricing profile is balanced, enabling predictable workload routing decisions.
  • Can be paired with policy guardrails for safer deployment at scale.

Tradeoffs

  • Prompt standards are still needed to keep output quality consistent across teams.
  • Long-context prompts can increase spend and latency if prompts are not scoped carefully.
  • Balanced-price tiers still need policy-based routing to protect monthly budgets.
  • Model training workflows need dataset consent, version control, and output review before reuse.

Best for

  • Sakana Namazu for training governed model variants from approved datasets.
  • Sakana Namazu for preparing, reviewing, and controlling training datasets.
  • Sakana Namazu for style, subject, or brand adaptation with versioned approvals.
  • Sakana Namazu for validating trained outputs before production reuse.

Rollout checklist

  • Define where Sakana Namazu is default vs. fallback in your routing policy.
  • Enable role-based access and policy checks before opening access broadly.
  • Set spend guardrails by team and monitor weekly token consumption.
  • Start with approved teams, then expand in controlled waves.
  • Re-run quality and cost benchmarks monthly as newer releases appear.

Related models

Explore adjacent model profiles for routing and benchmarking decisions.

Free Resource

Where Should Your Team Start with AI?

Tell us your industry and team size. We'll tell you which AI use cases will save the most time with the least setup.

You get

A shortlist of AI use cases ranked by impact and effort for your situation.

Tuning notes

include_reasoning

Enable only where reasoning traces add operational value or review quality.

reasoning

Increase reasoning effort only for complex tasks that justify extra cost.

reasoning_effort

Use this parameter only with tested defaults in production workflows.

structured_outputs

Use this parameter only with tested defaults in production workflows.

Free Assessment

What Could Go Wrong?

5 questions about how your company uses AI today. We'll show you the risks most companies miss until it's too late.

You get

A risk breakdown with the 3 things you should fix first.

Book demo
Knowledge Hub

Sakana Namazu FAQs

Choose Sakana Namazu when the workload aligns with model training, dataset workflows, style adaptation and quality targets justify its pricing profile.
It depends on workload mix. Most organizations use routing policies so routine traffic stays on lower-cost tiers.
Validate quality on real internal prompts, token efficiency, latency, and policy compliance behavior.

Deploy This Model With Governance

Use policy controls, role-based access, and budget guardrails before enabling advanced model tiers at scale.

Try Sakana Namazu with your team