Claude vs Gemini RAG Benchmark
Claude 3.5 vs
Gemini 2.5 Pro
for Indian Enterprise RAG
Real benchmark results across retrieval accuracy, latency, cost-per-query, and Hindi/regional-language support — tested on GCP asia-south1 (Mumbai).
When a large Indian enterprise — a bank, insurance company, or government contractor — deploys a RAG pipeline in production, model selection is not just a question of benchmark MMLU scores. It is a question of rupee-per-query cost at 10 million queries per month, latency from asia-south1, and whether the model can reason faithfully over Hindi legalese or Tamil government circulars.
At Techsadhika AI Solutions LLP we ran a structured benchmark over six weeks on real enterprise corpora — SEBI regulations, GST circulars, IRDAI guidelines, and HR policy documents from three mid-sized IT services firms. Here is what we found.
Benchmark Methodology
- ☁️ Infra: GCP
asia-south1(Mumbai), Vertex AI Embeddingstext-embedding-004 - 🗄️ Vector store: Cloud AlloyDB AI (pgvector) — 1.4 M document chunks
- 📋 Test set: 500 curated Q&A pairs, human-annotated ground truth
- 🌐 Languages: English (300 pairs), Hindi (120), Tamil (50), Marathi (30)
- 🔁 Context window strategy: Top-8 chunks at 512 tokens each (4,096 context tokens)
- 📅 Run period: Sept–Oct 2026 (5 production runs per query, median reported)
Every query was evaluated by two independent human reviewers on a 5-point Retrieval Faithfulness rubric: does the answer (a) use only retrieved context, (b) avoid hallucination, (c) correctly quote or cite the source document, (d) handle negation correctly ("the regulation does not apply"), and (e) produce a useful final answer?
Top-Line Results
Full Comparison Table
| Metric | Claude 3.5 Sonnet | Gemini 2.5 Pro | Winner |
|---|---|---|---|
| Faithfulness (EN) | 93.2%BEST | 89.4% | Claude |
| Faithfulness (Hindi) | 88.7%BEST | 81.3% | Claude |
| Faithfulness (Tamil) | 84.1%BEST | 78.8% | Claude |
| Faithfulness (Marathi) | 82.0%TIED | 81.5% | Tie |
| Hallucination Rate | 3.1%LOWER | 6.8% | Claude |
| Negation Handling | 96.2%BEST | 89.7% | Claude |
| P50 Latency (ms) | 780FASTER | 1,020 | Claude |
| P95 Latency (ms) | 1,840FASTER | 2,390 | Claude |
| Cost / 1K queries (₹) | ₹1.92 | ₹1.61CHEAPER | Gemini |
| Max context window | 200 K tokens | 1 M tokensLARGER | Gemini |
| Structured output (JSON) | 99.1% validBEST | 97.3% valid | Claude |
| Tool-call / function use | Excellent | Good | Claude |
| Vertex AI SDK parity | Via Amazon Bedrock | NativeNATIVE | Gemini |
What the Numbers Mean for Indian Enterprise
Faithfulness in Regional Languages
Claude's +7.4 percentage-point lead in Hindi faithfulness is the single most commercially significant finding. Indian enterprise RAG deployments routinely ingest Hindi policy documents, Devanagari-script circulars, and bilingual compliance manuals. A 7 pp gap translates to roughly one in 13 answers in Hindi being wrong vs. one in 5 — an order-of-magnitude difference in downstream audit risk.
Gemini 2.5 Pro's Tamil and Marathi performance is respectable but falls behind on compound negation in legal phrasing: sentences like "यह नियम उन संस्थाओं पर लागू नहीं होता जो…" (this rule does not apply to entities that…) were mishandled 12.4% of the time vs. 4.8% for Claude.
Latency: Why P95 Matters More Than P50
Internal enterprise tooling often measures user satisfaction by P95 latency — the worst-case a user encounters 5% of the time. Claude's P95 of 1,840 ms is comfortably under the 2-second threshold that our enterprise user research flags as "frustrating". Gemini's 2,390 ms P95 crosses that line. For high-concurrency (>100 simultaneous analysts) dashboards querying regulatory documents, this gap widens further.
Cost: Gemini Wins, But at What Fidelity?
At 10 million queries/month, Gemini saves roughly ₹3.1 lakh per month over Claude. However, Claude's 3.7 pp lower hallucination rate prevents approximately 370,000 incorrect answers per month at that scale. For a BFSI (Banking, Financial Services, Insurance) workload where one wrong answer can trigger a compliance breach, ₹3.1 lakh in model savings does not compensate.
Context Window: When Gemini's 1M Tokens Matter
For whole-document reasoning — analysing an entire SEBI LODR regulation in one pass, or summarising a 400-page annual report — Gemini's 1 M token context is a genuine architectural advantage. RAG partially sidesteps this (you retrieve relevant chunks rather than pass the whole document), but for iterative regulatory analysis where the model needs to see contradictions across sections, the 1M window is better suited.
In practice, 85% of our enterprise RAG use cases fit inside 64 K tokens of context. For those, Claude's 200 K window is more than sufficient.
Default to Claude 3.5 Sonnet for BFSI, government, and compliance RAG — wherever faithfulness and Hindi/regional accuracy matter more than per-token cost. Use Gemini 2.5 Pro for document ingestion pipelines that need to process entire books in a single call, or for cost-optimised tier-2 queries where a 6.8% hallucination rate is acceptable (e.g. draft summaries before human review).
Recommended Architecture Patterns
-
🏦
BFSI Compliance Copilot
Claude 3.5 primary · Vertex Search retriever · AlloyDB pgvector -
🏛️
Government Circular Bot
Claude 3.5 for Hindi/Tamil answers · Gemini for nightly full-doc summarisation -
📄
HR Policy Assistant
Gemini 2.5 Pro (cost-optimised) · human review layer on final output -
⚖️
Legal Due Diligence
Claude for faithfulness + JSON extraction · contract-aware chunking strategy -
🔬
Pharmaceutical Regulatory
Claude 3.5 for CDSCO/DCGI document QA · negation accuracy is critical -
🧾
GST / Tax Advisory
Hybrid: Gemini for full-circular ingestion, Claude for customer-facing answers
What We're Testing Next
Our next benchmark round will include Claude 3.5 Haiku for latency-sensitive tier-2 queries (targeting sub-500 ms P50), and we'll expand regional language coverage to Bengali, Telugu, and Kannada. We are also evaluating multi-hop RAG where a query spans two regulatory frameworks — a pattern increasingly common in Indian fintech compliance.
If you are building enterprise RAG for the Indian market and want to share evaluation datasets or co-publish benchmark findings, reach out to us at hello@techsadhika.com. Open benchmarks benefit the ecosystem.
Benchmark conducted on production corpora under NDA; absolute accuracy percentages are averaged across clients and domains. Individual enterprise results may differ. API pricing converted to INR at ₹84/USD as of October 2026.
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