Inkling vs Sakana Namazu
Sakana Namazu leads the LLM Stats Score 41.7 to 37.1. Sakana Namazu is 1.0x cheaper per token.
Thinking Machines Lab · Sakana AI · Updated for 2026
Which is better?
Sakana Namazu leads the overall LLM Stats Score 41.7 to 37.1, ranking #67 overall.
In the 1 individual benchmarks reported for both models, Inkling wins 1; this is a narrower head-to-head signal than the composite indexes.
Inkling also accepts a larger context window (524,288 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose Inkling
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- you process long inputs — it offers a 524,288 token context window
- you need open weights you can self-host or fine-tune
Choose Sakana Namazu
- overall performance matters — it scores 41.7 and ranks #67 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you want the most recent training data — it shipped Aug 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
16 reported for Inkling · 3 for Sakana Namazu
Inkling outperforms in 1 benchmarks (AIME 2026), while Sakana Namazu is better at 0 benchmarks.
Inkling significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, Inkling ($0.95/1M tokens) costs the same as Sakana Namazu ($0.95/1M tokens).
For output processing, Inkling ($4.05/1M tokens) is 1.0x more expensive than Sakana Namazu ($4.00/1M tokens).
In conclusion, Inkling is more expensive than Sakana Namazu.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Inkling accepts 524,288 input tokens compared to Sakana Namazu's 256,000 tokens. Inkling can generate longer responses up to 524,288 tokens, while Sakana Namazu is limited to 256,000 tokens.
Input capabilities
Documented input modalities across available providers
Both Inkling and Sakana Namazu support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Inkling
Sakana Namazu
License
Usage and distribution terms
Inkling is licensed under Apache 2.0, while Sakana Namazu uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
Apache 2.0
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
Inkling was released on 2026-07-21, while Sakana Namazu was released on 2026-08-03.
Sakana Namazu is 0 month newer than Inkling.
Jul 21, 2026
3 months ago
Aug 3, 2026
2 months ago
1w newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Inkling is available from DeepInfra. Sakana Namazu is available from Sakana AI.
Inkling
Sakana Namazu
Outputs Comparison
Judge for yourself.
Run your own prompts against Inkling and Sakana Namazu side-by-side, then vote on the output you prefer.
FAQ
Common questions about Inkling vs Sakana Namazu.