Best embeddings model (2026): ₹ cost & recall for RAG

Best embeddings model for RAG and semantic search

Embeddings turn text into vectors for semantic search — the retrieval half of any RAG pipeline. You embed once and search repeatedly, so cost per token compounds fast at scale. On unoblox, the standout is Qwen3-Embedding-0.6B: it's free, native, and good enough for most production RAG.

Embeddings model comparison

Model	₹ cost (per 1M tokens)	Best for
Qwen3-Embedding-0.6B	₹0	Native on unoblox — prototypes and production RAG
Other embedding models	see /models	Check live ₹ rate and specs before committing

We won't invent a price for a model we don't quote natively — /models always reflects the current, real rate.

Qwen3-Embedding-0.6B: the ₹0 default

• Cost: free on unoblox's native /v1/embeddings, within the standard free-tier limits, billed at the published rate beyond that.
• Use case: RAG prototypes, internal tools, and cost-sensitive production search.
• Context: long enough for typical chunked-document workflows — check /models for the exact limit.
• Speed: fast enough to embed at query time, not just in a batch job.

Because it's free and native, it's the sensible default to start with — you only need to look elsewhere if you hit a specific recall ceiling on your own data.

When to look beyond Qwen3-Embedding

If retrieval quality plateaus on a hard domain — dense legal text, multilingual mixed-script documents, very short queries against long documents — benchmark a second embedding model on your own eval set before switching. Check /models for what's currently live and its ₹ rate; pricing and availability for non-native embedding models can change, so we point you there instead of publishing a number that might already be stale.

A 3-step RAG setup with Qwen3-Embedding

1. Embed your documents:

   curl -X POST https://api.unoblox.ai/v1/embeddings \
     -H "Authorization: Bearer ub-gw-..." \
     -d '{"input":"your doc chunk","model":"qwen/Qwen3-Embedding-0.6B"}'
   

2. Store the vectors in Postgres+pgvector, Pinecone, or Weaviate:

   index.upsert([(doc_id, vector, {"text": text})])
   

3. Retrieve at query time:

   query_embedding = client.embeddings.create(
       input=user_query,
       model="qwen/Qwen3-Embedding-0.6B"
   ).data[0].embedding
   results = index.query(query_embedding, top_k=5)
   

Add unoblox's native /v1/rerank (Qwen3-Reranker-0.6B, also ₹0) as a fourth step to re-score your top-k before passing them to an LLM — it noticeably improves precision on noisy corpora at no extra cost.

Frequently asked questions

Is Qwen3-Embedding really free? Yes — it's ₹0 on unoblox's native endpoint within standard free-tier limits; sustained heavy production usage settles at the published rate beyond that.

Do I need a reranker as well as an embedding model? For small top-k retrieval (5 or fewer documents) it's optional. For noisier, higher-recall searches, reranking meaningfully improves what actually reaches your LLM.

Does Qwen3-Embedding work with LangChain or LlamaIndex? Yes — point either framework's OpenAI-compatible embeddings client at base_url="https://api.unoblox.ai/v1" with model="qwen/Qwen3-Embedding-0.6B".

How do I compare it against another embedding model for my use case? Embed the same document set with both, run your real queries, and compare top-k overlap and answer quality — a benchmark on someone else's data won't tell you much about yours.

Can I mix embedding models in one index? Don't — different models produce vectors that aren't comparable. Pick one and re-embed everything if you switch.

Where do I find current pricing for non-native embedding models? /models always has the live rate — we link there rather than print a number that could be stale by the time you read this.

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