GLM-5.2 vs Qwen3.8 Flash
GLM-5.2 and Qwen3.8 Flash are closely matched at 45.6 and 49.1 on the LLM Stats Score. Qwen3.8 Flash is 5.1x cheaper per token.
Zhipu AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
GLM-5.2 and Qwen3.8 Flash are closely matched on the overall LLM Stats Score at 45.6 and 49.1.
In the 6 individual benchmarks reported for both models, Qwen3.8 Flash wins 4; this is a narrower head-to-head signal than the composite indexes.
On price, Qwen3.8 Flash is roughly 5.1x 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 process long inputs — it offers a 1,048,576 token context window
- you need open weights you can self-host or fine-tune
Choose Qwen3.8 Flash
- you value its reported benchmark strengths — it wins 4 of 6 exact shared results
- cost matters — it's about 5.1x cheaper per token
- 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 · 22 for Qwen3.8 Flash
GLM-5.2 outperforms in 2 benchmarks (Humanity's Last Exam, NL2Repo), while Qwen3.8 Flash is better at 4 benchmarks (DeepSWE 1.1, GPQA, SWE-Bench Pro, Toolathlon).
Qwen3.8 Flash shows notably better performance in the majority of 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 ($0.75/1M tokens) is 5.0x more expensive than Qwen3.8 Flash ($0.15/1M tokens).
For output processing, GLM-5.2 ($2.40/1M tokens) is 5.1x more expensive than Qwen3.8 Flash ($0.47/1M tokens).
In conclusion, GLM-5.2 is more expensive than Qwen3.8 Flash.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-5.2 has 628.0B more parameters than Qwen3.8 Flash, making it 502.4% larger.
Context Window
Maximum input and output token capacity
GLM-5.2 accepts 1,048,576 input tokens compared to Qwen3.8 Flash's 1,000,000 tokens. GLM-5.2 can generate longer responses up to 1,048,576 tokens, while Qwen3.8 Flash is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Qwen3.8 Flash supports multimodal inputs, whereas GLM-5.2 does not.
Qwen3.8 Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-5.2
Qwen3.8 Flash
License
Usage and distribution terms
GLM-5.2 is licensed under MIT, while Qwen3.8 Flash 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 Qwen3.8 Flash was released on 2026-08-26.
Qwen3.8 Flash is 2 months newer than GLM-5.2.
Jun 16, 2026
3 months ago
Aug 26, 2026
3 weeks ago
2mo 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. Qwen3.8 Flash is available from Novita.
GLM-5.2
Qwen3.8 Flash
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.2 and Qwen3.8 Flash side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.2 vs Qwen3.8 Flash.