GPT-5.6 Luna vs Grok 4.5
Head-to-head specifications
| Metric | GPT-5.6 Luna | Grok 4.5 | Difference |
|---|---|---|---|
| Intelligence Index | 51.0 | 54.0 | -5.6% |
| Coding Index | 71.4 | 72.4 | -1.4% |
| Agentic Index | 45.6 | 45.7 | — |
| Context window | 1M tokens | 922K tokens | — |
| Blended price ($/1M tokens) | $0.64 | $0.87 | -26.4% |
| Output speed (tokens/s) | 220 | 118 | +86.4% |
| Access | Proprietary API | Proprietary API | — |
- Grok 4.5 leads overall capability (Intelligence Index 54.0 vs 51.0).
- GPT-5.6 Luna is the cheaper model to run at $0.64/1M blended tokens — about 1.4× cheaper.
- GPT-5.6 Luna offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: GPT-5.6 Luna or Grok 4.5?
GPT-5.6 Luna advantages
- Context window (+8%)
- Affordability (+26%)
- Output speed (+46%)
Grok 4.5 advantages
- General intelligence (+6%)
Which should you choose?
- Choose the GPT-5.6 Luna if you work with long documents or large codebases.
- Choose the Grok 4.5 if you need the strongest overall reasoning and accuracy.
- Choose the GPT-5.6 Luna if you want the lowest cost per token at scale.
Value for money
GPT-5.6 Luna offers more intelligence per dollar (1.3× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
GPT-5.6 Luna vs Grok 4.5: 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.
Grok 4.5 — xAI multimodal model with an Intelligence Index of 54, a 922K-token context window and a blended price of $0.87/1M tokens.
GPT-5.6 Luna vs Grok 4.5: Grok 4.5 scores higher on the Intelligence Index. Grok 4.5 leads overall capability (Intelligence Index 54.0 vs 51.0). GPT-5.6 Luna is the cheaper model to run at $0.64/1M blended tokens — about 1.4× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Grok 4.5 scores 54.0 versus 51.0. For software development, the Coding Index puts Grok 4.5 ahead (72.4 vs 71.4). On agentic, multi-step tool-use tasks, Grok 4.5 measures stronger. 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 118 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 $0.87 per 1M tokens). GPT-5.6 Luna is proprietary api and Grok 4.5 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 Grok 4.5?
GPT-5.6 Luna takes the overall edge, though Grok 4.5 wins in specific areas worth weighing. Grok 4.5 leads overall capability (Intelligence Index 54.0 vs 51.0).
What is the main difference between the GPT-5.6 Luna and the Grok 4.5?
Grok 4.5 leads overall capability (Intelligence Index 54.0 vs 51.0). GPT-5.6 Luna is the cheaper model to run at $0.64/1M blended tokens — about 1.4× cheaper.
Which is better value?
GPT-5.6 Luna offers more intelligence per dollar (1.3× 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 work with long documents or large codebases. Choose the Grok 4.5 if you need the strongest overall reasoning and accuracy.
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.