Grok 4 vs GLM-4.7
Head-to-head specifications
| Metric | Grok 4 | GLM-4.7 | Difference |
|---|---|---|---|
| Intelligence Index | 34.0 | 30.0 | +13.3% |
| Context window | 400K tokens | 256K tokens | — |
| Blended price ($/1M tokens) | $1.68 | $0.60 | +180.0% |
| Access | Proprietary API | Open weights | — |
- Grok 4 leads overall capability (Intelligence Index 34.0 vs 30.0).
- GLM-4.7 is the cheaper model to run at $0.60/1M blended tokens — about 2.8× cheaper.
- Grok 4 offers the larger context window (400K tokens), useful for long documents and codebases.
Verdict: Grok 4 or GLM-4.7?
Grok 4 advantages
- General intelligence (+12%)
- Context window (+36%)
GLM-4.7 advantages
- Affordability (+64%)
Which should you choose?
- Choose the Grok 4 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 Grok 4 if you work with long documents or large codebases.
Value for money
GLM-4.7 offers more intelligence per dollar (2.5× 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.
Grok 4 vs GLM-4.7: which should you choose?
Grok 4 — xAI multimodal model with an Intelligence Index of 34, a 400K-token context window and a blended price of $1.68/1M tokens.
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).
Grok 4 vs GLM-4.7: Grok 4 scores higher on the Intelligence Index. Grok 4 leads overall capability (Intelligence Index 34.0 vs 30.0). GLM-4.7 is the cheaper model to run at $0.60/1M blended tokens — about 2.8× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Grok 4 scores 34.0 versus 30.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The Grok 4 accepts up to 400K tokens per request, which sets how much documentation, transcript or code it can reason over at once.
Pricing and access
At blended per-token rates, GLM-4.7 is the cheaper model to run ($0.60 vs $1.68 per 1M tokens). Grok 4 is proprietary api 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 Grok 4 better than the GLM-4.7?
Grok 4 takes the overall edge, though GLM-4.7 wins in specific areas worth weighing. Grok 4 leads overall capability (Intelligence Index 34.0 vs 30.0).
What is the main difference between the Grok 4 and the GLM-4.7?
Grok 4 leads overall capability (Intelligence Index 34.0 vs 30.0). GLM-4.7 is the cheaper model to run at $0.60/1M blended tokens — about 2.8× cheaper.
Which is better value?
GLM-4.7 offers more intelligence per dollar (2.5× 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 Grok 4 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.