GLM-5.1 vs Grok 4.5
Head-to-head specifications
| Metric | GLM-5.1 | Grok 4.5 | Difference |
|---|---|---|---|
| Intelligence Index | 35.0 | 54.0 | -35.2% |
| Coding Index | 55.8 | 72.4 | -22.9% |
| Agentic Index | 29.9 | 45.7 | — |
| Context window | 256K tokens | 922K tokens | — |
| Blended price ($/1M tokens) | $0.66 | $0.87 | -24.1% |
| Output speed (tokens/s) | 59 | 118 | -50.0% |
| Access | Open weights | Proprietary API | — |
- Grok 4.5 leads overall capability (Intelligence Index 54.0 vs 35.0).
- GLM-5.1 is the cheaper model to run at $0.66/1M blended tokens — about 1.3× cheaper.
- Grok 4.5 offers the larger context window (922K tokens), useful for long documents and codebases.
Verdict: GLM-5.1 or Grok 4.5?
GLM-5.1 advantages
- Affordability (+24%)
Grok 4.5 advantages
- General intelligence (+35%)
- Coding ability (+23%)
- Agentic task performance (+35%)
- Context window (+72%)
- Output speed (+50%)
Which should you choose?
- Choose the GLM-5.1 if you want the lowest cost per token at scale.
- Choose the Grok 4.5 if you need the strongest overall reasoning and accuracy.
Value for money
Grok 4.5 offers more intelligence per dollar (1.2× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
GLM-5.1 vs Grok 4.5: 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).
Grok 4.5 — xAI multimodal model with an Intelligence Index of 54, a 922K-token context window and a blended price of $0.87/1M tokens.
GLM-5.1 vs Grok 4.5: Grok 4.5 scores higher on the Intelligence Index. Grok 4.5 leads overall capability (Intelligence Index 54.0 vs 35.0). GLM-5.1 is the cheaper model to run at $0.66/1M blended tokens — about 1.3× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Grok 4.5 scores 54.0 versus 35.0. For software development, the Coding Index puts Grok 4.5 ahead (72.4 vs 55.8). On agentic, multi-step tool-use tasks, Grok 4.5 measures stronger. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The Grok 4.5 accepts up to 922K tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, Grok 4.5 generates faster (118 vs 59 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, GLM-5.1 is the cheaper model to run ($0.66 vs $0.87 per 1M tokens). GLM-5.1 is open weights and Grok 4.5 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-5.1 better than the Grok 4.5?
Grok 4.5 is the clearly stronger overall choice, winning most of the dimensions that matter. Grok 4.5 leads overall capability (Intelligence Index 54.0 vs 35.0).
What is the main difference between the GLM-5.1 and the Grok 4.5?
Grok 4.5 leads overall capability (Intelligence Index 54.0 vs 35.0). GLM-5.1 is the cheaper model to run at $0.66/1M blended tokens — about 1.3× cheaper.
Which is better value?
Grok 4.5 offers more intelligence per dollar (1.2× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Which should I choose?
Choose the GLM-5.1 if you want the lowest cost per token at scale. Choose the Grok 4.5 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.