Best LLM for RAG: vector search + retrieval in ₹

Best LLM for RAG: GPT-5 and Claude lead; DeepSeek wins on ₹

Retrieval-Augmented Generation (RAG) pairs vector search with an LLM that grounds answers in your docs. GPT-5 and Claude excel at dense retrieval + long-context reasoning. DeepSeek offers the same quality at half the ₹ cost. Via unoblox, you get all three, native embeddings API, and ₹-native billing for your entire RAG stack.

RAG model guide

Model	Context	₹ Input	Reasoning	Best for
GPT-5	128K	₹126	Exceptional	Precise Q&A, complex queries
Claude Opus	200K	₹504	Excellent	Long docs, nuanced answers
DeepSeek V3.2	64K	₹26.21	Very good	Budget RAG, high volume
Qwen3 Max	32K	₹120.95	Excellent	Vision RAG, multimodal
Llama Maverick	32K	₹20.16	Good	OSS workflows, cost-sensitive

Why context window matters for RAG

Longer context = more retrieved docs in one pass. GPT-5's 128K window lets you stuff 20–30 document chunks into a single prompt, improving coherence. Claude Opus's 200K is even better for book-length corpora. DeepSeek's 64K is sufficient for most use cases.

Building RAG with unoblox

Unoblox provides the full RAG stack:

• Native /v1/embeddings — Qwen3-Embedding-0.6B at ₹0 (free tier).
• Retrieval LLM — DeepSeek, GPT-5, Claude, or Qwen.
• Native /v1/rerank — Qwen3-Reranker-0.6B (re-score search results before LLM).
• One endpoint — No API switching, single ₹-denominated bill.

import requests

BASE_URL = "https://api.unoblox.ai/v1"
KEY = "ub-gw-..."

# Step 1: Embed query
resp = requests.post(
    f"{BASE_URL}/embeddings",
    headers={"Authorization": f"Bearer {KEY}"},
    json={"model": "qwen/qwen3-embedding-0.6b", "input": "What is RAG?"}
)
query_embedding = resp.json()["data"][0]["embedding"]

# Step 2: Vector search (your DB: Pinecone, Milvus, Weaviate)
# ... retrieve top-k docs ...

# Step 3: Rerank results (optional, keeps high-quality docs first)
rerank_resp = requests.post(
    f"{BASE_URL}/rerank",
    headers={"Authorization": f"Bearer {KEY}"},
    json={
        "model": "qwen/qwen3-reranker-0.6b",
        "query": "What is RAG?",
        "documents": [retrieved_docs]
    }
)

# Step 4: Call LLM with top docs
llm_resp = requests.post(
    f"{BASE_URL}/chat/completions",
    headers={"Authorization": f"Bearer {KEY}"},
    json={
        "model": "deepseek-ai/deepseek-v3.2",  # or gpt-5, claude-opus
        "messages": [
            {"role": "system", "content": "Answer based on these docs:"},
            {"role": "user", "content": f"Query: {query}\nDocs: {reranked_docs}"}
        ]
    }
)

Cost breakdown (10,000 queries against a 100-doc corpus)

• Embedding queries: ~100 tokens each, Qwen3-Embedding — ₹0 (free tier).
• Reranking: top candidates per query, Qwen3-Reranker — ₹0 (free tier).
• LLM synthesis (DeepSeek V3.2): 3K input + 500 output tokens per query ≈ ₹0.10 per query → **₹1,000 for 10,000 queries**.
• Same workload on GPT-4o: same token counts ≈ ₹1.26 per query → ~₹12,600 for 10,000 queries.

Retrieval (embedding + reranking) is free either way — the LLM you pick for synthesis is what moves your bill.

Frequently asked questions

Q: Should I always use the longest-context model? No. DeepSeek's 64K is enough for most RAG. Longer context adds latency (₹ cost same per token).

Q: Do I need a reranker? For high-recall searches (50+ docs), yes—reranking meaningfully improves what reaches the LLM by demoting weak matches. For tight searches (top 5), it is optional.

Q: Can I fine-tune the embedding model? Not via unoblox API, but you can use Qwen3-Embedding's open weights locally.

Q: Do I need to know the vector dimension in advance? Yes, for your vector DB schema — call the embeddings endpoint once and read the dimension off the response. It's compatible with all major vector DBs regardless of size.

Q: Is Claude Opus worth the extra ₹ for RAG? For nuanced, conversational answers: yes. For fact-based Q&A: DeepSeek is fine.

Q: How do I handle documents larger than context? Chunk into 1–3K-token segments, embed, retrieve, rerank, then pass top 5–10 to the LLM.

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