Sakana Namazu model profile
Evidence snapshot:
Sakana Namazu is a Sakana AI model profile with a 262,144-token context window. The dated catalog snapshot records text, image, file modalities and interfaces for file input, prompt caching, reasoning controls, structured output, text generation.
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,...
Sakana Namazu is a reference profile: Sakana AI publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call.
Sakana Namazu is an independent knowledge profile. It does not claim that Kendr hosts the model, quote a Kendr customer price, promise API availability, or advertise a Kendr routing receipt.
Sakana Namazu accepts text, image, and file and returns text. The snapshot records file input, prompt caching, reasoning controls, structured output, text generation, tool calling, vision, and web search pricing as capabilities or interfaces.
The dated reference snapshot lists $0.95 input and $4 output per million tokens. Cached input is $0.15 per million tokens.
No comparable third-party benchmark value is present in the 2026-09-06 snapshot. Missing values are not estimated.
No comparable popularity rank or traffic share is present in the 2026-09-06 snapshot. No comparable routing rank or traffic-share observation is attached to this dated profile; missing adoption evidence is not estimated.
Profile data was reviewed for the 2026-09-06 snapshot. Claims retain their source dates, and unavailable fields are shown as unavailable instead of estimated.
Profile facts
- Profile type
- Model research profile
- Provider or publisher
- Sakana AI
- Context window
- 262,144 tokens
- Snapshot date
- 2026-09-06
- Knowledge cutoff
- Not disclosed
- Reference catalog ID
- sakana/sakana-namazu
When to pick Sakana Namazu
Sakana Namazu is a reference profile: Sakana AI publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call.
Each condition below is derived from this model's own values in the 2026-09-06 snapshot, compared only against models publishing the same field.
- Reach for it when the input includes images. The snapshot records image input, so screenshots, scans, and diagrams can be sent directly instead of described.
- Reach for it when the model has to call tools. Tool calling is recorded in the snapshot, so this route can drive an agent loop rather than only answer in prose.
- Reach for it when you want to trade depth against cost per call. Reasoning effort is selectable across high, none, so one route can serve both cheap and deep work.
- Look elsewhere when you need to call it through Kendr today. This is a research reference profile with no Kendr alias. The facts and evidence here are published for comparison; the model is not routable on Kendr.
Context, modalities, and identity
Sakana Namazu accepts text, image, and file and returns text. The snapshot records file input, prompt caching, reasoning controls, structured output, text generation, tool calling, vision, and web search pricing as capabilities or interfaces.
- Provider or publisher
- Sakana AI
- Context window
- 262,144 tokens
- Maximum output
- 65,536 tokens
- Input modalities
- text, image, and file
- Output modalities
- text
- Knowledge cutoff
- Not disclosed
Model overview
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,...
- Reference model ID
- sakana/sakana-namazu
- Canonical version
- sakana/namazu-20260811
- Hugging Face ID
- Not separately documented
Capabilities and supported controls
Capabilities and parameters are reported from the dated catalog and source records; their presence does not guarantee identical behavior across every provider route.
- Supported parameters
- include_reasoning, reasoning, reasoning_effort, structured_outputs, tool_choice, tools, web_search_options
- file input
- prompt caching
- reasoning controls
- structured output
- text generation
- tool calling
- vision
- web search pricing
Dated pricing snapshot
The dated reference snapshot lists $0.95 input and $4 output per million tokens. Cached input is $0.15 per million tokens.
These are dated third-party reference prices, not Kendr prices or an availability offer. Routes, tiers, caching, region, and provider policy can change the landed price.
- Price date
- 2026-09-06
- Input
- $0.95 per 1M tokens
- Cached input
- $0.15 per 1M tokens
- Output
- $4 per 1M tokens
Provider routes, performance, and uptime
The snapshot retains 1 provider endpoint. Provider prices, context limits, p50 performance, and uptime can differ by route and are not Kendr guarantees.
- Active provider endpoints
- 1
- Best p50 latency
- No recent metric
- Best p50 throughput
- No recent metric
- Availability with routing
- No recent metric
- Availability without routing
- No recent metric
- Performance date
- 2026-09-06
| Provider | Quantization | Input / 1M | Output / 1M | Cache read / 1M | Context | p50 latency | p50 throughput | Uptime (1d) |
|---|---|---|---|---|---|---|---|---|
| Sakana AI | unknown | $0.95 / 1M | $4 / 1M | $0.15 / 1M | 262,144 tokens | No recent p50 | No recent p50 | 100.00% |
Benchmark evidence and limitations
No comparable third-party benchmark value is present in the 2026-09-06 snapshot. Missing values are not estimated.
Third-party catalog fields normalized from the cited model dataset; benchmark methodology and coverage differ by source.
Popularity and market context
No comparable popularity rank or traffic share is present in the 2026-09-06 snapshot. No comparable routing rank or traffic-share observation is attached to this dated profile; missing adoption evidence is not estimated.
- Third-party catalog rank
- No verified rank
- Global market share
- Not inferred
- Observation date
- 2026-09-06
Frequently asked questions
When should I use Sakana Namazu?
Sakana Namazu is a reference profile: Sakana AI publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call. Reach for it when the input includes images: The snapshot records image input, so screenshots, scans, and diagrams can be sent directly instead of described. Reach for it when the model has to call tools: Tool calling is recorded in the snapshot, so this route can drive an agent loop rather than only answer in prose. Reach for it when you want to trade depth against cost per call: Reasoning effort is selectable across high, none, so one route can serve both cheap and deep work. Look elsewhere when you need to call it through Kendr today: This is a research reference profile with no Kendr alias. The facts and evidence here are published for comparison; the model is not routable on Kendr.
What is Sakana Namazu?
Sakana Namazu is a Sakana AI model profile with a 262,144-token context window. The dated catalog snapshot records text, image, file modalities and interfaces for file input, prompt caching, reasoning controls, structured output, text generation.
What context window does Sakana Namazu have?
The dated profile lists 262,144 tokens of context and up to 65,536 output tokens. Provider routes, variants, and runtime configuration can impose lower effective limits.
What benchmark evidence is available for Sakana Namazu?
No comparable third-party benchmark value is present in the 2026-09-06 snapshot. Missing values are not estimated. Third-party catalog fields normalized from the cited model dataset; benchmark methodology and coverage differ by source.
Is Sakana Namazu available through Kendr?
This is an independent knowledge profile, not a Kendr-hosted availability claim. Check Kendr's live public model API for currently enabled Kendr aliases.
Does the popularity rank represent Sakana Namazu's global market share?
No comparable popularity rank or traffic share is present in the 2026-09-06 snapshot. No comparable routing rank or traffic-share observation is attached to this dated profile; missing adoption evidence is not estimated. No global market-share percentage is inferred when the source does not publish one.
Sources and evidence dates
- Sakana Namazu third-party catalog record (catalog, checked 2026-09-06)
- Third-party model catalog methodology (methodology, checked 2026-09-06)
- Sakana AI official model documentation (primary, checked 2026-09-06)