SAVRN Model Hub · Comparisons
all-MiniLM-L6-v2 vs paraphrase-MiniLM-L6-v2
All-MiniLM-L6-v2 has 23M parameters and paraphrase-MiniLM-L6-v2 has 23M parameters; both are released under Apache License 2.0; at 16-bit, all-MiniLM-L6-v2 needs about 0.1 GB (1x MI300X from $1.85 an hour) and paraphrase-MiniLM-L6-v2 about 0.1 GB (1x MI300X from $1.85 an hour).
| Field | all-MiniLM-L6-v2 sentence-transformers/all-MiniLM-L6-v2 | paraphrase-MiniLM-L6-v2 sentence-transformers/paraphrase-MiniLM-L6-v2 |
|---|---|---|
| Publisher | Sentence Transformers | Sentence Transformers |
| Task | Sentence similarity | Sentence similarity |
| Modality | Text | Text |
| Parameters, as reported | 23M parameters | 23M parameters |
| Architecture | BertModel | BertModel |
| Library | sentence-transformers | sentence-transformers |
| Context length | 512 tokens | 512 tokens |
| Repository size | 976.9 MB | 886.0 MB |
| Artifact formats | safetensors, onnx, openvino, pytorch, rust, tf | safetensors, onnx, openvino, pytorch, tf |
| 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 | 1110a243fdf4 | c9a2bfebc254 |
| Downloads reported by the hub | 255.1M | 1.6M |
| 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.
Other Reported Results
These results are listed for each model on its own, because the conditions needed to compare them are not stated or do not match. Two results that leave a condition blank are not assumed to share it.
all-MiniLM-L6-v2
| Benchmark | Conditions | Result | Reported by | Revision | Date |
|---|---|---|---|---|---|
| mteb/arguana | Task ArguAnaMetric ArguAnaSetup Obtained using MTEB v1.12.75Comparison conditions not established | 50.167 | Obtained using MTEB v1.12.75 Reported by a third party |
Evaluated revision not stated | 2026-03-05 |
| mteb/arguana | Task ArguAna_default_testMetric ArguAna_default_testSetup Obtained using MTEB v1.12.75Comparison conditions not established | 50.167 | Obtained using MTEB v1.12.75 Reported by a third party |
Evaluated revision not stated | 2026-03-05 |
SAVRN's Notes on all-MiniLM-L6-v2
Sentence embedding sits at the front of almost every retrieval pipeline we run, and the memory line tells you where this model belongs: 0.1 GB at 16-bit, rounding to zero at 8-bit and 4-bit. The cheapest on-demand setup we price is one MI300X with 192 GB at $1.85 an hour, so nobody rents a card for this alone. It rides in the spare memory of whatever GPU already serves your main model, 23M parameters producing a 384-wide vector.
Apache 2.0 lets you fine-tune it and ship the result commercially, provided the license, copyright and NOTICE files travel with it and significant changes are marked. Check the 512-token context against your document lengths, since longer text gets split first, and read the lineage: nreimers/MiniLM-L6-H384-uncased as the source model and seven training sets, including ms_marco, s2orc and code_search_net. No host prices it by the token in our Index.
SAVRN's Notes on paraphrase-MiniLM-L6-v2
Semantic search and clustering are the jobs here: the model turns a sentence or paragraph into a 384-dimensional vector, and everything downstream compares vectors. With 23 million parameters, the 16-bit memory need rounds to 0.1 GB and the weights round to zero. The cheapest configuration on file, one MI300X with 192 GB at $1.85 per hour on-demand, says the cost driver is not the model but how many documents you push through it.
Under Apache 2.0 you can embed proprietary documents, keep the vectors, fine-tune on your own sentence pairs and redistribute commercially, as long as the notices travel with it and significant changes are stated. Check the context length first: 512 tokens, so longer passages need chunking, and the chunking will shape results more than the model does. Then use a format the publisher already ships, safetensors, ONNX, OpenVINO, PyTorch or TensorFlow.
Questions
Which is larger, all-MiniLM-L6-v2 or paraphrase-MiniLM-L6-v2?
all-MiniLM-L6-v2 (23M parameters) is larger than paraphrase-MiniLM-L6-v2 (23M parameters), by the parameter counts their publishers report.
Which is cheaper to run, all-MiniLM-L6-v2 or paraphrase-MiniLM-L6-v2?
At 4-bit, all-MiniLM-L6-v2 fits on 1x MI300X from $1.85 an hour and paraphrase-MiniLM-L6-v2 on 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use all-MiniLM-L6-v2 commercially?
Yes. all-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.
Can I use paraphrase-MiniLM-L6-v2 commercially?
Yes. paraphrase-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.