DeepSeek-V4.1-Flash-1017 vs GLM-5.2
DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.0 to 44.8. DeepSeek-V4.1-Flash-1017 is 2.2x cheaper per token.
DeepSeek · Zhipu AI · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash-1017 leads the overall LLM Stats Score 51.0 to 44.8, ranking #15 overall.
The models split the 4 individual benchmarks reported for both models evenly.
On price, DeepSeek-V4.1-Flash-1017 is roughly 2.2x 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-V4.1-Flash-1017
- overall performance matters — it scores 51.0 and ranks #15 on LLM Stats
- your work emphasizes coding and agents — it leads those capability indexes
- cost matters — it's about 2.2x cheaper per token
- you want the most recent training data — it shipped Oct 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
11 reported for DeepSeek-V4.1-Flash-1017 · 19 for GLM-5.2
DeepSeek-V4.1-Flash-1017 outperforms in 2 benchmarks (DeepSWE 1.1, NL2Repo), while GLM-5.2 is better at 2 benchmarks (Humanity's Last Exam, Program Bench).
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-V4.1-Flash-1017 ($0.30/1M tokens) is 2.5x cheaper than GLM-5.2 ($0.75/1M tokens).
For output processing, DeepSeek-V4.1-Flash-1017 ($1.20/1M tokens) is 2.0x cheaper than GLM-5.2 ($2.40/1M tokens).
In conclusion, GLM-5.2 is more expensive than DeepSeek-V4.1-Flash-1017.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4.1-Flash-1017 has 10.2B more parameters than GLM-5.2, making it 1.4% larger.
Context Window
Maximum input and output token capacity
Both models have the same input context window of 1,048,576 tokens. GLM-5.2 can generate longer responses up to 1,048,576 tokens, while DeepSeek-V4.1-Flash-1017 is limited to 393,216 tokens.
Input capabilities
Documented input modalities across available providers
DeepSeek-V4.1-Flash-1017 supports multimodal inputs, whereas GLM-5.2 does not.
DeepSeek-V4.1-Flash-1017 can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4.1-Flash-1017
GLM-5.2
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-V4.1-Flash-1017 was released on 2026-10-17, while GLM-5.2 was released on 2026-06-16.
DeepSeek-V4.1-Flash-1017 is 4 months newer than GLM-5.2.
Oct 17, 2026
2 days ago
4mo newerJun 16, 2026
4 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-V4.1-Flash-1017 is available from DeepSeek. GLM-5.2 is available from DeepInfra, Fireworks, FriendliAI, Novita, Together, ZAI.
DeepSeek-V4.1-Flash-1017
GLM-5.2
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash-1017 and GLM-5.2 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash-1017 vs GLM-5.2.
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