Llama pricing in India (₹)

Llama pricing in India — fast, open-weight, ₹-native

Meta's Llama 4 models are priced on unoblox in rupees: Llama 4 Scout and Llama 3.3 70B Instruct Turbo cost a fraction of Claude or GPT, with the same GST tax invoice issued at each top-up and rupee-only billing as every other model on the platform.

Llama 4 pricing (per 1M tokens)

Model	Input (₹)	Output (₹)	Typical latency	Strengths
Llama 4 Scout	₹10.12	₹30.36	Fastest	Classification, simple generation
Llama 3.3 70B Instruct Turbo	₹10.12	₹32.39	Fast	Multilingual chat, general generation

Rates as of 6 October 2026; the live price is on each model page. Llama 4 Maverick is not currently offered on unoblox.

Why Llama is worth a look before defaulting to GPT or Claude

Llama 4 is open-weight and heavily optimized for inference speed, which is why unoblox can price it well below proprietary models:

• Llama 3.3 70B at ₹10.12/M input is 20× cheaper than Claude Sonnet 5's ₹202.46/M, and 50× cheaper than Claude Opus's ₹506.14/M.
• Scout at ₹10.12/M input is one of the lowest-priced options on the platform, built for high-volume, well-defined tasks.

Worked example — 40M input + 10M output tokens a month:

• Llama Scout: (40 × ₹10.12) + (10 × ₹30.36) = ₹708
• Claude Sonnet: (40 × ₹202.46) + (10 × ₹1,012.28) = ₹18,221

Same volume, roughly 26× apart. The output quality gap for structured tasks (classification, tagging, templated generation) is small enough that most teams don't notice it in production.

Use cases where Llama is the practical default

Task	Best model	Why
Ticket classification	Scout	Fast, cheap, accurate enough
Sentiment analysis	Scout	Sub-second, near-zero marginal cost

Cost breakdown: Llama vs alternatives

Monthly spend at 30M input tokens + 10M output tokens:

Model	Monthly cost
Llama 4 Scout	₹607
Qwen3 235B-A22B	₹830
Claude Sonnet	₹16,197
GPT-4o	₹17,715

At this volume, Llama Scout runs roughly 27–29× cheaper than Claude Sonnet or GPT-4o for workloads where structured, high-volume output matters more than nuanced reasoning.

Calling Llama through unoblox

from openai import OpenAI
client = OpenAI(api_key="ub-gw-YOUR_KEY", base_url="https://api.unoblox.ai/v1")
response = client.chat.completions.create(
    model="meta-llama/llama-4-scout-17b-16e-instruct",
    messages=[{"role": "user", "content": "Hello"}]
)

Same rupee wallet, a GST tax invoice at each top-up, same claimable GST input credit, no separate account needed.

Frequently asked questions

Is open-weight Llama as good as proprietary models? For classification, summarization, and templated generation, the practical gap is small at a fraction of the cost. For open-ended research or edge-case reasoning, proprietary models still have an edge.

How does Llama's latency compare to Claude or GPT? Llama 4 is generally fast, since it's optimized for efficient serving. Proprietary models can be more consistent under heavy multi-step reasoning, but for typical chat and classification workloads the difference is rarely noticeable.

Can I run Llama in production, not just prototypes? Yes — Llama 4 is stable and widely deployed; unoblox serves it on the same infrastructure and SLA as every other model.

Scout or Llama 3.3 70B — which one? Both cost ₹10.12 per 1M input tokens. Scout has the longer context window on unoblox and accepts image input; test both on your own task before choosing.

Do I need my own GPU capacity to use Llama? No. unoblox hosts inference; you call a standard API endpoint.

Is there a free option before I commit to Llama pricing? Qwen3.8-27B is ₹0 for prototyping. Llama Scout at ₹10.12/M input is a low-cost paid step up once you need more consistent quality.

Get started in rupees → https://unoblox.ai/sign-in