Compare GPT 4.1 Nano from OpenAI and GPT 5.6 Luna from OpenAI on key metrics including benchmarks, price, context length, and other model features. Access both models and hundreds of others through the AIHubMix API.
GPT 4.1 NanoGPT 5.6 LunaUltra-lightweight model with million-token context, optimized for speed and low latency, costing only $0.10 per million input tokens. It is suitable for edge computing and real-time interaction. The automatic caching mechanism offers a 75% cost reduction on cache hits.
GPT-5.6 Luna is designed for cost-sensitive, high-volume workloads. It roughly corresponds to the nano model tier used in earlier GPT-5 families.
Prices are per million tokens. Time to First Token and throughput are rolling averages measured on AIHubMix.
Promotional prices show the discounted rate; see each model page for promotion windows.
Daily traffic served through AIHubMix — how demand for each model is trending.
Measured on real AIHubMix traffic, hourly buckets. Gaps mean no traffic in that hour.
Estimate your monthly bill for the same workload on each model.
Monthly = daily × 30. Discounted rates applied where a promotion is active.
GPT 4.1 Nano: $0.4/M output tokens; GPT 5.6 Luna: $6/M. Use the cost calculator above to estimate your own workload.
GPT 4.1 Nano: 0.8s time to first token measured on AIHubMix; see the live performance charts above for how each model behaves across the day.
GPT 4.1 Nano accepts 1,047,576 and GPT 5.6 Luna accepts 1,050,000 input tokens. Maximum output per request is 32,768 tokens on GPT 4.1 Nano and 128,000 tokens on GPT 5.6 Luna.
GPT 5.6 Luna at 88.4 tok/s and GPT 4.1 Nano at 83.0 tok/s, measured as output throughput on AIHubMix — a separate metric from time to first token.
GPT 4.1 Nano accepts text, image and PDF input and supports tool calling, function calling, structured outputs and long context; GPT 5.6 Luna accepts text and image input and supports tool calling, thinking and structured outputs.
Yes. AIHubMix serves every model on this page behind one OpenAI-compatible endpoint, so switching between them is a one-line change to the model field — no second account, key or SDK.
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