GLM-5 vs GPT-5.6 Terra
Head-to-head specifications
| Metric | GLM-5 | GPT-5.6 Terra | Difference |
|---|---|---|---|
| Intelligence Index | 33.0 | 55.0 | -40.0% |
| Context window | 256K tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $0.52 | $1.14 | -54.4% |
| Output speed (tokens/s) | 46 | 138 | -66.7% |
| Access | Open weights | Proprietary API | — |
- GPT-5.6 Terra leads overall capability (Intelligence Index 55.0 vs 33.0).
- GLM-5 is the cheaper model to run at $0.52/1M blended tokens — about 2.2× cheaper.
- GPT-5.6 Terra offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: GLM-5 or GPT-5.6 Terra?
GLM-5 advantages
- Affordability (+54%)
GPT-5.6 Terra advantages
- General intelligence (+40%)
- Context window (+74%)
- Output speed (+67%)
Which should you choose?
- Choose the GLM-5 if you want the lowest cost per token at scale.
- Choose the GPT-5.6 Terra if you need the strongest overall reasoning and accuracy.
Value for money
GLM-5 offers more intelligence per dollar (1.3× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
GLM-5 vs GPT-5.6 Terra: which should you choose?
GLM-5 — Z.ai (Zhipu) text model with an Intelligence Index of 33, a 256K-token context window and a blended price of $0.52/1M tokens (open weights).
GPT-5.6 Terra — OpenAI multimodal model with an Intelligence Index of 55, a 1M-token context window and a blended price of $1.14/1M tokens.
GLM-5 vs GPT-5.6 Terra: GPT-5.6 Terra scores higher on the Intelligence Index. GPT-5.6 Terra leads overall capability (Intelligence Index 55.0 vs 33.0). GLM-5 is the cheaper model to run at $0.52/1M blended tokens — about 2.2× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5.6 Terra scores 55.0 versus 33.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The GPT-5.6 Terra 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 Terra generates faster (138 vs 46 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, GLM-5 is the cheaper model to run ($0.52 vs $1.14 per 1M tokens). GLM-5 is open weights and GPT-5.6 Terra 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 GLM-5 better than the GPT-5.6 Terra?
GPT-5.6 Terra takes the overall edge, though GLM-5 wins in specific areas worth weighing. GPT-5.6 Terra leads overall capability (Intelligence Index 55.0 vs 33.0).
What is the main difference between the GLM-5 and the GPT-5.6 Terra?
GPT-5.6 Terra leads overall capability (Intelligence Index 55.0 vs 33.0). GLM-5 is the cheaper model to run at $0.52/1M blended tokens — about 2.2× cheaper.
Which is better value?
GLM-5 offers more intelligence per dollar (1.3× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
Which should I choose?
Choose the GLM-5 if you want the lowest cost per token at scale. Choose the GPT-5.6 Terra 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.