vectara · pwoznic · Oct 1, 2025 · Copilot · Oct 1, 2025
diff --git a/www/docs/api-reference/search-apis/reranking.md b/www/docs/api-reference/search-apis/reranking.md
@@ -10,7 +10,6 @@ import {vars} from '@site/static/variables.json';
 
 import CodePanel from '@site/src/theme/CodePanel';
 
-
 Initial search results often fail to capture nuanced relevance or diversity, 
 potentially leading to suboptimal user experiences. Utilizing Vectara's 
 reranking can significantly enhance the quality and usefulness of 
@@ -23,17 +22,24 @@ more accurate results.
 
 ## Available rerankers
 
-Vectara currently provides the following rerankers: 
+Vectara offers multiple reranking models that enable you to choose the best one
+that for your data and use case. You can evaluate different models
-that for your data and use case. You can evaluate different models
+for your data and use case. You can evaluate different models
-that for your data and use case. You can evaluate different models
+for your data and use case. You can evaluate different models
+against your own dataset to determine which provides optimal results for your
+domain and accuracy and latency requirements.
 
-* [**Multilingual Reranker v1**](/docs/learn/vectara-multi-lingual-reranker) (`type=customer_reranker` and `reranker_name=Rerank_Multilingual_v1`) 
-  also known as Slingshot, provides more accurate neural ranking than the 
-  initial Boomerang retrieval. While computationally more expensive, it offers 
-  improved text scoring across a wide range of languages, making it suitable 
-  for diverse content.
-* [**Maximal Marginal Relevance (MMR) Reranker**](/docs/learn/mmr-reranker) (`type=mmr`) 
-  for diversifying results while maintaining relevance.
-* [**User Defined Function Reranker**](/docs/learn/user-defined-function-reranker) (`type=userfn`) for 
-  custom scoring based on metadata.
+| Reranker Name | API Name | Description |
+|--------------|----------|-------------|
+| **Qwen3 Reranker** (default) | `qwen3-reranker` | High-performance multilingual neural reranker optimized for accuracy. In many benchmarks, Qwen3 demonstrates strong performance, though results vary by dataset. |
+| **Mixbread Reranker** | `mxbai-rerank-base-v2` | Efficient production-friendly model offering a good balance between speed and accuracy. |
+| [**Multilingual Reranker v1**](/docs/learn/vectara-multi-lingual-reranker) (Slingshot) | `Rerank_Multilingual_v1` | Neural reranker providing more accurate ranking than initial Boomerang retrieval. While computationally more expensive, it offers improved text scoring across a wide range of languages. |
+| [**Maximal Marginal Relevance (MMR) Reranker**](/docs/learn/mmr-reranker) | `type=mmr` | Diversifies results while maintaining relevance. |
+| [**User Defined Function Reranker**](/docs/learn/user-defined-function-reranker) | `type=userfn` | Applies custom scoring based on metadata or business rules. |
+
+:::tip
+To enable reranking in the Vectara console, navigate to the
+Query tab of a corpus and select **Retrieval**. Use this for exploration
+and experimenting with the API.
+:::
 
 ### Chain reranking
 
@@ -51,32 +57,66 @@ precision while maintaining recall.
 
 ## Enable reranking
 
-To enable reranking, specify the appropriate value for the `type` in the 
-`reranker` object. For the MMR reranker, use `mmr`. In most scenarios, 
-it makes sense to use the default query `start` value of `0` so that you're 
-reranking all of the best initial results. You can also set the  `limit` of the 
-`query` to the total number of documents you wish to rerank. The default value 
+To enable reranking, specify the appropriate value for the `type` in the
+`reranker` object. For the MMR reranker, use `mmr`. In most scenarios,
+it makes sense to use the default query `start` value of `0` so that you're
+reranking all of the best initial results. You can also set the  `limit` of the
+`query` to the total number of documents you wish to rerank. The default value
 is `25`.
 
-The following example shows the `limit` and `type` values in a query. Note that 
+The following example shows the `limit` and `type` values in a query. Note that
 this simplified example intentionally omits several parameter values.
 
 <CodePanel snippets={[{language: "json", code: `{
-  "query": "What is my question?",
-  "stream_response": false,
-  "search": {
-    "start": 0,
-    "limit": 25,
-    "context_configuration": {},
-    },
-    "reranker": {
-          "type": "mmr",
-          "diversity_bias": "0.4"
-    },
-  "generation": [],
-  "enable_factual_consistency_score": true
+   "query": "What is my question?",
+   "stream_response": false,
+   "search": {
+     "start": 0,
+     "limit": 25,
+     "context_configuration": {},
+     },
+     "reranker": {
+           "type": "mmr",
+           "diversity_bias": "0.4"
+     },
+   "generation": [],
+   "enable_factual_consistency_score": true
 }`}]} title="Code Example" layout="stacked" />
 
