GPT-5.6 Luna
Proprietary CommercialDeveloped by OpenAI · Released 2025-01-01
What are the token costs and operational benchmarks for GPT-5.6 Luna?
Architectural Overview
Frontier foundation model developed by OpenAI featuring Sub-100ms Ultra-Efficient Edge Model architecture.
Optimal Production Use Cases
Real-time conversational agents, high-volume classification, intent detection, and low-latency interactive apps.
Interactive Monthly Token Economics & ROI Forecaster
Model your expected production workload across prompt (input) and completion (output) tokens.
Estimated Monthly Spend
$0.18/1M in · $0.72/1M out
$0.08/1M in · $0.32/1M out
Head-to-Head Comparisons Involving GPT-5.6 Luna
GPT-5.6 Luna vs Claude 3.5 Haiku
GPT-5.6 Luna is 77.5% cheaper for input tokens ($0.18 vs. $0.80 per 1M tokens) and $0.72 vs. $4.00 for output tokens (4.4x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (50ms faster than Claude 3.5 Haiku). GPT-5.6 Luna leads coding benchmarks at 48.5% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
GPT-5.6 Luna vs Claude 3.5 Sonnet
GPT-5.6 Luna is 94.0% cheaper for input tokens ($0.18 vs. $3.00 per 1M tokens) and $0.72 vs. $15.00 for output tokens (16.7x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (230ms faster than Claude 3.5 Sonnet). Claude 3.5 Sonnet leads coding benchmarks at 63.7% SWE-bench vs. 48.5% for GPT-5.6 Luna.
GPT-5.6 Luna vs Claude 3.7 Sonnet
GPT-5.6 Luna is 94.0% cheaper for input tokens ($0.18 vs. $3.00 per 1M tokens) and $0.72 vs. $15.00 for output tokens (16.7x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (560ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 48.5% for GPT-5.6 Luna.
GPT-5.6 Luna vs Claude Opus 5
GPT-5.6 Luna is 96.4% cheaper for input tokens ($0.18 vs. $5.00 per 1M tokens) and $0.72 vs. $25.00 for output tokens (27.8x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (250ms faster than Claude Opus 5). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 48.5% for GPT-5.6 Luna.
GPT-5.6 Luna vs Codestral 25.01
GPT-5.6 Luna is 40.0% cheaper for input tokens ($0.18 vs. $0.30 per 1M tokens) and $0.72 vs. $0.90 for output tokens (1.7x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (60ms faster than Codestral 25.01). GPT-5.6 Luna leads coding benchmarks at 48.5% SWE-bench vs. 44.2% for Codestral 25.01.
GPT-5.6 Luna vs Composer 2.5
GPT-5.6 Luna is 90.0% cheaper for input tokens ($0.18 vs. $1.80 per 1M tokens) and $0.72 vs. $7.20 for output tokens (10.0x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (90ms faster than Composer 2.5). Composer 2.5 leads coding benchmarks at 74.6% SWE-bench vs. 48.5% for GPT-5.6 Luna.
GPT-5.6 Luna vs DeepSeek-R1
GPT-5.6 Luna is 67.3% cheaper for input tokens ($0.18 vs. $0.55 per 1M tokens) and $0.72 vs. $2.19 for output tokens (3.1x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (1710ms faster than DeepSeek-R1). DeepSeek-R1 leads coding benchmarks at 49.2% SWE-bench vs. 48.5% for GPT-5.6 Luna.
GPT-5.6 Luna vs DeepSeek-V3
DeepSeek-V3 is 22.2% cheaper for input tokens ($0.14 vs. $0.18 per 1M tokens) and $0.28 vs. $0.72 for output tokens (1.3x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (250ms faster than DeepSeek-V3). GPT-5.6 Luna leads coding benchmarks at 48.5% SWE-bench vs. 42.0% for DeepSeek-V3.
