GPT-5.6 Luna vs GLM-5.2
Head-to-head specifications
| Metric | GPT-5.6 Luna | GLM-5.2 | Difference |
|---|---|---|---|
| Intelligence Index | 51.0 | 51.0 | — |
| Coding Index | 71.4 | 68.8 | +3.8% |
| Agentic Index | 45.6 | 43.1 | — |
| Context window | 1M tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $0.64 | $0.65 | -1.5% |
| Output speed (tokens/s) | 220 | 154 | +42.9% |
| Access | Proprietary API | Open weights | — |
- GPT-5.6 Luna leads overall capability (Intelligence Index 51.0 vs 51.0).
- Both cost about the same to run (~$0.64/1M blended tokens), so capability and speed should decide.
Verdict: GPT-5.6 Luna or GLM-5.2?
GPT-5.6 Luna advantages
- Agentic task performance (+5%)
- Output speed (+30%)
GLM-5.2 advantages
- No decisive advantage on the tracked metrics.
Which should you choose?
- Choose the GPT-5.6 Luna if you build agents or multi-step tool-use workflows.
- Choose the GPT-5.6 Luna if low latency and fast generation matter for your application.
Value for money
GPT-5.6 Luna offers more intelligence per dollar (1.0× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
GPT-5.6 Luna vs GLM-5.2: 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.
GLM-5.2 — Z.ai (Zhipu) text model with an Intelligence Index of 51, a 1M-token context window and a blended price of $0.65/1M tokens (open weights).
GPT-5.6 Luna vs GLM-5.2: GPT-5.6 Luna scores higher on the Intelligence Index. GPT-5.6 Luna leads overall capability (Intelligence Index 51.0 vs 51.0). 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 51.0. For software development, the Coding Index puts GPT-5.6 Luna ahead (71.4 vs 68.8). 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 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 154 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 $0.65 per 1M tokens). GPT-5.6 Luna is proprietary api and GLM-5.2 is open weights. 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 GLM-5.2?
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 51.0).
What is the main difference between the GPT-5.6 Luna and the GLM-5.2?
GPT-5.6 Luna leads overall capability (Intelligence Index 51.0 vs 51.0). 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 (1.0× 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. Choose the GPT-5.6 Luna if low latency and fast generation matter for your application.
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.