GLM-5.2 vs Sakana Namazu
GLM-5.2 and Sakana Namazu are closely matched at 46.5 and 43.2 on the LLM Stats Score. GLM-5.2 is 1.2x cheaper per token.
Zhipu AI · Sakana AI · Updated for 2026
Which is better?
GLM-5.2 and Sakana Namazu are closely matched on the overall LLM Stats Score at 46.5 and 43.2.
In the 1 individual benchmarks reported for both models, GLM-5.2 wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, GLM-5.2 is roughly 1.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GLM-5.2 also accepts a larger context window (1,048,576 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 GLM-5.2
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- cost matters — it's about 1.2x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
- you need open weights you can self-host or fine-tune
Choose Sakana Namazu
- 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
19 reported for GLM-5.2 · 3 for Sakana Namazu
GLM-5.2 outperforms in 1 benchmarks (AIME 2026), while Sakana Namazu is better at 0 benchmarks.
GLM-5.2 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, GLM-5.2 ($0.95/1M tokens) costs the same as Sakana Namazu ($0.95/1M tokens).
For output processing, GLM-5.2 ($3.00/1M tokens) is 1.3x cheaper than Sakana Namazu ($4.00/1M tokens).
In conclusion, Sakana Namazu is more expensive than GLM-5.2.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
GLM-5.2 accepts 1,048,576 input tokens compared to Sakana Namazu's 256,000 tokens. Sakana Namazu can generate longer responses up to 256,000 tokens, while GLM-5.2 is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Sakana Namazu supports multimodal inputs, whereas GLM-5.2 does not.
Sakana Namazu can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-5.2
Sakana Namazu
License
Usage and distribution terms
GLM-5.2 is licensed under MIT, while Sakana Namazu uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
GLM-5.2 was released on 2026-06-16, while Sakana Namazu was released on 2026-08-03.
Sakana Namazu is 2 months newer than GLM-5.2.
Jun 16, 2026
2 months ago
Aug 3, 2026
3 weeks ago
1mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
GLM-5.2 is available from DeepInfra, Fireworks, FriendliAI, Novita, Together, ZAI. Sakana Namazu is available from Sakana AI.
GLM-5.2
Sakana Namazu
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.2 and Sakana Namazu side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.2 vs Sakana Namazu.