DeepSeek-V4-Pro-0813 vs GLM-5.3-Flash
DeepSeek-V4-Pro-0813 and GLM-5.3-Flash are closely matched at 52.0 and 50.2 on the LLM Stats Score. GLM-5.3-Flash is 2.3x cheaper per token.
DeepSeek · Zhipu AI · Updated for 2026
Which is better?
DeepSeek-V4-Pro-0813 and GLM-5.3-Flash are closely matched on the overall LLM Stats Score at 52.0 and 50.2.
The models split the 6 individual benchmarks reported for both models evenly.
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.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-V4-Pro-0813
- you want predictable pricing at $0.43/M input and $0.87/M output
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.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
12 reported for DeepSeek-V4-Pro-0813 · 15 for GLM-5.3-Flash
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.
Human preference
Blind head-to-head votes and playground 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
Both models have the same input context window of 1,048,576 tokens. GLM-5.3-Flash can generate longer responses up to 1,048,576 tokens, while DeepSeek-V4-Pro-0813 is limited to 393,216 tokens.
Input capabilities
Documented input modalities across available providers
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 months ago
Aug 26, 2026
3 weeks 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 DeepInfra, FriendliAI, Novita, 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.