DeepSeek-V4-Pro-0813 vs GLM-5.3-Flash
Both models are evenly matched across the benchmarks. GLM-5.3-Flash is 2.3x cheaper per token.
DeepSeek · Zhipu AI · Updated for 2026
Which is better?
DeepSeek-V4-Pro-0813 outperforms in 3 benchmarks (Humanity's Last Exam, NL2Repo, Terminal-Bench 2.1), while GLM-5.3-Flash is better at 3 benchmarks (Agents' Last Exam, AutomationBench, Toolathlon). Both models are evenly matched across the benchmarks.
On price, GLM-5.3-Flash is roughly 2.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4-Pro-0813 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-V4-Pro-0813
- you process long inputs — it offers a 1,048,576 token context window
Choose GLM-5.3-Flash
- cost matters — it's about 2.3x cheaper per token
- 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-V4-Pro-0813 outperforms in 3 benchmarks (Humanity's Last Exam, NL2Repo, Terminal-Bench 2.1), while GLM-5.3-Flash is better at 3 benchmarks (Agents' Last Exam, AutomationBench, Toolathlon).
Both models are evenly matched across the benchmarks.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4-Pro-0813 ($0.43/1M tokens) is 2.9x more expensive than GLM-5.3-Flash ($0.15/1M tokens).
For output processing, DeepSeek-V4-Pro-0813 ($0.87/1M tokens) is 1.7x more expensive than GLM-5.3-Flash ($0.50/1M tokens).
In conclusion, DeepSeek-V4-Pro-0813 is more expensive than GLM-5.3-Flash.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4-Pro-0813 has 1280.0B more parameters than GLM-5.3-Flash, making it 400.0% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4-Pro-0813 accepts 1,048,576 input tokens compared to GLM-5.3-Flash's 1,000,000 tokens. DeepSeek-V4-Pro-0813 can generate longer responses up to 393,216 tokens, while GLM-5.3-Flash is limited to 131,072 tokens.
Input Capabilities
Supported data types and modalities
GLM-5.3-Flash supports multimodal inputs, whereas DeepSeek-V4-Pro-0813 does not.
GLM-5.3-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Pro-0813
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-V4-Pro-0813 was released on 2026-08-13, while GLM-5.3-Flash was released on 2026-08-26.
GLM-5.3-Flash is 0 month newer than DeepSeek-V4-Pro-0813.
Aug 13, 2026
1 weeks ago
Aug 26, 2026
0 days ago
1w 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-V4-Pro-0813 is available from DeepSeek, DeepInfra, Novita, Together. GLM-5.3-Flash is available from ZAI.
DeepSeek-V4-Pro-0813
GLM-5.3-Flash
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Pro-0813 and GLM-5.3-Flash side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Pro-0813 vs GLM-5.3-Flash.