DeepSeek-V3.2 (Non-thinking) vs GLM-5.3-Flash
Comparing DeepSeek-V3.2 (Non-thinking) and GLM-5.3-Flash across benchmarks, pricing, and capabilities.
DeepSeek · Zhipu AI · Updated for 2026
Which is better?
DeepSeek-V3.2 (Non-thinking) and GLM-5.3-Flash trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, GLM-5.3-Flash is roughly 1.3x 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 benchmark, pricing, and model metadata for 2026.
Choose DeepSeek-V3.2 (Non-thinking)
- you want predictable pricing at $0.28/M input and $0.42/M output
Choose GLM-5.3-Flash
- cost matters — it's about 1.3x 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
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3.2 (Non-thinking) and GLM-5.3-Flashdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V3.2 (Non-thinking) ($0.28/1M tokens) is 1.9x more expensive than GLM-5.3-Flash ($0.15/1M tokens).
For output processing, DeepSeek-V3.2 (Non-thinking) ($0.42/1M tokens) is 1.2x cheaper than GLM-5.3-Flash ($0.50/1M tokens).
In conclusion, DeepSeek-V3.2 (Non-thinking) is more expensive than GLM-5.3-Flash.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V3.2 (Non-thinking) has 365.0B more parameters than GLM-5.3-Flash, making it 114.1% larger.
Context Window
Maximum input and output token capacity
GLM-5.3-Flash accepts 1,048,576 input tokens compared to DeepSeek-V3.2 (Non-thinking)'s 131,072 tokens. GLM-5.3-Flash can generate longer responses up to 131,072 tokens, while DeepSeek-V3.2 (Non-thinking) is limited to 8,192 tokens.
Input Capabilities
Supported data types and modalities
GLM-5.3-Flash supports multimodal inputs, whereas DeepSeek-V3.2 (Non-thinking) does not.
GLM-5.3-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V3.2 (Non-thinking)
GLM-5.3-Flash
License
Usage and distribution terms
Both models are licensed under MIT.
Both models share the same licensing terms, providing consistent usage rights.
MIT
Open weights
MIT
Open weights
Release Timeline
When each model was launched
DeepSeek-V3.2 (Non-thinking) was released on 2025-12-01, while GLM-5.3-Flash was released on 2026-08-26.
GLM-5.3-Flash is 9 months newer than DeepSeek-V3.2 (Non-thinking).
Dec 1, 2025
8 months ago
Aug 26, 2026
0 days ago
8mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V3.2 (Non-thinking) is available from DeepSeek. GLM-5.3-Flash is available from DeepInfra, Novita, ZAI.
DeepSeek-V3.2 (Non-thinking)
GLM-5.3-Flash
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V3.2 (Non-thinking) and GLM-5.3-Flash side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V3.2 (Non-thinking) vs GLM-5.3-Flash.