+### Using neural rerankers
+
+For neural rerankers like Qwen3, Mixbread, or Multilingual v1, use
+`type=customer_reranker` and specify the `reranker_name`.
+
+<CodePanel snippets={[{language: "json", code: `{
+   "query": "What is quantum computing?",
+   "reranker": {
+     "type": "customer_reranker",
+     "reranker_name": "qwen3-reranker"
+   }
+}`}]} title="Query with Qwen3 Reranker" layout="stacked" />
+
+<CodePanel snippets={[{language: "json", code: `{
+   "query": "What is quantum computing?",
+   "reranker": {
+     "type": "customer_reranker",
+     "reranker_name": "mxbai-rerank-base-v2"
+   }
+}`}]} title="Query with Mixbread Reranker" layout="stacked" />
+
+## Best practices
+
+When working with multiple rerankers, consider the following best practices:
+
+* **Experimentation**: Each reranker behaves differently depending on your
+  content and queries. Evaluate each reranker on your own dataset to determine
+  which provides the best results for your specific use case.
+* **Latency vs. accuracy**: Larger models like Qwen3 tend to provide more
+  accurate results but can add more latency compared to smaller models like
+  Mixbread. Test both models to find the right balance for your application.
+* **Fallback handling**: Ensure your application handles reranker errors
+  gracefully and can fall back to retrieval-only results if a reranker fails
+  or times out.
 
 ## Search cutoffs 
 
@@ -92,9 +132,9 @@ level of relevance. For example, when you set the `cutoff` to `0.5`, only result
 with a score of `0.5` or higher are considered. For example:
 
 <CodePanel snippets={[{language: "json", code: `"reranker": {
-  "type": "customer_reranker",
-  "reranker_name": "Rerank_Multilingual_v1",
-  "cutoff": 0.5
+   "type": "customer_reranker",
+   "reranker_name": "Rerank_Multilingual_v1",
+   "cutoff": 0.5
 }`}]} title="Code Example" layout="stacked" />
 When a reranker is applied with a cutoff, it performs the following steps:
 
@@ -111,10 +151,11 @@ cutoff is applied first, followed by the limit.
 :::
 
 :::caution
-Search cutoffs are most effective when used with neural rerankers like 
-the Vectara Multilingual reranker (Slingshot). This provides normalized 
-scores between 0 and 1. If you use hybrid search methods that involve BM25, 
-scores may be unbounded, making cutoff values less predictable.
+Search cutoffs are most effective when used with neural rerankers like
+Qwen3, Mixbread, or the Vectara Multilingual reranker (Slingshot), which
+provide normalized scores between 0 and 1. If you use hybrid search methods
+that involve BM25, scores may be unbounded, making cutoff values less
+predictable.
 :::
 
 ## Search limits
@@ -204,8 +245,4 @@ only highly relevant and recent documents for summarization.
 2. The next stage prioritizes documents based on their `publish_ts` value, 
    which represents the publication timestamp.
 
-:::tip
-You can also enable reranking in the Vectara console after navigating to the 
-Query tab of a corpus and selecting **Retrieval**. Use this for exploration 
-and experimenting with the API.
-:::
+
diff --git a/www/docs/learn/knee-reranking.md b/www/docs/learn/knee-reranking.md
@@ -15,17 +15,17 @@ distributions across queries. Knee reranking addresses this challenge by
 detecting natural boundaries between relevant and irrelevant results 
 automatically.
 
-Knee reranking combines statistical analysis with configurable parameters 
-to provide intelligent, adaptive filtering. Designed specifically to work 
-after the Slingshot reranker, it analyzes score patterns to identify 
-significant drops in relevance while maintaining safeguards against 
-over-aggressive filtering. For more details about how this reranker works, see 
+Knee reranking combines statistical analysis with configurable parameters
+to provide intelligent, adaptive filtering. Designed specifically to work
+after the Slingshot reranker, it analyzes score patterns to identify
+significant drops in relevance while maintaining safeguards against
+over-aggressive filtering. For more details about how this reranker works, see
 this [**blog post**](https://www.vectara.com/blog/introducing-the-knee-reranking-smart-result-filtering-for-better-results).
 
 ## Enable knee reranking
 
-Enable knee reranking by adding it your reranking chain after the Slingshot 
-reranker. The default settings balance precision and recall, making them 
+Enable knee reranking by adding it your reranking chain after the Slingshot
+reranker. The default settings balance precision and recall, making them
 suitable for most use cases.
 
