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ms-marco-MiniLM-L12-v2 vs ms-marco-MiniLM-L6-v2

Ms-marco-MiniLM-L12-v2 has 33M parameters and ms-marco-MiniLM-L6-v2 has 23M parameters; both are released under Apache License 2.0; at 16-bit, ms-marco-MiniLM-L12-v2 needs about 0.1 GB (1x MI300X from $1.85 an hour) and ms-marco-MiniLM-L6-v2 about 0.1 GB (1x MI300X from $1.85 an hour).

Published metadata for 2 models, each read from its own repository.
Field ms-marco-MiniLM-L12-v2
cross-encoder/ms-marco-MiniLM-L12-v2
ms-marco-MiniLM-L6-v2
cross-encoder/ms-marco-MiniLM-L6-v2
Publisher Sentence Transformers - Cross-Encoders Sentence Transformers - Cross-Encoders
Task Text ranking Text ranking
Modality Other Other
Parameters, as reported 33M parameters 23M parameters
Architecture BertForSequenceClassification BertForSequenceClassification
Library sentence-transformers sentence-transformers
Context length 512 tokens 512 tokens
Repository size 1.3 GB 890.2 MB
Artifact formats safetensors, onnx, openvino, pytorch, jax safetensors, onnx, openvino, pytorch, jax
License apache-2.0 apache-2.0
Access Open weights, no gate Open weights, no gate
Memory at 16-bit (weights and margin) 0.1 GB 0.1 GB
Cheapest GPUs at 16-bit, per hour 1x MI300X, $1.85 1x MI300X, $1.85
Memory at 4-bit (weights and margin) 0 GB 0 GB
Cheapest GPUs at 4-bit, per hour 1x MI300X, $1.85 1x MI300X, $1.85
Revision viewed 7b0235231ca2 233902d25c44
Downloads reported by the hub 1.8M 88.6M
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 ms-marco-MiniLM-L12-v2

Put this behind the search box, after retrieval. A cross-encoder scores a query against each candidate passage, so you pull candidates with something like Elasticsearch and let this model order them. Trained on the MS MARCO passage ranking task, it carries 33 million parameters and needs 0.1 GB of memory at 16-bit. A 192 GB MI300X at $1.85 an hour, the cheapest fit we list, is a mismatch of scale, so this rides on a card already doing other work.

The license is Apache 2.0, with the usual obligations: keep the license and NOTICE file attached and state significant changes. Start with the 512 token limit: query plus passage must fit inside it, so long passages get chunked before scoring. Second, lineage: it derives from microsoft/MiniLM-L12-H384-uncased and trained on sentence-transformers/msmarco, so test ranking on your own documents. It ships in safetensors, ONNX, OpenVINO, PyTorch and JAX formats.

SAVRN's Notes on ms-marco-MiniLM-L6-v2

Once a first-pass search has returned its candidates, this cross-encoder puts them in order, reading the query with each passage and handing them back sorted. At 23M parameters it needs 0.1 GB at 16-bit and rounds to nothing at 8-bit or 4-bit. For $1.85 an hour on demand, the cheapest setup we price is a 192 GB MI300X, a card this ranker would leave almost empty; its sensible home is a corner of a GPU you already pay for.

Apache 2.0 puts no fence around commercial use, so you can retrain it on your query logs and redistribute the result, keeping the license and NOTICE files intact and stating what changed. Two checks: the 512-token context must hold the query and the passage together, and the lineage runs to cross-encoder/ms-marco-MiniLM-L12-v2 with sentence-transformers/msmarco as the training set. Access is open and the files were last updated 2026-08-09.

Questions

Which is larger, ms-marco-MiniLM-L12-v2 or ms-marco-MiniLM-L6-v2?

ms-marco-MiniLM-L12-v2 (33M parameters) is larger than ms-marco-MiniLM-L6-v2 (23M parameters), by the parameter counts their publishers report.

Which is cheaper to run, ms-marco-MiniLM-L12-v2 or ms-marco-MiniLM-L6-v2?

At 4-bit, ms-marco-MiniLM-L12-v2 fits on 1x MI300X from $1.85 an hour and ms-marco-MiniLM-L6-v2 on 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Can I use ms-marco-MiniLM-L12-v2 commercially?

Yes. ms-marco-MiniLM-L12-v2 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.

Can I use ms-marco-MiniLM-L6-v2 commercially?

Yes. ms-marco-MiniLM-L6-v2 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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