GPT-5.6 Luna Pro (batch) is $0.1 cheaper per 1M input tokens (50% lower; 2x difference).
API cost decision in 10 seconds
GPT-5.6 Luna Pro (batch) vs Laguna M.1
The standard workload cost is tied; choose by context window, provider fit, latency, or model quality.
Page updated:
Data confirmed:
Prices normalized to USD per 1M tokens
Sample workload: 1M input + 500K output
Budget verdict
The standard workload cost is tied; choose by context window, provider fit, latency, or model quality.
Both models are estimated at $0.4 for the standard 1M input plus 500K output workload.
Cost-first pickTie
Context-first pickGPT-5.6 Luna Pro (batch)
Sample savings$00%
10x traffic gap$0
Context-window winner: GPT-5.6 Luna Pro (batch). Cost does not separate this pair on the standard workload, so the next decision point is context window and model behavior.
Cost sensitivity
Workload Sensitivity
Cost winner changes by workload shape: input-heavy / RAG favors GPT-5.6 Luna Pro (batch), balanced workload favors Laguna M.1, and output-heavy chatbot favors Laguna M.1.
| Workload shape | Token mix | Better pick | GPT-5.6 Luna Pro (batch) | Laguna M.1 |
|---|---|---|---|---|
| Input-heavy / RAG | 5M input + 500K output | GPT-5.6 Luna Pro (batch) | $0.8 | $1.2 |
| Balanced workload | 1M input + 1M output | Laguna M.1 | $0.7 | $0.6 |
| Output-heavy chatbot | 1M input + 5M output | Laguna M.1 | $3.1 | $2.2 |
Laguna M.1 is $0.2 cheaper per 1M output tokens (33.3% lower; 1.5x difference).
GPT-5.6 Luna Pro (batch) has 787.86K more context (4.01x larger).
Both models have the same estimated cost for the standard 1M input plus 500K output workload: $0.4.
Estimate your workload cost
Your Workload Cost
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
GPT-5.6 Luna Pro (batch) has the lower input price; Laguna M.1 has the lower output price; GPT-5.6 Luna Pro (batch) offers the larger context window. For the 1M input plus 500K output sample, the standard workload cost is tied.
For a 1M input token plus 500K output token workload, the estimated API cost is $0.4 for GPT-5.6 Luna Pro (batch) and $0.4 for Laguna M.1.
Choose GPT-5.6 Luna Pro (batch) when you care most about lower input-token price, and larger context window.
Choose Laguna M.1 when you care most about lower output-token price.
- Both models are estimated at $0.4 for the standard 1M input plus 500K output workload.
- Both models have the same estimated cost for the standard 1M input plus 500K output workload: $0.4.
- GPT-5.6 Luna Pro (batch) is $0.1 cheaper per 1M input tokens (50% lower; 2x difference).
- Laguna M.1 is $0.2 cheaper per 1M output tokens (33.3% lower; 1.5x difference).
- GPT-5.6 Luna Pro (batch) has 787.86K more context (4.01x larger).
| Feature | GPT-5.6 Luna Pro (batch) (OpenAI) | Laguna M.1 (Poolside) |
|---|---|---|
| Input Price prompt tokens per 1M | $0.1 | $0.2 |
| Completion Price per 1M tokens | $0.6 | $0.4 |
| Sample Workload Cost 1M input + 500K output | $0.4 | $0.4 |
| Context Window | 1.05M | 262.14K |
| Release Date |
Use-Case Decision Matrix
| Use case | Better pick | Why |
|---|---|---|
| Budget-constrained production | Tie | Both models are estimated at $0.4 for the standard 1M input plus 500K output workload. |
| High-volume input processing | GPT-5.6 Luna Pro (batch) | Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill. |
| Long responses and chatbots | Laguna M.1 | Lower output-token price matters most when assistants generate many completion tokens. |
| RAG or long-document work | GPT-5.6 Luna Pro (batch) | A larger context window leaves more room for retrieved passages, conversation history, or source files. |
Related Alternatives
- gpt-oss-120b (free) can replace GPT-5.6 Luna Pro (batch) when lower sample workload cost matters most: $0.
- gpt-oss-20b (free) can replace GPT-5.6 Luna Pro (batch) when lower sample workload cost matters most: $0.
- gpt-oss-20b can replace GPT-5.6 Luna Pro (batch) when lower sample workload cost matters most: $0.1.
- gpt-oss-120b can replace GPT-5.6 Luna Pro (batch) when lower sample workload cost matters most: $0.12.
- DeepSeek V4 Flash 0731 offers 1.31M context with $0.17 sample workload cost.
- DeepSeek V4 Flash Latest offers 1.31M context with $0.11 sample workload cost.
- Llama 4 Scout offers 1.31M context with $0.25 sample workload cost.
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- MiMo-V2.5 · Xiaomi · #2
- Hy3 · Tencent · #3
- Ox Alpha · stealth · #4
Cheaper alternatives
Review low-cost models sorted by a standard 1M input plus 500K output workload.
Open cheapest modelsLarger context alternatives
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Open Poolside modelsGPT-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
Laguna M.1 is the flagship coding agent model from [Poolside](https://poolside.ai/), optimized for complex software engineering tasks. Designed for agentic coding workflows, it supports tool calling and reasoning, with a 256K...