DeepSeek-V4-Flash-0731 vs GLM-5.2
DeepSeek-V4-Flash-0731 and GLM-5.2 are closely matched at 44.7 and 45.5 on the LLM Stats Score. DeepSeek-V4-Flash-0731 is 12.9x cheaper per token.
DeepSeek · Zhipu AI · Updated for 2026
Which is better?
DeepSeek-V4-Flash-0731 and GLM-5.2 are closely matched on the overall LLM Stats Score at 44.7 and 45.5.
In the 4 individual benchmarks reported for both models, DeepSeek-V4-Flash-0731 wins 3; this is a narrower head-to-head signal than the composite indexes.
On price, DeepSeek-V4-Flash-0731 is roughly 12.9x 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-Flash-0731
- you value its reported benchmark strengths — it wins 3 of 4 exact shared results
- cost matters — it's about 12.9x cheaper per token
- you want the most recent training data — it shipped Jul 2026
Choose GLM-5.2
- you want predictable pricing at $0.75/M input and $2.40/M output
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
9 reported for DeepSeek-V4-Flash-0731 · 19 for GLM-5.2
DeepSeek-V4-Flash-0731 outperforms in 3 benchmarks (DeepSWE, NL2Repo, Toolathlon), while GLM-5.2 is better at 0 benchmarks.
DeepSeek-V4-Flash-0731 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-Flash-0731 ($0.06/1M tokens) is 12.5x cheaper than GLM-5.2 ($0.75/1M tokens).
For output processing, DeepSeek-V4-Flash-0731 ($0.18/1M tokens) is 13.3x cheaper than GLM-5.2 ($2.40/1M tokens).
In conclusion, GLM-5.2 is more expensive than DeepSeek-V4-Flash-0731.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-5.2 has 449.0B more parameters than DeepSeek-V4-Flash-0731, making it 147.7% larger.
Context Window
Maximum input and output token capacity
Both models have the same input context window of 1,048,576 tokens. Both models can generate responses up to 1,048,576 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-Flash-0731 was released on 2026-07-31, while GLM-5.2 was released on 2026-06-16.
DeepSeek-V4-Flash-0731 is 2 months newer than GLM-5.2.
Jul 31, 2026
1 months 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-Flash-0731 is available from DeepInfra, Novita, Fireworks. GLM-5.2 is available from DeepInfra, Fireworks, FriendliAI, Novita, Together, ZAI.
DeepSeek-V4-Flash-0731
GLM-5.2
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Flash-0731 and GLM-5.2 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Flash-0731 vs GLM-5.2.