Ingot-8B-R3

Embedding models have a simple job: compress language into dense vectors where proximity means meaning. They make semantic search function by intent rather than keyword matching, allowing retrieval-augmented generation pipelines to locate exact context across millions of documents. The quality of this embedding layer defines the operational ceiling for every downstream retrieval and reasoning step.

Ingot-8B-R3 is a text embedding model built by Jonathan Corners at Voxell. It is based on Qwen/Qwen3-Embedding-8B and extended with a proprietary routing framework. Different specialists activate at inference time from the input content alone, requiring no task metadata or manual routing flags. The routing work is proprietary to Voxell and patent-pending.

As measured on MTEB(eng, v2), Ingot-8B-R3 achieves the highest Mean (Task) score of any English embedding model developed in the United States at the time of evaluation.

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The Solo Contrast

If you look at the top of the MTEB leaderboard, you will see a familiar pattern. Most of the top entries are backed by state-funded research consortiums, sprawling hyperscale labs, and deep academic teams. Many of those cards end with a corporate WeChat handle or QR code for support.

We do not have a WeChat handle, a corporate campus, or an army of PhDs. Ingot was built by a single engineer, working from home on a tight budget of private consumer GPUs. We won on data engineering, not on computing scale.

Access is gated. Weights are available on request for academic evaluation and verification. Use the Request Access button above to describe your use case.

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Performance

Evaluated on MTEB(eng, v2), a 41-task English benchmark covering retrieval, semantic textual similarity, classification, clustering, pair classification, reranking, and summarization. The base model, Qwen3-Embedding-8B, scores 75.23 Mean (Task) on this benchmark.

Metric	Score
Mean (Task)	75.99
Mean (Category)	69.9958
Borda Points	5567

Borda scoring ranks each model against the full leaderboard cohort on every task, then sums those rank points. It rewards consistent representation quality across the entire task distribution rather than optimization peaks on a few specific datasets.

By Category

Reranking and Summarization in MTEB(eng, v2) are single-task categories. These scores reflect one dataset each, not a category average.

Category	Tasks	Mean
Classification	8	90.41
STS	9	89.32
PairClassification	4	87.66
Retrieval	10	70.01
Clustering	8	58.47
Summarization	1	36.96
Reranking	1	32.84

Per-task results are published in the mteb/results dataset.

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Architecture

Attribute	Specification
Base Model	Qwen/Qwen3-Embedding-8B
Output Dimension	4096 (float32)
Max Sequence Length	32,768 tokens
Similarity Metric	Cosine

The routing logic, specialist checkpoints, and dispatch thresholds are proprietary to Voxell and are not included in this repository.

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Usage

Ingot-8B-R3 is served through the Voxell Forge API at api.voxell.ai. No weights download is required. Request an API key using the Request Access button at the top of this page, or contact corp@voxell.ai. Set VOXELL_API_KEY in your environment, then:

import os
from openai import OpenAI

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

response = client.embeddings.create(
    model="JCorners/Ingot-8B-R3",
    input=["Example sentence"],
)
print(len(response.data[0].embedding))  # 4096

The API is OpenAI-compatible. Output is a 4096-dimensional float32 vector. Cosine similarity is the correct distance function. The proprietary routing layer activates the correct specialist for your input at runtime — no additional configuration is required.

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What ships, and what does not

Ingot-8B-R3 is a research instrument: a frontier-grade embedder built and tuned to the shape of MTEB(eng, v2). The advances that generalize to production retrieval, including large-scale document corpora, structure preservation, hierarchical tables, source code, and Abstract Syntax Trees, ship in Forge.

You can try the Forge embedding API right now with no signup required. Paste any text to see it transformed into a vector and copy production-ready integration snippets in under sixty seconds at playground.voxell.ai. Creating a developer account awards an initial grant of 10,000,000 free tokens to build on.

The benchmark is the proof. Forge is the point.

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Contact and Connections

• Weights access: Use the gated access request button at the top of this card.
• Professional networking: Connect with Jonathan Corners on LinkedIn for corporate, venture, and engineering updates.
• Partnerships and commercial licensing: corp@voxell.ai or voxell.ai.
• Technical essays: Deep-dives on synthetic data and routing mechanics at sentimark.ai.