Muse Spark 1.1 vs GPT-5.6 Luna
Head-to-head specifications
| Metric | Muse Spark 1.1 | GPT-5.6 Luna | Difference |
|---|---|---|---|
| Intelligence Index | 51.0 | 51.0 | — |
| Coding Index | 71.3 | 71.4 | -0.1% |
| Agentic Index | 37.5 | 45.6 | — |
| Context window | 1M tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $0.62 | $0.64 | -3.1% |
| Output speed (tokens/s) | 118 | 220 | -46.4% |
| Access | Proprietary API | Proprietary API | — |
- Muse Spark 1.1 leads overall capability (Intelligence Index 51.0 vs 51.0).
- Both cost about the same to run (~$0.62/1M blended tokens), so capability and speed should decide.
Verdict: Muse Spark 1.1 or GPT-5.6 Luna?
Muse Spark 1.1 advantages
- No decisive advantage on the tracked metrics.
GPT-5.6 Luna advantages
- Agentic task performance (+18%)
- Output speed (+46%)
Which should you choose?
- Choose the GPT-5.6 Luna if you build agents or multi-step tool-use workflows.
Value for money
Muse Spark 1.1 offers more intelligence per dollar (1.0× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Muse Spark 1.1 vs GPT-5.6 Luna: which should you choose?
Muse Spark 1.1 — Muse multimodal model with an Intelligence Index of 51, a 1M-token context window and a blended price of $0.62/1M tokens.
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.
Muse Spark 1.1 vs GPT-5.6 Luna: Muse Spark 1.1 scores higher on the Intelligence Index. Muse Spark 1.1 leads overall capability (Intelligence Index 51.0 vs 51.0). Both cost about the same to run (~$0.62/1M blended tokens), so capability and speed should decide.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Muse Spark 1.1 scores 51.0 versus 51.0. For software development, the Coding Index puts GPT-5.6 Luna ahead (71.4 vs 71.3). On agentic, multi-step tool-use tasks, GPT-5.6 Luna measures stronger. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The Muse Spark 1.1 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, Muse Spark 1.1 is the cheaper model to run ($0.62 vs $0.64 per 1M tokens). Muse Spark 1.1 is proprietary api and GPT-5.6 Luna 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 Muse Spark 1.1 better than the GPT-5.6 Luna?
GPT-5.6 Luna is the clearly stronger overall choice, winning most of the dimensions that matter. Muse Spark 1.1 leads overall capability (Intelligence Index 51.0 vs 51.0).
What is the main difference between the Muse Spark 1.1 and the GPT-5.6 Luna?
Muse Spark 1.1 leads overall capability (Intelligence Index 51.0 vs 51.0). Both cost about the same to run (~$0.62/1M blended tokens), so capability and speed should decide.
Which is better value?
Muse Spark 1.1 offers more intelligence per dollar (1.0× 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 build agents or multi-step tool-use workflows.
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.