GPT-5.6 Luna vs DeepSeek-V4 Flash
DeepSeek-V4 Flash is 22.2% cheaper for input tokens ($0.14 vs. $0.18 per 1M tokens) and $0.56 vs. $0.72 for output tokens (1.3x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (60ms faster than DeepSeek-V4 Flash). DeepSeek-V4 Flash leads coding benchmarks at 62.4% SWE-bench vs. 48.5% for GPT-5.6 Luna.
GPT-5.6 Luna vs Fable 5
GPT-5.6 Luna is 91.0% cheaper for input tokens ($0.18 vs. $2.00 per 1M tokens) and $0.72 vs. $8.00 for output tokens (11.1x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (150ms faster than Fable 5). Fable 5 leads coding benchmarks at 58.0% SWE-bench vs. 48.5% for GPT-5.6 Luna.
GPT-5.6 Luna vs Gemini 2.0 Flash
Gemini 2.0 Flash is 44.4% cheaper for input tokens ($0.10 vs. $0.18 per 1M tokens) and $0.40 vs. $0.72 for output tokens (1.8x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (290ms faster than Gemini 2.0 Flash). GPT-5.6 Luna leads coding benchmarks at 48.5% SWE-bench vs. 48.0% for Gemini 2.0 Flash.
GPT-5.6 Luna vs Gemini 3.7 Flash
Gemini 3.7 Flash is 55.6% cheaper for input tokens ($0.08 vs. $0.18 per 1M tokens) and $0.32 vs. $0.72 for output tokens (2.2x cost difference). In terms of operational performance, Gemini 3.7 Flash delivers faster response latency with 75ms TTFT (15ms faster than GPT-5.6 Luna). Gemini 3.7 Flash leads coding benchmarks at 68.2% SWE-bench vs. 48.5% for GPT-5.6 Luna.
GPT-5.6 Luna vs GLM 5.3 Flash
GLM 5.3 Flash is 16.7% cheaper for input tokens ($0.15 vs. $0.18 per 1M tokens) and $0.50 vs. $0.72 for output tokens (1.2x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (40ms faster than GLM 5.3 Flash). GLM 5.3 Flash leads coding benchmarks at 54.2% SWE-bench vs. 48.5% for GPT-5.6 Luna.
GPT-5.6 Luna vs OpenAI GPT-4o
GPT-5.6 Luna is 92.8% cheaper for input tokens ($0.18 vs. $2.50 per 1M tokens) and $0.72 vs. $10.00 for output tokens (13.9x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (190ms faster than OpenAI GPT-4o). GPT-5.6 Luna leads coding benchmarks at 48.5% SWE-bench vs. 38.8% for OpenAI GPT-4o.
GPT-5.6 Luna vs GPT-5.6 Sol
GPT-5.6 Luna is 97.8% cheaper for input tokens ($0.18 vs. $8.00 per 1M tokens) and $0.72 vs. $32.00 for output tokens (44.4x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (330ms faster than GPT-5.6 Sol). GPT-5.6 Sol leads coding benchmarks at 79.5% SWE-bench vs. 48.5% for GPT-5.6 Luna.
GPT-5.6 Luna vs GPT-5.6 Terra
GPT-5.6 Luna is 88.0% cheaper for input tokens ($0.18 vs. $1.50 per 1M tokens) and $0.72 vs. $6.00 for output tokens (8.3x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (120ms faster than GPT-5.6 Terra). GPT-5.6 Terra leads coding benchmarks at 65.4% SWE-bench vs. 48.5% for GPT-5.6 Luna.
GPT-5.6 Luna vs Grok 3
GPT-5.6 Luna is 94.0% cheaper for input tokens ($0.18 vs. $3.00 per 1M tokens) and $0.72 vs. $15.00 for output tokens (16.7x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (760ms faster than Grok 3). Grok 3 leads coding benchmarks at 58.5% SWE-bench vs. 48.5% for GPT-5.6 Luna.
