GLM-5.3-Flash vs Qwen3.8-27B
GLM-5.3-Flash leads the LLM Stats Score 50.6 to 45.2. GLM-5.3-Flash is 4.4x cheaper per token.
Zhipu AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
GLM-5.3-Flash leads the overall LLM Stats Score 50.6 to 45.2, ranking #16 overall.
In the 7 individual benchmarks reported for both models, GLM-5.3-Flash wins 4; this is a narrower head-to-head signal than the composite indexes.
On price, GLM-5.3-Flash is roughly 4.4x 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 50.6 and ranks #16 on LLM Stats
- your work emphasizes agents — it leads those capability indexes
- you value its reported benchmark strengths — it wins 4 of 7 exact shared results
- cost matters — it's about 4.4x 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 Qwen3.8-27B
- you want predictable pricing at $0.40/M input and $3.00/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 · 26 for Qwen3.8-27B
GLM-5.3-Flash outperforms in 4 benchmarks (DeepSWE 1.1, Humanity's Last Exam, NL2Repo, Terminal-Bench 2.1), while Qwen3.8-27B is better at 3 benchmarks (Agents' Last Exam, BabyVision, CharXiv-R).
GLM-5.3-Flash has a slight edge in benchmark performance.
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 2.7x cheaper than Qwen3.8-27B ($0.40/1M tokens).
For output processing, GLM-5.3-Flash ($0.50/1M tokens) is 6.0x cheaper than Qwen3.8-27B ($3.00/1M tokens).
In conclusion, Qwen3.8-27B 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 292.2B more parameters than Qwen3.8-27B, making it 1051.8% larger.
Context Window
Maximum input and output token capacity
GLM-5.3-Flash accepts 1,048,576 input tokens compared to Qwen3.8-27B's 262,144 tokens. GLM-5.3-Flash 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
Both GLM-5.3-Flash and Qwen3.8-27B support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GLM-5.3-Flash
Qwen3.8-27B
License
Usage and distribution terms
GLM-5.3-Flash is licensed under MIT, while Qwen3.8-27B 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 Qwen3.8-27B was released on 2026-08-14.
GLM-5.3-Flash is 0 month newer than Qwen3.8-27B.
Aug 26, 2026
2 weeks ago
1w newerAug 14, 2026
3 weeks 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. Qwen3.8-27B is available from DeepInfra, FriendliAI.
GLM-5.3-Flash
Qwen3.8-27B
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.3-Flash and Qwen3.8-27B side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.3-Flash vs Qwen3.8-27B.