GLM-5.3-Flash vs Sakana Namazu
GLM-5.3-Flash and Sakana Namazu are closely matched at 50.7 and 42.8 on the LLM Stats Score. GLM-5.3-Flash is 7.2x cheaper per token.
Zhipu AI · Sakana AI · Updated for 2026
Which is better?
GLM-5.3-Flash and Sakana Namazu are closely matched on the overall LLM Stats Score at 50.7 and 42.8.
On price, GLM-5.3-Flash is roughly 7.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GLM-5.3-Flash 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-5.3-Flash
- cost matters — it's about 7.2x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Aug 2026
- you need open weights you can self-host or fine-tune
Choose Sakana Namazu
- you want predictable pricing at $0.95/M input and $4.00/M output
At a glance
The differences that matter most.
Individual benchmarks
15 reported for GLM-5.3-Flash · 3 for Sakana Namazu
GLM-5.3-Flash and Sakana Namazudon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-5.3-Flash ($0.15/1M tokens) is 6.3x cheaper than Sakana Namazu ($0.95/1M tokens).
For output processing, GLM-5.3-Flash ($0.50/1M tokens) is 8.0x cheaper than Sakana Namazu ($4.00/1M tokens).
In conclusion, Sakana Namazu is more expensive than GLM-5.3-Flash.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
GLM-5.3-Flash 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-5.3-Flash is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Both GLM-5.3-Flash and Sakana Namazu support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GLM-5.3-Flash
Sakana Namazu
License
Usage and distribution terms
GLM-5.3-Flash 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-5.3-Flash was released on 2026-08-26, while Sakana Namazu was released on 2026-08-03.
GLM-5.3-Flash is 1 month newer than Sakana Namazu.
Aug 26, 2026
1 weeks ago
3w newerAug 3, 2026
1 months ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
GLM-5.3-Flash is available from DeepInfra, FriendliAI, Novita, ZAI. Sakana Namazu is available from Sakana AI.
GLM-5.3-Flash
Sakana Namazu
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.3-Flash and Sakana Namazu side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.3-Flash vs Sakana Namazu.