As of August 20, 2026, GPT-5.6 Luna is 75% cheaper per million tokens than o4-mini. GPT-5.6 Luna is $0.20 / $1.20 per million input/output tokens versus o4-mini at $1.10 / $4.40. Source: https://developers.openai.com/api/docs/pricing
Source: OpenAI API pricing (https://developers.openai.com/api/docs/pricing)
GPT-5.6 Luna
OpenAI
Input
$0.20
Output
$1.20
o4-mini
OpenAI
Input
$1.10
Output
$4.40
Token pricing, specs, and benchmarks side by side
GPT-5.6 Lunao4-miniVerdict
GPT-5.6 Luna offers both lower pricing and stronger benchmark performance across the board, making it the clear value leader in this comparison.
As of August 20, 2026, GPT-5.6 Luna is 75% cheaper on blended LLM API pricing. GPT-5.6 Luna costs $0.20 per million input tokens and $1.20 per million output tokens ($1.40 blended 1M-in + 1M-out). o4-mini costs $1.10 / $4.40 per million tokens ($5.50 blended). Sources: https://developers.openai.com/api/docs/pricing and https://developers.openai.com/api/docs/pricing. The cheapest LLM for your app still depends on how many output tokens you generate.
GPT-5.6 Luna OpenAI API pricing is $0.20 input and $1.20 output per million tokens via OpenAI. o4-mini OpenAI API pricing is $1.10 input and $4.40 output per million tokens via OpenAI. Use those four numbers, not ChatGPT Plus or Claude Pro subscription prices, when you are comparing APIs.
GPT-5.6 Luna leads on SWE-bench Verified: GPT-5.6 Luna at 93% vs o4-mini at 68.1%. SWE-bench measures real GitHub fixes; LiveCodeBench measures contest programming.
GPT-5.6 Luna supports a 1M token context window, while o4-mini supports 200K tokens. GPT-5.6 Luna offers a larger context window. Filling a larger window bills more input tokens, so the cheaper-per-million model can still cost more on long documents.
GPT-5.6 Luna leads on 17 of 19 shared benchmarks versus o4-mini's 2 wins. Check the comparison table for GPQA Diamond, SWE-bench, MMLU, HLE, and the other evals we track. Token price and benchmark score together are the usual LLM comparison, not either number alone.
Both GPT-5.6 Luna and o4-mini are production API models. For cost-sensitive production traffic, GPT-5.6 Luna has better economics on the blended 1M-in + 1M-out scale. For maximum capability, weight the benchmark table for your domain. Many production stacks route cheap models for drafts and a frontier model for the hard turn, which is usually cheaper than sending everything to the expensive API.
Yes. Call GPT-5.6 Luna through OpenAI and o4-mini through OpenAI with separate API keys, or through a gateway that already wraps both. Keep prompts in tokens, not characters, when you estimate the invoice. AnotherWrapper templates can swap providers without rewriting auth and billing.
List rates are the latest published OpenAI API pricing and OpenAI API pricing, quoted per million tokens. Official rate sheets: https://developers.openai.com/api/docs/pricing and https://developers.openai.com/api/docs/pricing. Prompt caching, batch APIs, and committed-use discounts can change the invoice. Benchmark scores are from official publications and independent evals, updated as new numbers land.
GPT-5.6 Luna supports up to 128K output tokens per request, while o4-mini supports up to 100K output tokens. Output tokens are usually the expensive half of API pricing, so a higher max output cap is a capability, not a discount.
GPT-5.6 Luna runs at approximately 48.45216448188169 tokens/second while o4-mini runs at 115 tokens/second. o4-mini is faster in raw throughput. Throughput (tokens per second) and time-to-first-token are why two models with the same per-million-token price can still feel different in chat and agents.
Use the model pickers at the top of this LLM comparison. Search any model in the index and the URL updates. Open GPT-5.6 Luna or o4-mini from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.
This comparison covers API pricing, context window, throughput, and benchmark scores. GPT-5.6 Luna costs $0.20/M input and $1.20/M output. o4-mini costs $1.10/M input and $4.40/M output.
Rank both on the LLM leaderboard, SWE-bench, and Humanity's Last Exam.
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