Cohere: Rerank 4 Pro vs Qwen3 Reranker 8B
A side-by-side comparison of Cohere: Rerank 4 Pro (Free) and Qwen3 Reranker 8B (Free) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.
Specifications
| Cohere: Rerank 4 Pro | Qwen3 Reranker 8B | |
|---|---|---|
| Provider | Cohere | Qwen |
| Context window | 32,768 | 40,960 |
| Max output | 29,491 | 36,864 |
| Input | text | text |
| Output | rerank | rerank |
| Tool calling | ||
| Structured output | ||
| Reasoning control | ||
| Added | Apr 6, 2026 | Aug 13, 2026 |
Key differences
- Qwen3 Reranker 8B reads 1.3× more context (40,960 tokens).
In depth
Cohere: Rerank 4 Pro is a reranking model from Cohere, built to take a query and a set of candidate passages and return them ordered by relevance. Qwen3 Reranker 8B is a reranking model from Qwen, the model family associated with Alibaba, and it serves the same broad purpose. Both sit in the rerank category, so neither is a general chat or generation model. You send text in and you get a relevance ordering out. The two differ mainly in lineage and in the engineering choices each provider made around context and capacity.
On capabilities the two are close. Both accept text only as input and both produce rerank output, so there is no multimodal angle to weigh here. Neither exposes any supported parameters, which means you cannot tune sampling style knobs on either one; the request shape is essentially fixed. The one clear, factual difference is context window. Qwen3 Reranker 8B handles up to 40960 tokens, while Cohere Rerank 4 Pro handles up to 32768 tokens. That makes Qwen the larger window of the two. The qualitative comparison marks Qwen as the bigger context model and leaves price, tool use, structured output and reasoning as either not meaningfully different or unknown, so there is no basis to claim one is cheaper or more capable on those axes. On price, treat them as comparable until the live table says otherwise.
For picking between them, start with the length of what you are reranking. If your queries and candidate documents fit comfortably inside 32768 tokens, Cohere Rerank 4 Pro is a reasonable default and comes from a provider with a long history of dedicated reranking work. If you routinely push past that, for example long documents, large retrieved chunks, or a query plus many candidates packed into one call, Qwen3 Reranker 8B gives you more headroom at 40960 tokens and saves you from truncating or splitting the input. If your pipeline already leans on Qwen models elsewhere, staying in that family keeps your tooling and evaluation consistent. If your pipeline is built around Cohere embeddings or Cohere's other rerankers, Cohere Rerank 4 Pro is the natural fit. Where both fit, the tie on the other qualitative fields means the decision usually comes down to context length and existing stack, not to a capability gap.
Calling either one through api14 is the same operation. The API is OpenAI compatible, so you point your existing client at the api14 base URL, send your request in the usual rerank or chat style payload your client already supports, and authenticate with your api14 key. The only thing that changes between the two models is the model id string. Use "cohere/rerank-4-pro" for Cohere and "qwen/qwen3-reranker-8b" for Qwen. Everything else, the endpoint, the headers, the response handling, stays put, which makes it cheap to benchmark both against your own query and passage sets and pick the one that orders your results better.
There is a case where the smaller window model is still the better pick. If your inputs are short and you want to stay with Cohere because the rest of your retrieval stack is Cohere based, Cohere Rerank 4 Pro is the sensible choice even though Qwen offers more context. Extra window does nothing for you if you never approach the limit, and consistency across a single provider's models can simplify debugging and evaluation. So the honest guidance is: check your token counts first, then let provider fit and the live price table settle the rest.
Switch with one string
Both models use the same endpoint and request shape. Change the model value from "cohere/rerank-4-pro" to "qwen/qwen3-reranker-8b" and prompts, streaming and tools keep working.
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api14.com/api/v1",
apiKey: process.env.API14_API_KEY,
});
const result = await client.post("/rerank", { body: {
"model": "qwen/qwen3-reranker-8b",
"query": "What is the capital of France?",
"documents": [
"Paris is the capital of France.",
"Berlin is in Germany."
],
"top_n": 1
} });
console.log(result.results);
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