GPT-5.6 Luna vs o4-mini
Head-to-head specifications
| Metric | GPT-5.6 Luna | o4-mini | Difference |
|---|---|---|---|
| Intelligence Index | 51.0 | 29.0 | +75.9% |
| Context window | 1M tokens | 256K tokens | — |
| Blended price ($/1M tokens) | $0.64 | $0.64 | — |
| Output speed (tokens/s) | 220 | 167 | +31.7% |
| Access | Proprietary API | Proprietary API | — |
- GPT-5.6 Luna leads overall capability (Intelligence Index 51.0 vs 29.0).
- Both cost about the same to run (~$0.64/1M blended tokens), so capability and speed should decide.
- GPT-5.6 Luna offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: GPT-5.6 Luna or o4-mini?
GPT-5.6 Luna advantages
- General intelligence (+43%)
- Context window (+74%)
- Output speed (+24%)
o4-mini advantages
- No decisive advantage on the tracked metrics.
Which should you choose?
- Choose the GPT-5.6 Luna if you need the strongest overall reasoning and accuracy.
- Choose the GPT-5.6 Luna if you work with long documents or large codebases.
Value for money
GPT-5.6 Luna offers more intelligence per dollar (1.8× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
GPT-5.6 Luna vs o4-mini: which should you choose?
GPT-5.6 Luna — OpenAI multimodal model with an Intelligence Index of 51, a 1M-token context window and a blended price of $0.64/1M tokens.
o4-mini — OpenAI multimodal model with an Intelligence Index of 29, a 256K-token context window and a blended price of $0.64/1M tokens.
GPT-5.6 Luna vs o4-mini: GPT-5.6 Luna scores higher on the Intelligence Index. GPT-5.6 Luna leads overall capability (Intelligence Index 51.0 vs 29.0). Both cost about the same to run (~$0.64/1M blended tokens), so capability and speed should decide.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5.6 Luna scores 51.0 versus 29.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The GPT-5.6 Luna accepts up to 1 million tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, GPT-5.6 Luna generates faster (220 vs 167 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, GPT-5.6 Luna is the cheaper model to run ($0.64 vs $0.64 per 1M tokens). GPT-5.6 Luna is proprietary api and o4-mini is proprietary api. Open-weight models can be self-hosted, trading per-call cost for infrastructure you manage; for production also weigh rate limits, throughput and data-residency requirements.
The verdict
Both are credible choices in the ai model comparison space; the specification table above lays out every metric so you can weigh the trade-offs that matter to you. Pick the one whose strengths line up with how you will actually use it.
Frequently asked questions
Is the GPT-5.6 Luna better than the o4-mini?
GPT-5.6 Luna is the clearly stronger overall choice, winning most of the dimensions that matter. GPT-5.6 Luna leads overall capability (Intelligence Index 51.0 vs 29.0).
What is the main difference between the GPT-5.6 Luna and the o4-mini?
GPT-5.6 Luna leads overall capability (Intelligence Index 51.0 vs 29.0). Both cost about the same to run (~$0.64/1M blended tokens), so capability and speed should decide.
Which is better value?
GPT-5.6 Luna offers more intelligence per dollar (1.8× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Which should I choose?
Choose the GPT-5.6 Luna if you need the strongest overall reasoning and accuracy. Choose the GPT-5.6 Luna if you work with long documents or large codebases.
Methodology
Large language models are compared on independent leaderboard metrics: an Intelligence Index (a composite of reasoning and knowledge evaluations), Coding and Agentic indices where measured, community arena Elo, maximum context window, a blended API price per million tokens (weighted across cache-hit, input and output rates), and measured output speed in tokens per second. Where a model ships multiple reasoning-effort variants, we report its strongest variant. Benchmarks capture only part of real-world quality, which also depends on tool use, latency, safety and task fit — and this space moves quickly, so figures reflect the leaderboard snapshot on the page date.