GLM-5.1 vs GLM-4.7
Head-to-head specifications
| Metric | GLM-5.1 | GLM-4.7 | Difference |
|---|---|---|---|
| Intelligence Index | 35.0 | 30.0 | +16.7% |
| Coding Index | 55.8 | 45.3 | +23.2% |
| Agentic Index | 29.9 | 25.4 | — |
| Context window | 256K tokens | 256K tokens | — |
| Blended price ($/1M tokens) | $0.66 | $0.60 | +10.0% |
| Output speed (tokens/s) | 59 | 87 | -32.2% |
| Access | Open weights | Open weights | — |
- GLM-5.1 leads overall capability (Intelligence Index 35.0 vs 30.0).
- GLM-4.7 is the cheaper model to run at $0.60/1M blended tokens — about 1.1× cheaper.
Verdict: GLM-5.1 or GLM-4.7?
GLM-5.1 advantages
- General intelligence (+14%)
- Coding ability (+19%)
- Agentic task performance (+15%)
GLM-4.7 advantages
- Affordability (+9%)
- Output speed (+32%)
Which should you choose?
- Choose the GLM-5.1 if you need the strongest overall reasoning and accuracy.
- Choose the GLM-4.7 if you want the lowest cost per token at scale.
- Choose the GLM-5.1 if coding and software development are your main workload.
Value for money
GLM-5.1 offers more intelligence per dollar (1.1× 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-5.1 vs GLM-4.7: which should you choose?
GLM-5.1 — Z.ai (Zhipu) text model with an Intelligence Index of 35, a 256K-token context window and a blended price of $0.66/1M tokens (open weights).
GLM-4.7 — Z.ai (Zhipu) text model with an Intelligence Index of 30, a 256K-token context window and a blended price of $0.6/1M tokens (open weights).
GLM-5.1 vs GLM-4.7: GLM-5.1 scores higher on the Intelligence Index. GLM-5.1 leads overall capability (Intelligence Index 35.0 vs 30.0). GLM-4.7 is the cheaper model to run at $0.60/1M blended tokens — about 1.1× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GLM-5.1 scores 35.0 versus 30.0. For software development, the Coding Index puts GLM-5.1 ahead (55.8 vs 45.3). On agentic, multi-step tool-use tasks, GLM-5.1 measures stronger. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The GLM-5.1 accepts up to 256K tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, GLM-4.7 generates faster (87 vs 59 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, GLM-4.7 is the cheaper model to run ($0.60 vs $0.66 per 1M tokens). GLM-5.1 is open weights and GLM-4.7 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 GLM-5.1 better than the GLM-4.7?
GLM-5.1 takes the overall edge, though GLM-4.7 wins in specific areas worth weighing. GLM-5.1 leads overall capability (Intelligence Index 35.0 vs 30.0).
What is the main difference between the GLM-5.1 and the GLM-4.7?
GLM-5.1 leads overall capability (Intelligence Index 35.0 vs 30.0). GLM-4.7 is the cheaper model to run at $0.60/1M blended tokens — about 1.1× cheaper.
Which is better value?
GLM-5.1 offers more intelligence per dollar (1.1× 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-5.1 if you need the strongest overall reasoning and accuracy. Choose the GLM-4.7 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.