GLM-5.3-Flash vs DeepSeek-V4.1-Flash

Official GLM-5.3-Flash benchmark figure from the zai-org/GLM-5 repository — Z.ai GLM-5 repository bench_53.png. Source · License: MIT (weight card) / Apache-2.0 (GLM-5 tree).

Official DeepSeek-V4.1-Flash agentic benchmark figure from the original Hugging Face card — DeepSeek-AI model card. Source · License: MIT.

Verdict. Both publish ungated MIT multimodal weights with a 1M context figure. Pick DeepSeek-V4.1-Flash-1017 if you want the 552B-backbone / 8B-16B-active Flash family this site already identifies. Pick GLM-5.3-Flash if you want Z.ai’s 320B/18B GLM-5 multimodal MoE (image + video in). We have no sourced bake-off and we do not OCR scores out of GLM’s bench PNG.

Compare GLM-5.3-Flash and DeepSeek-V4.1-Flash.

Dimension	GLM-5.3-Flash	DeepSeek-V4.1-Flash	Source
License	MIT (weight LICENSE + cardData mit)	MIT (1017 LICENSE + README)	Official LICENSE files (2026-10-25 / 2026-10-21)
Gating	No	No	Hugging Face API
Developer	Z.ai	DeepSeek-AI	Official cards
Parameters (vendor README)	320B total / 18B activated	552B backbone; 8B prefill / 16B decode	Official READMEs
Modalities	Text + image + video in	Images + text in, text out	Official cards
Context length	1,048,576 tokens	1M tokens	Official cards / config
Hugging Face likes	2,941 (2026-10-25)	1,970 on -1017; 4,674 on original Flash (2026-10-21)	Hugging Face API
usedStorage	656,694,929,761 bytes (~612 GiB)	510,296,920,807 bytes on 1017 (~475 GiB)	Hugging Face API
Quality vs ChatGPT	Not independently tested	Not independently tested	n/a

When to choose which

Permissive MIT weights: Tie — Both cards we opened are MIT. Companion GitHub trees are a different grant.
Video-in (card modalities): GLM-5.3-Flash — Flash-1017 card we quote is image-text-to-text. Not a quality score.
Already-identified 1017 Flash family: DeepSeek-V4.1-Flash — Official engine on this site is DeepInfer. Different family from GLM-5.

Read the entity pages: GLM-5.3-Flash and DeepSeek-V4.1-Flash (1017 checkpoint). Both are listed on ChatGPT alternatives and closed LLM APIs.

Why this comparison exists

Z.ai publishes GLM-5.3-Flash as an ungated MIT multimodal MoE. This site already identifies DeepSeek-V4.1-Flash-1017. Searchers type both names next to ChatGPT-class APIs. That is why the page exists. It is not a quality ranking.

Sourced snapshot (2026-10-25 / 2026-10-21)

Field	GLM-5.3-Flash	DeepSeek-V4.1-Flash-1017
Host	huggingface.co/zai-org/GLM-5.3-Flash (not gated)	huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017 (not gated)
HF likes / downloads	2,941 / 6,377,661 (2026-10-25)	1,970 / 27,737 (2026-10-21)
License	MIT	MIT
Size (README)	320B / 18B	552B backbone; 8B/16B active
Job	Text + image + video in	Images + text in, text out
Context	1,048,576	1M
usedStorage	~612 GiB	~475 GiB
Quality vs ChatGPT	Not independently tested	Not independently tested

License

DeepSeek: MIT on the 1017 LICENSE and the README sentence that the repository and weights are MIT.

Not legal advice.

How to choose

Need MIT Flash-1017 weights and DeepSeek’s official engine: DeepSeek-V4.1-Flash — DeepInfer (DeepInfer vs vLLM).
Need Z.ai’s 320B/18B multimodal MoE and the GLM-5.3-Flash card: GLM-5.3-Flash.
Need MiniMax’s 428B/23B multimodal MoE and can live with the Community License: MiniMax M3.1 Flash — vs Flash-1017.
Need Xiaomi’s 309B/15B Flash-RL card (MIT tag; no LICENSE blob): MiMo-V2.6-Flash.

See also AI Chat & Agents. Pixel editors remain a different job: PhotoCraft vs GIMP.

FAQ

Did GLM-5.3-Flash beat DeepSeek-V4.1-Flash-1017?

We do not have a sourced side-by-side table that both vendors printed against each other on the cards we opened today. GLM’s README ships a bench figure (bench_53.png); we do not OCR it. DeepSeek 1017 self-reported rows stay on that model page. Not a forkalt ranking.

Which license is more permissive?

Both weight LICENSEs we opened are MIT. GLM’s companion GitHub tree (zai-org/GLM-5) is Apache-2.0; DeepSeek’s DeepInfer engine is Apache-2.0. Not legal advice.

Can I run both locally?

Both publish ungated weight hosts. GLM usedStorage is ~612 GiB; 1017 is ~475 GiB. Datacenter-class. VRAM not independently measured.

Does either replace ChatGPT?

No sourced bake-off against ChatGPT on this site.

Sources & verification

Last verified 2026-10-25 · Last updated 2026-10-25· Edited by forkalt editors · Editorial policy

License notes are not legal advice. Read the original LICENSE. Trademark & disclaimer.