DeepSeek-V4-Pro-0813 vs GLM-5.3
DeepSeek-V4-Pro-0813 and GLM-5.3 are closely matched at 52.5 and 53.6 on the LLM Stats Score. DeepSeek-V4-Pro-0813 is 4.0x cheaper per token.
DeepSeek · Zhipu AI · Updated for 2026
Which is better?
DeepSeek-V4-Pro-0813 and GLM-5.3 are closely matched on the overall LLM Stats Score at 52.5 and 53.6.
In the 7 individual benchmarks reported for both models, GLM-5.3 wins 5; this is a narrower head-to-head signal than the composite indexes.
On price, DeepSeek-V4-Pro-0813 is roughly 4.0x 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 LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-V4-Pro-0813
- cost matters — it's about 4.0x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
Choose GLM-5.3
- you value its reported benchmark strengths — it wins 5 of 7 exact shared results
- 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 · 17 for GLM-5.3
DeepSeek-V4-Pro-0813 outperforms in 2 benchmarks (NL2Repo, Toolathlon), while GLM-5.3 is better at 5 benchmarks (Agents' Last Exam, AutomationBench, CyberGym, Humanity's Last Exam, Terminal-Bench 2.1).
GLM-5.3 shows notably better performance in the majority of 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 3.2x cheaper than GLM-5.3 ($1.40/1M tokens).
For output processing, DeepSeek-V4-Pro-0813 ($0.87/1M tokens) is 5.1x cheaper than GLM-5.3 ($4.40/1M tokens).
In conclusion, GLM-5.3 is more expensive than DeepSeek-V4-Pro-0813.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4-Pro-0813 has 847.0B more parameters than GLM-5.3, making it 112.5% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4-Pro-0813 accepts 1,048,576 input tokens compared to GLM-5.3's 1,000,000 tokens. DeepSeek-V4-Pro-0813 can generate longer responses up to 393,216 tokens, while GLM-5.3 is limited to 131,072 tokens.
License
Usage and distribution terms
DeepSeek-V4-Pro-0813 is licensed under MIT, while GLM-5.3 uses GLM-5.3 License.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
GLM-5.3 License
Open weights
Release Timeline
When each model was launched
DeepSeek-V4-Pro-0813 was released on 2026-08-13, while GLM-5.3 was released on 2026-08-14.
GLM-5.3 is 0 month newer than DeepSeek-V4-Pro-0813.
Aug 13, 2026
3 weeks ago
Aug 14, 2026
3 weeks ago
1d 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 is available from FriendliAI, Novita, ZAI.
DeepSeek-V4-Pro-0813
GLM-5.3
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Pro-0813 and GLM-5.3 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Pro-0813 vs GLM-5.3.