GPT-5.6 Luna vs xAI Grok 4.6
GPT-5.6 Luna is 91.0% cheaper for input tokens ($0.18 vs. $2.00 per 1M tokens) and $0.72 vs. $6.00 for output tokens (11.1x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (190ms faster than xAI Grok 4.6). xAI Grok 4.6 leads coding benchmarks at 76.8% SWE-bench vs. 48.5% for GPT-5.6 Luna.
GPT-5.6 Luna vs Llama 3.3 70B Instruct
Both models share identical input pricing at $0.18 per 1M tokens. In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (330ms faster than Llama 3.3 70B Instruct). GPT-5.6 Luna leads coding benchmarks at 48.5% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
GPT-5.6 Luna vs Mistral Large 2
GPT-5.6 Luna is 91.0% cheaper for input tokens ($0.18 vs. $2.00 per 1M tokens) and $0.72 vs. $6.00 for output tokens (11.1x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (460ms faster than Mistral Large 2). GPT-5.6 Luna leads coding benchmarks at 48.5% SWE-bench vs. 39.0% for Mistral Large 2.
GPT-5.6 Luna vs OpenAI o1
GPT-5.6 Luna is 98.8% cheaper for input tokens ($0.18 vs. $15.00 per 1M tokens) and $0.72 vs. $60.00 for output tokens (83.3x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (760ms faster than OpenAI o1). OpenAI o1 leads coding benchmarks at 48.9% SWE-bench vs. 48.5% for GPT-5.6 Luna.
GPT-5.6 Luna vs o3-mini
GPT-5.6 Luna is 83.6% cheaper for input tokens ($0.18 vs. $1.10 per 1M tokens) and $0.72 vs. $4.40 for output tokens (6.1x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (1110ms faster than o3-mini). o3-mini leads coding benchmarks at 49.3% SWE-bench vs. 48.5% for GPT-5.6 Luna.
GPT-5.6 Luna vs Microsoft Phi-4 (14B)
Microsoft Phi-4 (14B) is 33.3% cheaper for input tokens ($0.12 vs. $0.18 per 1M tokens) and $0.36 vs. $0.72 for output tokens (1.5x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (20ms faster than Microsoft Phi-4 (14B)). GPT-5.6 Luna leads coding benchmarks at 48.5% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
GPT-5.6 Luna vs Qwen 2.5 72B Instruct
GPT-5.6 Luna is 48.6% cheaper for input tokens ($0.18 vs. $0.35 per 1M tokens) and $0.72 vs. $0.40 for output tokens (1.9x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (330ms faster than Qwen 2.5 72B Instruct). GPT-5.6 Luna leads coding benchmarks at 48.5% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
GPT-5.6 Luna vs Qwen 2.5 Max
GPT-5.6 Luna is 35.7% cheaper for input tokens ($0.18 vs. $0.28 per 1M tokens) and $0.72 vs. $0.84 for output tokens (1.6x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (390ms faster than Qwen 2.5 Max). GPT-5.6 Luna leads coding benchmarks at 48.5% SWE-bench vs. 44.2% for Qwen 2.5 Max.
GPT-5.6 Luna vs Qwen 3.8 Flash Next
Qwen 3.8 Flash Next is 33.3% cheaper for input tokens ($0.12 vs. $0.18 per 1M tokens) and $0.48 vs. $0.72 for output tokens (1.5x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (30ms faster than Qwen 3.8 Flash Next). Qwen 3.8 Flash Next leads coding benchmarks at 56.8% SWE-bench vs. 48.5% for GPT-5.6 Luna.
Frequently Asked Questions & Query Fan-Out
How much does GPT-5.6 Luna cost per 1M tokens?
GPT-5.6 Luna costs $0.18 per million prompt (input) tokens and $0.72 per million completion (output) tokens.
What is the context window for GPT-5.6 Luna?
GPT-5.6 Luna supports a maximum context window of 131,072 tokens, with a maximum single-generation output of 16,384 tokens.
What are the primary use cases for GPT-5.6 Luna?
Real-time conversational agents, high-volume classification, intent detection, and low-latency interactive apps.