GLM-5.1 vs GLM-5
Head-to-head specifications
| Metric | GLM-5.1 | GLM-5 | Difference |
|---|---|---|---|
| Intelligence Index | 35.0 | 33.0 | +6.1% |
| Context window | 256K tokens | 256K tokens | — |
| Blended price ($/1M tokens) | $0.66 | $0.52 | +26.9% |
| Output speed (tokens/s) | 59 | 46 | +28.3% |
| Access | Open weights | Open weights | — |
- GLM-5.1 leads overall capability (Intelligence Index 35.0 vs 33.0).
- GLM-5 is the cheaper model to run at $0.52/1M blended tokens — about 1.3× cheaper.
Verdict: GLM-5.1 or GLM-5?
GLM-5.1 advantages
- General intelligence (+6%)
- Output speed (+22%)
GLM-5 advantages
- Affordability (+21%)
Which should you choose?
- Choose the GLM-5.1 if you need the strongest overall reasoning and accuracy.
- Choose the GLM-5 if you want the lowest cost per token at scale.
- Choose the GLM-5.1 if low latency and fast generation matter for your application.
Value for money
GLM-5 offers more intelligence per dollar (1.2× 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-5: 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-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).
GLM-5.1 vs GLM-5: GLM-5.1 scores higher on the Intelligence Index. GLM-5.1 leads overall capability (Intelligence Index 35.0 vs 33.0). GLM-5 is the cheaper model to run at $0.52/1M blended tokens — about 1.3× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GLM-5.1 scores 35.0 versus 33.0. 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-5.1 generates faster (59 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 $0.66 per 1M tokens). GLM-5.1 is open weights and GLM-5 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-5?
GLM-5.1 takes the overall edge, though GLM-5 wins in specific areas worth weighing. GLM-5.1 leads overall capability (Intelligence Index 35.0 vs 33.0).
What is the main difference between the GLM-5.1 and the GLM-5?
GLM-5.1 leads overall capability (Intelligence Index 35.0 vs 33.0). GLM-5 is the cheaper model to run at $0.52/1M blended tokens — about 1.3× cheaper.
Which is better value?
GLM-5 offers more intelligence per dollar (1.2× 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-5 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.