DeepSeek-V4.1-Flash-1017 vs GLM-5.3
DeepSeek-V4.1-Flash-1017 and GLM-5.3 are closely matched at 51.2 and 50.9 on the LLM Stats Score. DeepSeek-V4.1-Flash-1017 is 3.6x cheaper per token.
DeepSeek · Zhipu AI · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash-1017 and GLM-5.3 are closely matched on the overall LLM Stats Score at 51.2 and 50.9.
In the 7 individual benchmarks reported for both models, DeepSeek-V4.1-Flash-1017 wins 6; this is a narrower head-to-head signal than the composite indexes.
On price, DeepSeek-V4.1-Flash-1017 is roughly 3.6x 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
- you value its reported benchmark strengths — it wins 6 of 7 exact shared results
- cost matters — it's about 3.6x cheaper per token
- you want the most recent training data — it shipped Oct 2026
Choose GLM-5.3
- you want predictable pricing at $1.20/M input and $4.00/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 · 17 for GLM-5.3
DeepSeek-V4.1-Flash-1017 outperforms in 6 benchmarks (Agents' Last Exam, AutomationBench, DeepSWE 1.1, NL2Repo, Program Bench, Terminal-Bench 4.0), while GLM-5.3 is better at 1 benchmark (Humanity's Last Exam).
DeepSeek-V4.1-Flash-1017 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-V4.1-Flash-1017 ($0.30/1M tokens) is 4.0x cheaper than GLM-5.3 ($1.20/1M tokens).
For output processing, DeepSeek-V4.1-Flash-1017 ($1.20/1M tokens) is 3.3x cheaper than GLM-5.3 ($4.00/1M tokens).
In conclusion, GLM-5.3 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-5.3, 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-5.3 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-5.3 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-5.3
License
Usage and distribution terms
DeepSeek-V4.1-Flash-1017 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-V4.1-Flash-1017 was released on 2026-10-17, while GLM-5.3 was released on 2026-08-14.
DeepSeek-V4.1-Flash-1017 is 2 months newer than GLM-5.3.
Oct 17, 2026
2 days ago
2mo newerAug 14, 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-V4.1-Flash-1017 is available from DeepSeek. GLM-5.3 is available from DeepInfra, FriendliAI, Novita, ZAI.
DeepSeek-V4.1-Flash-1017
GLM-5.3
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash-1017 and GLM-5.3 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash-1017 vs GLM-5.3.