DeepSeek-V4-Pro-0813 vs GLM-5.2
DeepSeek-V4-Pro-0813 leads the LLM Stats Score 54.1 to 46.5. DeepSeek-V4-Pro-0813 is 2.7x cheaper per token.
DeepSeek · Zhipu AI · Updated for 2026
Which is better?
DeepSeek-V4-Pro-0813 leads the overall LLM Stats Score 54.1 to 46.5, ranking #7 overall.
In the 5 individual benchmarks reported for both models, DeepSeek-V4-Pro-0813 wins 5; this is a narrower head-to-head signal than the composite indexes.
On price, DeepSeek-V4-Pro-0813 is roughly 2.7x 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
- overall performance matters — it scores 54.1 and ranks #7 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 5 of 5 exact shared results
- cost matters — it's about 2.7x cheaper per token
- you want the most recent training data — it shipped Aug 2026
Choose GLM-5.2
- you want predictable pricing at $0.95/M input and $3.00/M output
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 · 19 for GLM-5.2
DeepSeek-V4-Pro-0813 outperforms in 5 benchmarks (DeepSWE, Humanity's Last Exam, NL2Repo, Terminal-Bench 2.1, Toolathlon), while GLM-5.2 is better at 0 benchmarks.
DeepSeek-V4-Pro-0813 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-V4-Pro-0813 ($0.43/1M tokens) is 2.2x cheaper than GLM-5.2 ($0.95/1M tokens).
For output processing, DeepSeek-V4-Pro-0813 ($0.87/1M tokens) is 3.4x cheaper than GLM-5.2 ($3.00/1M tokens).
In conclusion, GLM-5.2 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-5.2, making it 112.5% larger.
Context Window
Maximum input and output token capacity
Both models have the same input context window of 1,048,576 tokens. DeepSeek-V4-Pro-0813 can generate longer responses up to 393,216 tokens, while GLM-5.2 is limited to 131,072 tokens.
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-5.2 was released on 2026-06-16.
DeepSeek-V4-Pro-0813 is 2 months newer than GLM-5.2.
Aug 13, 2026
2 weeks ago
1mo newerJun 16, 2026
2 months ago
Knowledge 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-5.2 is available from DeepInfra, Fireworks, FriendliAI, Novita, Together, ZAI.
DeepSeek-V4-Pro-0813
GLM-5.2
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Pro-0813 and GLM-5.2 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Pro-0813 vs GLM-5.2.