API cost decision in 10 seconds

NewGPT-6 Luna (batch) vs Nemotron 3.5 Lightning

Pick Nemotron 3.5 Lightning for lower cost; pick GPT-6 Luna (batch) only if the larger context window matters more.

Page updated:  Data confirmed:  Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick Nemotron 3.5 Lightning for lower cost; pick GPT-6 Luna (batch) only if the larger context window matters more.

On the standard 1M input plus 500K output workload, Nemotron 3.5 Lightning is estimated at $0.17 vs $0.17 for GPT-6 Luna (batch), saving $0.005 (2.9% lower).

Cost-first pickNemotron 3.5 Lightning
Context-first pickGPT-6 Luna (batch)
Sample savings$0.0052.9%
10x traffic gap$0.05

GPT-6 Luna (batch) has more context, but Nemotron 3.5 Lightning saves $0.005 on the standard workload. At 10x that traffic, the same price gap is about $0.05. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Cost winner changes by workload shape: input-heavy / RAG favors GPT-6 Luna (batch), balanced workload favors Nemotron 3.5 Lightning, and output-heavy chatbot favors Nemotron 3.5 Lightning.

Workload shapeToken mixBetter pickGPT-6 Luna (batch)Nemotron 3.5 Lightning
Input-heavy / RAG5M input + 500K outputGPT-6 Luna (batch)$0.38$0.45
Balanced workload1M input + 1M outputNemotron 3.5 Lightning$0.3$0.27
Output-heavy chatbot1M input + 5M outputNemotron 3.5 Lightning$1.3$1.07
Cheaper input GPT-6 Luna (batch) $0.05 vs $0.07 / 1M

GPT-6 Luna (batch) is $0.02 cheaper per 1M input tokens (28.6% lower; 1.4x difference).

Cheaper output Nemotron 3.5 Lightning $0.25 vs $0.2 / 1M

Nemotron 3.5 Lightning is $0.05 cheaper per 1M output tokens (20% lower; 1.25x difference).

Larger context GPT-6 Luna (batch) 1.05M vs 262.14K

GPT-6 Luna (batch) has 787.86K more context (4.01x larger).

Sample workload Nemotron 3.5 Lightning $0.17 vs $0.17

Nemotron 3.5 Lightning is $0.005 cheaper on the standard workload (2.9% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.
GPT-6 Luna (batch) Calculating… Estimated API cost
Nemotron 3.5 Lightning Calculating… Estimated API cost
Cheaper for this workload Calculating… Difference: calculating…

This estimate uses normalized public API pricing per 1M tokens. It is a planning aid, not a billing quote. Verify provider pricing, limits, and terms before production use.

Quick Decision

Verdict

GPT-6 Luna (batch) has the lower input price; Nemotron 3.5 Lightning has the lower output price; GPT-6 Luna (batch) offers the larger context window. For the 1M input plus 500K output sample, Nemotron 3.5 Lightning is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $0.17 for GPT-6 Luna (batch) and $0.17 for Nemotron 3.5 Lightning.

Best Fit

Choose GPT-6 Luna (batch) when you care most about lower input-token price, and larger context window.

Choose Nemotron 3.5 Lightning when you care most about lower output-token price.

Decision Notes
  • On the standard 1M input plus 500K output workload, Nemotron 3.5 Lightning is estimated at $0.17 vs $0.17 for GPT-6 Luna (batch), saving $0.005 (2.9% lower).
  • Nemotron 3.5 Lightning is $0.005 cheaper on the standard workload (2.9% lower).
  • GPT-6 Luna (batch) is $0.02 cheaper per 1M input tokens (28.6% lower; 1.4x difference).
  • Nemotron 3.5 Lightning is $0.05 cheaper per 1M output tokens (20% lower; 1.25x difference).
  • GPT-6 Luna (batch) has 787.86K more context (4.01x larger).
Head-to-Head Specs
FeatureNewGPT-6 Luna (batch)
(OpenAI)
Nemotron 3.5 Lightning
(NVIDIA)
Input Price
prompt tokens per 1M
$0.05$0.07
Completion Price
per 1M tokens
$0.25$0.2
Sample Workload Cost
1M input + 500K output
$0.17$0.17
Context Window1.05M262.14K
Release Date

Use-Case Decision Matrix

Use caseBetter pickWhy
Budget-constrained productionNemotron 3.5 LightningOn the standard 1M input plus 500K output workload, Nemotron 3.5 Lightning is estimated at $0.17 vs $0.17 for GPT-6 Luna (batch), saving $0.005 (2.9% lower).
High-volume input processingGPT-6 Luna (batch)Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbotsNemotron 3.5 LightningLower output-token price matters most when assistants generate many completion tokens.
RAG or long-document workGPT-6 Luna (batch)A larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

Same-provider lower-cost swaps
  • gpt-oss-120b (free) can replace GPT-6 Luna (batch) when lower sample workload cost matters most: $0.
  • gpt-oss-20b (free) can replace GPT-6 Luna (batch) when lower sample workload cost matters most: $0.
  • gpt-oss-20b can replace GPT-6 Luna (batch) when lower sample workload cost matters most: $0.06.
  • gpt-oss-20b (batch) can replace GPT-6 Luna (batch) when lower sample workload cost matters most: $0.08.
Larger context near this budget

Cheaper alternatives

Review low-cost models sorted by a standard 1M input plus 500K output workload.

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Larger context alternatives

Find models with larger context windows for RAG, long documents, and codebase review.

Open largest context models

Provider catalogs

Compare models within provider hubs before choosing a final API vendor.

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OpenAI catalog

Review all tracked OpenAI models before deciding whether this matchup is the right shortlist.

Open OpenAI models

NVIDIA catalog

Check other NVIDIA models with comparable pricing, context, or release timing.

Open NVIDIA models
GPT-6 Luna (batch)

GPT-6 Luna is the fast, cost-efficient model in OpenAI's GPT-6 series, positioned below GPT-6 Sol. It is suited for high-volume and latency-sensitive workloads such as chat, classification, and lightweight agentic...

Nemotron 3.5 Lightning

NVIDIA Nemotron 3.5 Lightning is an open mixture-of-experts model from NVIDIA, with 3B active parameters out of 30B total. It is suited for high-throughput agentic workloads and specialized tasks that...