API cost decision in 10 seconds

NewGemini 3.7 Flash (batch) vs GPT-5.6 Luna Pro (batch)

Pick GPT-5.6 Luna Pro (batch) when budget and context both matter.

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

Budget verdict

Pick GPT-5.6 Luna Pro (batch) when budget and context both matter.

On the standard 1M input plus 500K output workload, GPT-5.6 Luna Pro (batch) is estimated at $0.4 vs $0.66 for Gemini 3.7 Flash (batch), saving $0.26 (39% lower).

Cost-first pickGPT-5.6 Luna Pro (batch)
Context-first pickGPT-5.6 Luna Pro (batch)
Sample savings$0.2639%
10x traffic gap$2.56

GPT-5.6 Luna Pro (batch) is cheaper on the standard workload and also has the larger context window. At 10x that traffic, the same price gap is about $2.56. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

GPT-5.6 Luna Pro (batch) stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shapeToken mixBetter pickGemini 3.7 Flash (batch)GPT-5.6 Luna Pro (batch)
Input-heavy / RAG5M input + 500K outputGPT-5.6 Luna Pro (batch)$1.41$0.8
Balanced workload1M input + 1M outputGPT-5.6 Luna Pro (batch)$1.12$0.7
Output-heavy chatbot1M input + 5M outputGPT-5.6 Luna Pro (batch)$4.88$3.1
Cheaper input GPT-5.6 Luna Pro (batch) $0.1875 vs $0.1 / 1M

GPT-5.6 Luna Pro (batch) is $0.09 cheaper per 1M input tokens (46.7% lower; 1.88x difference).

Cheaper output GPT-5.6 Luna Pro (batch) $0.9375 vs $0.6 / 1M

GPT-5.6 Luna Pro (batch) is $0.34 cheaper per 1M output tokens (36% lower; 1.56x difference).

Larger context GPT-5.6 Luna Pro (batch) 1.05M vs 1.05M

GPT-5.6 Luna Pro (batch) has 1.42K more context (1x larger).

Sample workload GPT-5.6 Luna Pro (batch) $0.66 vs $0.4

GPT-5.6 Luna Pro (batch) is $0.26 cheaper on the standard workload (39% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.
Gemini 3.7 Flash (batch) Calculating… Estimated API cost
GPT-5.6 Luna Pro (batch) 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-5.6 Luna Pro (batch) has the lower input price; GPT-5.6 Luna Pro (batch) has the lower output price; GPT-5.6 Luna Pro (batch) offers the larger context window. For the 1M input plus 500K output sample, GPT-5.6 Luna Pro (batch) is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $0.66 for Gemini 3.7 Flash (batch) and $0.4 for GPT-5.6 Luna Pro (batch).

Best Fit

Choose Gemini 3.7 Flash (batch) when its provider, model quality, latency, or availability is more important than the numeric price/context winner.

Choose GPT-5.6 Luna Pro (batch) when you care most about lower input-token price, lower output-token price, and larger context window.

Decision Notes
  • On the standard 1M input plus 500K output workload, GPT-5.6 Luna Pro (batch) is estimated at $0.4 vs $0.66 for Gemini 3.7 Flash (batch), saving $0.26 (39% lower).
  • GPT-5.6 Luna Pro (batch) is $0.26 cheaper on the standard workload (39% lower).
  • GPT-5.6 Luna Pro (batch) is $0.09 cheaper per 1M input tokens (46.7% lower; 1.88x difference).
  • GPT-5.6 Luna Pro (batch) is $0.34 cheaper per 1M output tokens (36% lower; 1.56x difference).
  • GPT-5.6 Luna Pro (batch) has 1.42K more context (1x larger).
Head-to-Head Specs
FeatureNewGemini 3.7 Flash (batch)
(Google)		GPT-5.6 Luna Pro (batch)
(OpenAI)
Input Price
prompt tokens per 1M		$0.1875$0.1
Completion Price
per 1M tokens		$0.9375$0.6
Sample Workload Cost
1M input + 500K output		$0.66$0.4
Context Window1.05M1.05M
Release Date

Use-Case Decision Matrix

Use caseBetter pickWhy
Budget-constrained productionGPT-5.6 Luna Pro (batch)On the standard 1M input plus 500K output workload, GPT-5.6 Luna Pro (batch) is estimated at $0.4 vs $0.66 for Gemini 3.7 Flash (batch), saving $0.26 (39% lower).
High-volume input processingGPT-5.6 Luna Pro (batch)Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbotsGPT-5.6 Luna Pro (batch)Lower output-token price matters most when assistants generate many completion tokens.
RAG or long-document workGPT-5.6 Luna Pro (batch)A larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

Same-provider lower-cost swaps
  • Gemma 4 26B A4B (free) can replace Gemini 3.7 Flash (batch) when lower sample workload cost matters most: $0.
  • Gemma 4 31B (free) can replace Gemini 3.7 Flash (batch) when lower sample workload cost matters most: $0.
  • Lyria 3 Pro Preview can replace Gemini 3.7 Flash (batch) when lower sample workload cost matters most: $0.
  • Lyria 3 Clip Preview can replace Gemini 3.7 Flash (batch) when lower sample workload cost matters most: $0.

Cheaper alternatives

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Larger context alternatives

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

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Provider catalogs

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

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Google catalog

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

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OpenAI catalog

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

Open OpenAI models
Gemini 3.7 Flash (batch)

Gemini 3.7 Flash is a multimodal model from Google for fast agentic workflows, coding, and complex multi-step reasoning. It is designed for tasks that require responsive performance and reliable multi-step...

GPT-5.6 Luna Pro (batch)

GPT-5.6 Luna Pro is the same underlying model as [GPT-5.6 Luna](https://openrouter.ai/openai/gpt-5.6-luna), served with `reasoning.mode` set to `pro` for higher-quality responses on complex tasks. Learn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode