# Is papr-embed-code-1-0.6b SOTA?

Pending benchmarks. Announced Oct 28, 2026; no independent results yet.

As of Nov 3, 2026, papr-embed-code-1-0.6b is pending benchmarks on isitsota: announced Oct 28, 2026, with no independent results on the boards we track yet.

The smaller of Papr's two open code-retrieval embedding models, made by further training Qwen3-Embedding-0.6B on code-retrieval data. Papr says it is flat to worse than its base on non-code retrieval. Weights on Hugging Face under Apache-2.0, and hosted on Papr's embeddings API.

Canonical: https://isitsota.com/m/papr-embed-code-1-0-6b
Last updated: 2026-10-28
Tracking since: 2026-10-28

## Announced facts

| Fact | Value |
| --- | --- |
| Lab | Papr |
| Announced | Oct 28, 2026 |
| API | Live (`papr-embed-code-1-0.6b`) · POST https://memory.papr.ai/v1/embeddings with model papr-embed-code-1-0.6b |
| Open weights | Published · Hugging Face papr-ai/papr-embed-code-1-0.6b, fp32, Apache-2.0 (repo created Oct 19, card added Oct 21) |
| Parameters | 0.6B (fine-tune of Qwen3-Embedding-0.6B) |
| Task | Text embeddings for code retrieval; one dense vector per input, no schema input |
| Base model | Qwen/Qwen3-Embedding-0.6B |
| Embedding size | 1,024 dimensions; last-token pooling, L2-normalized; up to 8,192 tokens |
| Languages | English and code only |
| Training data note | Trained on the train splits of all 10 CoIR tasks and SciFact, so CoIR and SciFact are in-domain, per Papr |

Tags marked unsure are not stated on the lab's own pages.

## What would let us call it

Verdicts use only independent boards we already track. Two consecutive snapshots are required before any crown.

- [x] Publicly announced with a source
- [x] Usable by the public (API or weights)
- [ ] Listed on an independent board we track
- [ ] Enough results for a rank in a category
- [ ] Holds across two snapshots (crowns only)

## What the lab claims

Self-reported. Shown for context; not used in any verdict.

- Lab claim: CoIR mean nDCG@10 of 77.19 over 10 tasks (in-domain, per Papr), run with stock mteb 2.10 in fp32.
- Lab claim: on held-out retrieval tasks it trails Qwen3-Embedding-0.6B on ArguAna, SciDocs and TREC-COVID (82.70 vs 89.30) and gains on NFCorpus, FiQA and CodeEditSearch.

## Sources

- REPO: [papr-ai/papr-embed-code-1-0.6b on Hugging Face](https://huggingface.co/papr-ai/papr-embed-code-1-0.6b)
- REPO: [Papr-ai/papr-embed-evals on GitHub](https://github.com/Papr-ai/papr-embed-evals)
- PRESS: [Shawkat Kabbara on X: papr-embed release article](https://x.com/shawkatkabbara/status/2115476518088716604) (2026-10-28)

Tracking continues until an independent board lists it.

## Related

- Lab: [Papr](https://isitsota.com/labs/papr.md)
- All pending releases: https://isitsota.com/releases.md
- JSON: https://api.isitsota.com/v1/models/papr-embed-code-1-0-6b