GLM-5.3 vs Qwen3.8-27B
GLM-5.3 leads the LLM Stats Score 52.7 to 45.1. Qwen3.8-27B is 1.8x cheaper per token.
Zhipu AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
GLM-5.3 leads the overall LLM Stats Score 52.7 to 45.1, ranking #9 overall.
In the 5 individual benchmarks reported for both models, GLM-5.3 wins 4; this is a narrower head-to-head signal than the composite indexes.
On price, Qwen3.8-27B is roughly 1.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GLM-5.3 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
- overall performance matters — it scores 52.7 and ranks #9 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 4 of 5 exact shared results
- you process long inputs — it offers a 1,048,576 token context window
Choose Qwen3.8-27B
- cost matters — it's about 1.8x cheaper per token
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
17 reported for GLM-5.3 · 26 for Qwen3.8-27B
GLM-5.3 outperforms in 4 benchmarks (DeepSWE 1.1, Humanity's Last Exam, NL2Repo, Terminal-Bench 2.1), while Qwen3.8-27B is better at 1 benchmark (Agents' Last Exam).
GLM-5.3 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 ($1.20/1M tokens) is 3.0x more expensive than Qwen3.8-27B ($0.40/1M tokens).
For output processing, GLM-5.3 ($4.00/1M tokens) is 1.3x more expensive than Qwen3.8-27B ($3.00/1M tokens).
In conclusion, GLM-5.3 is more expensive than Qwen3.8-27B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-5.3 has 725.2B more parameters than Qwen3.8-27B, making it 2610.4% larger.
Context Window
Maximum input and output token capacity
GLM-5.3 accepts 1,048,576 input tokens compared to Qwen3.8-27B's 262,144 tokens. GLM-5.3 can generate longer responses up to 1,048,576 tokens, while Qwen3.8-27B is limited to 262,144 tokens.
Input capabilities
Documented input modalities across available providers
Qwen3.8-27B supports multimodal inputs, whereas GLM-5.3 does not.
Qwen3.8-27B can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-5.3
Qwen3.8-27B
License
Usage and distribution terms
GLM-5.3 is licensed under GLM-5.3 License, while Qwen3.8-27B uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
GLM-5.3 License
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
Both models were released on 2026-08-14.
They likely represent similar generations of model development.
Aug 14, 2026
1 months ago
Aug 14, 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 is available from DeepInfra, FriendliAI, Novita, ZAI. Qwen3.8-27B is available from DeepInfra, FriendliAI.
GLM-5.3
Qwen3.8-27B
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.3 and Qwen3.8-27B side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.3 vs Qwen3.8-27B.