---
title: "mxbai-rerank-v3.1-listwise"
description: "Meet listwise v3.1, a small upgrade to listwise v3. Now in Mixedbread Search, matching gpt-5.6-sol (high) quality at 61x lower latency."
date: 2026-07-27
authors:
  - Mixedbread Team
tags:
  - Research
  - Product
tag: blog
url: /blog/mxbai-rerank-v3-1-listwise
featured: false
---

**mxbai-rerank-v3.1-listwise** is now available as the default reranker.

It delivers gpt-5.6-sol-level ranking quality with substantially improved latency over [mxbai-rerank-v3-listwise](https://mixedbread.com/blog/listwise-rerank). It is excellent at complex tasks like recency-aware ranking, source-priority resolution, and multi-step composite instructions, compared with leading LLMs and pointwise rerankers.

> **Figure**: Quality versus latency on ViDoRe v3: mxbai-rerank-v3.1-listwise reaches gpt-5.6-sol quality at roughly 61 times lower latency, while Cohere and the GPT-5.6 rerankers are slower.

*Sampled queries per ViDoRe subset with Mixedbread base search (Wholembed v3) as first stage.*

| Reranker on top of the first stage | NDCG@10 (ViDoRe v3, sampled queries) | Latency per query |
| --- | --- | --- |
| First stage only (Wholembed v3) | 0.62 | 0.10 s |
| + cohere rerank-4-pro | 0.66 | 1.6 s |
| + gpt-5.6-terra (high) | 0.70 | 36 s |
| + gpt-5.6-sol (high) | 0.70 | 40 s |
| + gpt-5.6-luna (high) | 0.70 | 73 s |
| **+ mxbai-rerank-v3.1-listwise** | **0.71** | **0.65 s** |

Higher NDCG@10 and lower latency are better. Values are approximate readings from the chart above. The 61× figure compares gpt-5.6-sol's latency with v3.1's.

## Excellent and faster

Unlike pointwise rerankers measuring the relevance of individual documents, our listwise v3 reads the whole candidate set for better reasoning at the cost of higher ranking latency.

Now, by rewriting its underlying inference engine, v3.1 reranks faster across every input size, matching pointwise rerankers. In production, the speedup ranges from ~25% on typical queries to ~54% on long-tail inputs (64–128k tokens).

> **Figure**: Median reranking latency by input size: mxbai-rerank-v3.1-listwise is faster than v3 at every input size.

| Input size (tokens) | v3 median latency | v3.1 median latency | Speedup |
| --- | --- | --- | --- |
| < 8k | 159 ms | 119 ms | 25% |
| 8–16k | 258 ms | 188 ms | 27% |
| 16–32k | 380 ms | 243 ms | 36% |
| 32–64k | 611 ms | 348 ms | 43% |

Median reranking latency in production, lower is better. Values are approximate readings from the chart above. The ~54% speedup mentioned in the text is for 64–128k-token inputs, which the chart does not show.

For better threshold cutoffs, we also changed v3.1 to return **content-dependent relevance scores** instead of a fixed rank-based ladder in v3.

## Try it now

mxbai-rerank-v3.1-listwise is available today through Mixedbread Search:

**Python:**
```python
from mixedbread import Mixedbread

    client = Mixedbread()

    results = client.stores.search(
        store_identifiers=["my-store"],
        query="when is my flight to London? The most recent valid booking wins",
        search_options={
          "rerank": {
              "model": "mixedbread-ai/mxbai-rerank-v3.1-listwise",
          }
        },
    )
```

**TypeScript:**
```typescript
import { Mixedbread } from "@mixedbread/sdk";

    const client = new Mixedbread();

    const results = await client.stores.search({
        store_identifiers: ["my-store"],
        query: "when is my flight to London? The most recent valid booking wins",
        search_options: {
          rerank: {
              model: "mixedbread-ai/mxbai-rerank-v3.1-listwise",
          }
        },
    });
```