Claude Sonnet 5 vs GPT-5.6 Luna
Head-to-head specifications
| Metric | Claude Sonnet 5 | GPT-5.6 Luna | Difference |
|---|---|---|---|
| Intelligence Index | 53.0 | 51.0 | +3.9% |
| Coding Index | 71.5 | 71.4 | +0.1% |
| Agentic Index | 46.7 | 45.6 | — |
| Context window | 1M tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $0.90 | $0.64 | +40.6% |
| Output speed (tokens/s) | 71 | 220 | -67.7% |
| Access | Proprietary API | Proprietary API | — |
- Claude Sonnet 5 leads overall capability (Intelligence Index 53.0 vs 51.0).
- GPT-5.6 Luna is the cheaper model to run at $0.64/1M blended tokens — about 1.4× cheaper.
Verdict: Claude Sonnet 5 or GPT-5.6 Luna?
Claude Sonnet 5 advantages
- No decisive advantage on the tracked metrics.
GPT-5.6 Luna advantages
- Affordability (+29%)
- Output speed (+68%)
Which should you choose?
- 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 (1.4× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Claude Sonnet 5 vs GPT-5.6 Luna: which should you choose?
Claude Sonnet 5 — Anthropic multimodal model with an Intelligence Index of 53, a 1M-token context window and a blended price of $0.9/1M 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.
Claude Sonnet 5 vs GPT-5.6 Luna: Claude Sonnet 5 scores higher on the Intelligence Index. Claude Sonnet 5 leads overall capability (Intelligence Index 53.0 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 Claude Sonnet 5 scores 53.0 versus 51.0. For software development, the Coding Index puts Claude Sonnet 5 ahead (71.5 vs 71.4). On agentic, multi-step tool-use tasks, Claude Sonnet 5 measures stronger. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The Claude Sonnet 5 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 71 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 $0.90 per 1M tokens). Claude Sonnet 5 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 Claude Sonnet 5 better than the GPT-5.6 Luna?
GPT-5.6 Luna is the clearly stronger overall choice, winning most of the dimensions that matter. Claude Sonnet 5 leads overall capability (Intelligence Index 53.0 vs 51.0).
What is the main difference between the Claude Sonnet 5 and the GPT-5.6 Luna?
Claude Sonnet 5 leads overall capability (Intelligence Index 53.0 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 (1.4× 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 want the lowest cost per token at scale.
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.