 <CodePanel snippets={[{language: "json", code: `{
@@ -38,6 +38,18 @@ suitable for most use cases.
   }
 }`}]} title="Default Configuration Example" layout="stacked" />
 
+Knee reranking also works with the new neural rerankers like Qwen3 and Mixbread:
+
+<CodePanel snippets={[{language: "json", code: `{
+  "reranker": {
+    "type": "chain",
+    "rerankers": [
+      { "type": "customer_reranker", "reranker_name": "qwen3-reranker" },
+      { "type": "userfn", "user_function": "knee()", "cutoff": 0.5 }
+    ]
+  }
+}`}]} title="Knee Reranking with Qwen3" layout="stacked" />
+
 Customize the behavior of knee reranking through two key parameters:
 
 * **Sensitivity:** Controls how sharply the score must drop to identify a cutoff.

diff --git a/www/docs/learn/vectara-multi-lingual-reranker.md b/www/docs/learn/vectara-multi-lingual-reranker.md
@@ -11,28 +11,30 @@ import {vars} from '@site/static/variables.json';
 import CodePanel from '@site/src/theme/CodePanel';
 
 
-Generative AI applications often struggle with ranking the most relevant 
-information, leading to hallucinations and irrelevant responses. The new 
-Vectara Multilingual Reranker V1, also known as Slingshot, is a 
-state-of-the-art reranking model that significantly enhances the precision of 
-retrieved results. Providing advanced neural ranking, it refines the output of 
-initial models like [Boomerang](https://vectara.com/blog/introducing-boomerang-vectaras-new-and-improved-retrieval-model/), 
-offering even more accurate document scoring and response quality in Retrieval 
-Augmented Generation (RAG) pipelines.
-
-The Vectara Multilingual Reranker operates as a second-pass refinement tool, 
-building on Boomerang’s high-recall capabilities. While Boomerang quickly 
-retrieves a broad set of relevant documents, the Multilingual Reranker 
-delivers more precise results, ensuring that the top-ranked documents are the 
-most relevant. This reranker also excels across both English and multilingual 
-datasets, making it a powerful tool for global use cases.
-
-While more computationally expensive and introducing some additional latency, 
-the multilingual reranker improves neural ranking beyond Boomerang’s initial 
-selection by providing more precise text scoring. Think of the Slingshot 
-reranker as a "better Boomerang" for refining results, with the multilingual 
-capability serving primarily as a differentiator from other rerankers in the 
-market, which are often English-only.
+Generative AI applications often struggle with ranking the most relevant
+information, leading to hallucinations and irrelevant responses. The Vectara
+Multilingual Reranker V1, also known as Slingshot, is a neural reranking model
+that enhances the precision of retrieved results. Providing advanced neural
+ranking, it refines the output of initial models like [Boomerang](https://vectara.com/blog/introducing-boomerang-vectaras-new-and-improved-retrieval-model/),
+offering improved document scoring and response quality in Retrieval Augmented
+Generation (RAG) pipelines.
+
+The Vectara Multilingual Reranker operates as a second-pass refinement tool,
+building on Boomerang's high-recall capabilities. While Boomerang quickly
+retrieves a broad set of relevant documents, the Multilingual Reranker
+delivers more precise results, ensuring that the top-ranked documents are the
+most relevant. This reranker excels across both English and multilingual
+datasets, making it a strong choice for global use cases.
+
+While more computationally expensive and introducing some additional latency,
+the multilingual reranker improves neural ranking beyond Boomerang's initial
+selection by providing more precise text scoring. The multilingual capability
+serves as a key differentiator, as many market rerankers are English-only.
+
+Vectara now offers multiple reranking models including Qwen3 (the default for
+SaaS) and Mixbread. You should evaluate different rerankers on your own
+dataset to determine which provides the best results for your specific use
+case and latency requirements.
 
 Using this reranker requires both the `type` and `reranker_name` in the 
 `reranker` object. Set the `type` as `customer_reranker` and the `reranker_name` 

diff --git a/www/docs/release-notes.mdx b/www/docs/release-notes.mdx
@@ -20,6 +20,34 @@ and how these product and documentation changes can benefit your enterprise.
 
 ---
 
+## New Reranking Models: Qwen3 and Mixbread
+
+_September 30, 2025_
+
+Vectara now offers two new neural reranking models: **Qwen3** and **Mixbread**. 
+These models provide more flexibility to optimize search result relevance 
+based on your specific accuracy and latency requirements.
+
+**Why it matters:** Each use case demands different tradeoffs. With multiple
+available rerankers, you can evaluate and select the model that best fits your 
+data and performance needs.
+
+**What's new:**
+
+- **Qwen3 Reranker** (`qwen3-reranker`): High-accuracy multilingual model, now the
+  default option. Optimized for precision across diverse datasets.
+- **Mixbread Reranker** (`mxbai-rerank-base-v2`): Efficient model balancing speed
+  and accuracy for high-volume production workloads.
+- Override defaults per query or combine rerankers in chains for advanced strategies.
+- Evaluate all rerankers against your data to find the optimal fit.
+
+**More information:**
+
+* [Reranking](/docs/api-reference/search-apis/reranking)
+* [Chain Reranker](/docs/learn/chain-reranker)
+
+---
+
 ## Vectara Agents Framework
 
 _September 3, 2025_