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SAVRN Model Hub · Comparisons

CLIP-ViT-L-14-laion2B-s32B-b82K vs siglip2-base-patch16-naflex

CLIP-ViT-L-14-laion2B-s32B-b82K has 428M parameters and siglip2-base-patch16-naflex has 375M parameters; CLIP-ViT-L-14-laion2B-s32B-b82K is released under MIT License and siglip2-base-patch16-naflex under Apache License 2.0; at 16-bit, CLIP-ViT-L-14-laion2B-s32B-b82K needs about 1 GB (1x MI300X from $1.85 an hour) and siglip2-base-patch16-naflex about 0.9 GB (1x MI300X from $1.85 an hour).

Published metadata for 2 models, each read from its own repository.
Field CLIP-ViT-L-14-laion2B-s32B-b82K
laion/CLIP-ViT-L-14-laion2B-s32B-b82K
siglip2-base-patch16-naflex
google/siglip2-base-patch16-naflex
Publisher LAION eV Google
Task Zero shot image classification Zero shot image classification
Modality Other Other
Parameters, as reported 428M parameters 375M parameters
Architecture CLIPModel Siglip2Model
Library open_clip transformers
Context length 77 tokens Not stated
Repository size 6.8 GB 1.5 GB
Artifact formats safetensors, pytorch, tensorboard safetensors
License mit apache-2.0
Access Open weights, no gate Open weights, no gate
Memory at 16-bit (weights and margin) 1 GB 0.9 GB
Cheapest GPUs at 16-bit, per hour 1x MI300X, $1.85 1x MI300X, $1.85
Memory at 4-bit (weights and margin) 0.3 GB 0.2 GB
Cheapest GPUs at 4-bit, per hour 1x MI300X, $1.85 1x MI300X, $1.85
Revision viewed 162703219714 b53b807d3a2d
Downloads reported by the hub 4M 953.8k
Last observed 2026-09-18 2026-09-18

An evaluation row appears only where at least two of these models report the same benchmark with the same stated configuration, metric, unit and setup. Different evaluators stay named in each cell. Values are shown as reported: no unit conversion, no ranking.

SAVRN's Notes on CLIP-ViT-L-14-laion2B-s32B-b82K

Hand it a photo and a few label phrases you just wrote, and it tells you which phrase fits, with no training run; that is zero-shot image classification. The text side stops at 77 tokens, so labels are phrases, not paragraphs. At 16-bit the 428M parameters weigh 0.9 GB and the run needs 1.0 GB, a rounding error on the 192 GB MI300X we price at $1.85 an hour, so it shares a card rather than owning one.

MIT permits commercial use, modification and redistribution with the notices kept, but weigh the publisher's framing too: LAION eV calls this a research output for research communities, trained on the LAION-2B English subset of LAION-5B with OpenCLIP. Before committing, run that data lineage through your governance review, check that English training text suits your labels, and decide whether a checkpoint released September 14, 2022 is current enough for your pipeline.

SAVRN's Notes on siglip2-base-patch16-naflex

Google built this encoder for three jobs: sorting images against text labels with no task-specific training, matching images to captions for retrieval, and serving as the vision tower inside a larger vision-language model. At 375 million parameters it needs 0.9 GB of memory at 16-bit precision, so hardware choice is about what else is on the card. The cheapest card in our table, one MI300X with 192 GB at $1.85 an hour on demand, carries it alongside the language model it feeds.

Apache 2.0 clears commercial use, modification and redistribution with the notices kept. Three papers describe the method, arXiv 2502.14786, 2303.15343 and 2209.06794, and the first titles it a multilingual vision-language encoder, which matters if your labels are not in English. The release is February 2025, so confirm your inference stack supports the siglip2 model type before ordering hardware.

Questions

Which is larger, CLIP-ViT-L-14-laion2B-s32B-b82K or siglip2-base-patch16-naflex?

CLIP-ViT-L-14-laion2B-s32B-b82K (428M parameters) is larger than siglip2-base-patch16-naflex (375M parameters), by the parameter counts their publishers report.

Which is cheaper to run, CLIP-ViT-L-14-laion2B-s32B-b82K or siglip2-base-patch16-naflex?

At 4-bit, CLIP-ViT-L-14-laion2B-s32B-b82K fits on 1x MI300X from $1.85 an hour and siglip2-base-patch16-naflex on 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Can I use CLIP-ViT-L-14-laion2B-s32B-b82K commercially?

Yes. CLIP-ViT-L-14-laion2B-s32B-b82K is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.

Can I use siglip2-base-patch16-naflex commercially?

Yes. siglip2-base-patch16-naflex is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

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