Inkling-Small vs Sakana Namazu
Sakana Namazu leads the LLM Stats Score 43.2 to 39.3. Inkling-Small is 3.3x cheaper per token.
Thinking Machines Lab · Sakana AI · Updated for 2026
Which is better?
Sakana Namazu leads the overall LLM Stats Score 43.2 to 39.3, ranking #37 overall.
In the 1 individual benchmarks reported for both models, Sakana Namazu wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, Inkling-Small is roughly 3.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose Inkling-Small
- cost matters — it's about 3.3x cheaper per token
- you need open weights you can self-host or fine-tune
Choose Sakana Namazu
- overall performance matters — it scores 43.2 and ranks #37 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- 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
24 reported for Inkling-Small · 3 for Sakana Namazu
Inkling-Small outperforms in 0 benchmarks, while Sakana Namazu is better at 1 benchmark (AIME 2026).
Sakana Namazu 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-Small ($0.30/1M tokens) is 3.2x cheaper than Sakana Namazu ($0.95/1M tokens).
For output processing, Inkling-Small ($1.20/1M tokens) is 3.3x cheaper than Sakana Namazu ($4.00/1M tokens).
In conclusion, Sakana Namazu is more expensive than Inkling-Small.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Both models have the same input context window of 256,000 tokens. Both models can generate responses up to 256,000 tokens.
Input capabilities
Documented input modalities across available providers
Both Inkling-Small and Sakana Namazu support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Inkling-Small
Sakana Namazu
License
Usage and distribution terms
Inkling-Small 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-Small was released on 2026-07-30, while Sakana Namazu was released on 2026-08-03.
Sakana Namazu is 0 month newer than Inkling-Small.
Jul 30, 2026
4 weeks ago
Aug 3, 2026
3 weeks ago
4d 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-Small is available from Thinking Machines Lab. Sakana Namazu is available from Sakana AI.
Inkling-Small
Sakana Namazu
Outputs Comparison
Judge for yourself.
Run your own prompts against Inkling-Small and Sakana Namazu side-by-side, then vote on the output you prefer.
FAQ
Common questions about Inkling-Small vs Sakana Namazu.