GLM-5.3-Flash vs Hy3
GLM-5.3-Flash leads the LLM Stats Score 50.2 to 43.2. GLM-5.3-Flash is 1.1x cheaper per token.
Zhipu AI · Tencent · Updated for 2026
Which is better?
GLM-5.3-Flash leads the overall LLM Stats Score 50.2 to 43.2, ranking #18 overall.
In the 3 individual benchmarks reported for both models, GLM-5.3-Flash wins 3; this is a narrower head-to-head signal than the composite indexes.
On price, GLM-5.3-Flash is roughly 1.1x 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
- overall performance matters — it scores 50.2 and ranks #18 on LLM Stats
- your work emphasizes reasoning and agents — it leads those capability indexes
- you value its reported benchmark strengths — it wins 3 of 3 exact shared results
- cost matters — it's about 1.1x 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
Choose Hy3
- you want predictable pricing at $0.14/M input and $0.58/M output
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
15 reported for GLM-5.3-Flash · 31 for Hy3
GLM-5.3-Flash outperforms in 3 benchmarks (NL2Repo, Terminal-Bench 2.1, Toolathlon), while Hy3 is better at 0 benchmarks.
GLM-5.3-Flash 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-5.3-Flash ($0.15/1M tokens) is 1.1x more expensive than Hy3 ($0.14/1M tokens).
For output processing, GLM-5.3-Flash ($0.50/1M tokens) is 1.2x cheaper than Hy3 ($0.58/1M tokens).
In conclusion, Hy3 is more expensive than GLM-5.3-Flash.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-5.3-Flash has 25.0B more parameters than Hy3, making it 8.5% larger.
Context Window
Maximum input and output token capacity
GLM-5.3-Flash accepts 1,048,576 input tokens compared to Hy3's 262,144 tokens. GLM-5.3-Flash can generate longer responses up to 1,048,576 tokens, while Hy3 is limited to 262,144 tokens.
Input capabilities
Documented input modalities across available providers
GLM-5.3-Flash supports multimodal inputs, whereas Hy3 does not.
GLM-5.3-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-5.3-Flash
Hy3
License
Usage and distribution terms
GLM-5.3-Flash is licensed under MIT, while Hy3 uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
GLM-5.3-Flash was released on 2026-08-26, while Hy3 was released on 2026-07-06.
GLM-5.3-Flash is 2 months newer than Hy3.
Aug 26, 2026
3 weeks ago
1mo newerJul 6, 2026
2 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. Hy3 is available from DeepInfra.
GLM-5.3-Flash
Hy3
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.3-Flash and Hy3 side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.3-Flash vs Hy3.