Nemotron vs Llama: Open Model Comparison

NVIDIA's Nemotron and Meta's Llama 4 are both open-weight model families available on unoblox, but only one of them has a fixed ₹ rate in the current price list. Here's what's actually known and priced today.

What the model ids tell you

nvidia/nemotron-3-ultra-550b-a55b and meta-llama/llama-4-maverick-17b-128e-instruct-fp8 both encode architecture details in their names — a common convention where the first number is total parameters and a suffix like "a55b" or the expert count points to a mixture-of-experts design with a smaller active-parameter count per token. Treat that as a naming convention, not a performance claim — the only way to know how a model behaves on your workload is to test it directly.

₹ pricing: what's fixed and what isn't

Model	Model id	Input (₹ / 1M tokens)	Output (₹ / 1M tokens)
Nemotron 3 Ultra	nvidia/nemotron-3-ultra-550b-a55b	see live ₹ pricing	see live ₹ pricing
Llama 4 Scout	meta-llama/llama-4-scout-17b-16e-instruct	₹10.08	₹30.24
Llama 4 Maverick	meta-llama/llama-4-maverick-17b-128e-instruct-fp8	₹20.16	₹80.64

Both Llama 4 sizes have a fixed, published ₹ rate. Nemotron 3 Ultra's current rate isn't part of that fixed list — check its live model page at https://unoblox.ai/models/nvidia/nemotron-3-ultra-550b-a55b before estimating cost for a production workload built around it.

Choosing between a large MoE model and Llama 4

• If your priority is a known, fixed ₹ cost you can budget against today, Llama 4 Scout or Maverick is the safer starting point.
• If you specifically want to evaluate Nemotron's larger architecture for a demanding task, test it first at low volume and confirm its live rate before scaling up — don't assume it's priced similarly to Llama.
• For most general-purpose text tasks, start with the cheaper Llama 4 Scout and only move to a larger model if your evaluation shows a real quality gap on your own prompts.

Same request shape for both families

curl https://api.unoblox.ai/v1/chat/completions \
  -H "Authorization: Bearer ub-gw-xxxxxxxxxxxxxxxx" \
  -H "Content-Type: application/json" \
  -d '{"model":"meta-llama/llama-4-scout-17b-16e-instruct","messages":[{"role":"user","content":"Summarise this report in three bullet points."}]}'

Change "model" to nvidia/nemotron-3-ultra-550b-a55b to test the same prompt on Nemotron — same key, same endpoint, one GST invoice covering both.

Frequently asked questions

Is Nemotron cheaper or more expensive than Llama 4? Nemotron 3 Ultra doesn't have a fixed ₹ rate in this comparison — check its live model page for the current number rather than assuming where it falls relative to Llama's published rates.

What does the "550b-a55b" naming mean? It's a common shorthand for a mixture-of-experts model's total parameter count versus its active parameters per token — a naming convention, not a guarantee of relative quality against Llama.

Are Nemotron and Llama hosted on infrastructure in India? No, both run on their standard hosting behind unoblox's endpoint. The India benefit is ₹ billing, a GST invoice and one endpoint — not data residency for these models.

Which Llama 4 size should I start with? Llama 4 Scout, since it's the cheaper of the two and a reasonable baseline before testing whether Maverick's higher cost is justified for your task.

Can I test Nemotron without committing to production volume? Yes — call it like any other model with your existing key at low volume, and confirm the live ₹ rate before scaling.

Do open-weight models like these get updated over time? Providers do update model versions; always check the model's live page for the current id and rate rather than assuming past pricing still applies.

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