DeepSeek-V3.2 vs GLM-5.3-Flash
GLM-5.3-Flash leads the LLM Stats Score 51.6 to 33.5. GLM-5.3-Flash is 1.2x cheaper per token.
DeepSeek · Zhipu AI · Updated for 2026
Which is better?
GLM-5.3-Flash leads the overall LLM Stats Score 51.6 to 33.5, ranking #11 overall.
In the 2 individual benchmarks reported for both models, GLM-5.3-Flash wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, GLM-5.3-Flash is roughly 1.2x 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 DeepSeek-V3.2
- you want predictable pricing at $0.26/M input and $0.38/M output
Choose GLM-5.3-Flash
- overall performance matters — it scores 51.6 and ranks #11 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 2 of 2 exact shared results
- cost matters — it's about 1.2x 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.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
17 reported for DeepSeek-V3.2 · 15 for GLM-5.3-Flash
DeepSeek-V3.2 outperforms in 0 benchmarks, while GLM-5.3-Flash is better at 2 benchmarks (Humanity's Last Exam, Toolathlon).
GLM-5.3-Flash 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, DeepSeek-V3.2 ($0.26/1M tokens) is 1.7x more expensive than GLM-5.3-Flash ($0.15/1M tokens).
For output processing, DeepSeek-V3.2 ($0.38/1M tokens) is 1.3x cheaper than GLM-5.3-Flash ($0.50/1M tokens).
In conclusion, DeepSeek-V3.2 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 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's 163,840 tokens. DeepSeek-V3.2 can generate longer responses up to 163,840 tokens, while GLM-5.3-Flash is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
GLM-5.3-Flash supports multimodal inputs, whereas DeepSeek-V3.2 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
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 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.
Dec 1, 2025
9 months ago
Aug 26, 2026
4 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 is available from DeepInfra, Novita, Fireworks. GLM-5.3-Flash is available from DeepInfra, Novita, ZAI.
DeepSeek-V3.2
GLM-5.3-Flash
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V3.2 and GLM-5.3-Flash side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V3.2 vs GLM-5.3-Flash.