Ingot-8B-R3

An 8B English text-embedding model with per-task instruction routing and per-cluster specialists, served as an API. Built by Jonathan Corners at Voxell.

• 🏠 Home: https://voxell.ai
• 📝 Engineering write-up — Ingot Poured: https://voxell.ai/engineering/ingot_poured/
• 🧪 Technical essays: https://sentimark.ai
• 🔥 Use it — 10M tokens free, then pay-as-you-go / select a plan: https://voxell.ai/forge
• 🎮 Playground: https://playground.voxell.ai/
• 💼 LinkedIn: Jonathan Corners · Voxell
• ✉️ Contact: corp@voxell.ai

Access model. This is a proprietary, API-only model — no weights are distributed here. To use it, sign up at voxell.ai/forge — 10M tokens free, then pay-as-you-go / select a plan. MTEB reviewers can get a free per-reviewer key to reproduce the benchmark — see Access & usage below.

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Overview

Base model	Qwen/Qwen3-Embedding-8B (rev 1d8ad4ca…)
Embedding dimension	4096 (float32)
Max sequence length	32,768 tokens
Similarity	Cosine
Language	English (eng-Latn)
Parameters	7.57B total · 621M embedding
Serving	OpenAI-compatible /v1/embeddings

Ingot-8B-R3 wraps the Qwen3-Embedding-8B backbone with a routing framework that selects per-task instructions and activates per-cluster specialist experts behind a single embeddings endpoint. The routing logic is proprietary (provisional patent filed 2026) and is not included here; the model is consumed through the API. Full method notes are in the engineering write-up: Ingot Poured.

Results — MTEB(eng, v2)

Metric	Score
Mean (Task)	75.9795
Mean (Category)	69.9958
Borda points	5567

Category	Score
Classification	90.41
STS	89.32
PairClassification	87.66
Retrieval	70.01
Clustering	58.47
Summarization	36.96
Reranking	32.84

At evaluation time this was, to our knowledge, the highest Mean (Task) on MTEB(eng, v2) among English embedding models developed in the United States. Built by a single engineer on consumer GPUs — the emphasis is data engineering over compute scale (see the write-up).

Results are submitted to embeddings-benchmark/results and the registry PR to embeddings-benchmark/mteb is open for review.

Access & usage

Production use → voxell.ai/forge

For any real / production use, get an API key at voxell.ai/forge: 10M tokens free, then pay-as-you-go or select a plan. OpenAI-compatible endpoint, no weights to host.

MTEB benchmark reproduction

For reviewers reproducing MTEB(eng, v2), a separate audited eval endpoint is served at https://api-mteb.voxell.ai with an OpenAI-compatible body shape.

1. Get a key (~30 s, self-serve, no email round-trip): Visit https://api-mteb.voxell.ai/request-key, enter your email, complete the Cloudflare challenge, and your key appears on screen with a prefilled curl example. 200 requests/minute per reviewer.

2. Check status without a key:

curl -sS https://api-mteb.voxell.ai/v1/models
# → {"object":"list","data":[{"id":"jcorners/ingot-8b-r3","object":"model","owned_by":"voxell"}]}

3. Embed:

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["MTEB_API_KEY"],
    base_url="https://api-mteb.voxell.ai/v1",
)

resp = client.embeddings.create(
    model="jcorners/ingot-8b-r3",
    input=["Example sentence"],
)
print(len(resp.data[0].embedding))  # 4096

Or with curl (note task_name for instruction routing):

curl -sS https://api-mteb.voxell.ai/v1/embeddings \
  -H "Authorization: Bearer $MTEB_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"jcorners/ingot-8b-r3","input":["hello world"],"task_name":"STS12"}'

Use from MTEB

The model registers as jcorners/ingot-8b-r3 via an API loader (mteb/models/model_implementations/ingot_models.py). Set MTEB_API_KEY and run MTEB normally.

Training data disclosure

training_datasets is declared in full on the ModelMeta — two groups: datasets inherited from the Qwen3-Embedding-8B base, and the MTEB train splits used by Ingot R3 specialists (SprintDuplicateQuestions, TwitterSemEval2015, TwitterURLCorpus, MedrxivClusteringP2P.v2, MedrxivClusteringS2S.v2). Ingot's own synthetic training data is generated through a source-document-level leakage gate, so MTEB eval-set documents are quarantined by construction.

Citation

If you use this model, please reference the engineering write-up Ingot Poured and Voxell (https://voxell.ai).

The MTEB benchmark: MMTEB: Massive Multilingual Text Embedding Benchmark.