DeepSeek-V3.2-Exp vs GLM-5.3-Flash
GLM-5.3-Flash significantly outperforms across most benchmarks. GLM-5.3-Flash is 1.3x cheaper per token.
DeepSeek · Zhipu AI · Updated for 2026
Which is better?
DeepSeek-V3.2-Exp outperforms in 0 benchmarks, while GLM-5.3-Flash is better at 1 benchmark (Humanity's Last Exam). GLM-5.3-Flash significantly outperforms across most benchmarks.
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-Exp
- you want predictable pricing at $0.27/M input and $0.41/M output
Choose GLM-5.3-Flash
- you want the strongest raw capability — it leads on 1 of 1 shared benchmarks
- 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-Exp outperforms in 0 benchmarks, while GLM-5.3-Flash is better at 1 benchmark (Humanity's Last Exam).
GLM-5.3-Flash significantly outperforms across most benchmarks.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V3.2-Exp ($0.27/1M tokens) is 1.8x more expensive than GLM-5.3-Flash ($0.15/1M tokens).
For output processing, DeepSeek-V3.2-Exp ($0.41/1M tokens) is 1.2x cheaper than GLM-5.3-Flash ($0.50/1M tokens).
In conclusion, DeepSeek-V3.2-Exp 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-Exp 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-Exp's 163,840 tokens. GLM-5.3-Flash can generate longer responses up to 131,072 tokens, while DeepSeek-V3.2-Exp is limited to 65,536 tokens.
Input Capabilities
Supported data types and modalities
GLM-5.3-Flash supports multimodal inputs, whereas DeepSeek-V3.2-Exp 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-Exp
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-Exp was released on 2025-09-29, while GLM-5.3-Flash was released on 2026-08-26.
GLM-5.3-Flash is 11 months newer than DeepSeek-V3.2-Exp.
Sep 29, 2025
11 months ago
Aug 26, 2026
0 days ago
11mo 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-Exp is available from Novita. GLM-5.3-Flash is available from DeepInfra, Novita, ZAI.
DeepSeek-V3.2-Exp
GLM-5.3-Flash
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V3.2-Exp and GLM-5.3-Flash side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V3.2-Exp vs GLM-5.3-Flash.