GLM-4.7 vs Grok 3 mini Reasoning
Head-to-head specifications
| Metric | GLM-4.7 | Grok 3 mini Reasoning | Difference |
|---|---|---|---|
| Intelligence Index | 30.0 | 27.0 | +11.1% |
| Context window | 256K tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $0.60 | $0.16 | +275.0% |
| Output speed (tokens/s) | 87 | 66 | +31.8% |
| Access | Open weights | Proprietary API | — |
- GLM-4.7 leads overall capability (Intelligence Index 30.0 vs 27.0).
- Grok 3 mini Reasoning is the cheaper model to run at $0.16/1M blended tokens — about 3.8× cheaper.
- Grok 3 mini Reasoning offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: GLM-4.7 or Grok 3 mini Reasoning?
GLM-4.7 advantages
- General intelligence (+10%)
- Output speed (+24%)
Grok 3 mini Reasoning advantages
- Context window (+74%)
- Affordability (+73%)
Which should you choose?
- Choose the GLM-4.7 if you need the strongest overall reasoning and accuracy.
- Choose the Grok 3 mini Reasoning if you work with long documents or large codebases.
- Choose the GLM-4.7 if low latency and fast generation matter for your application.
Value for money
Grok 3 mini Reasoning offers more intelligence per dollar (3.4× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
GLM-4.7 vs Grok 3 mini Reasoning: which should you choose?
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 3 mini Reasoning — xAI multimodal model with an Intelligence Index of 27, a 1M-token context window and a blended price of $0.16/1M tokens.
GLM-4.7 vs Grok 3 mini Reasoning: GLM-4.7 scores higher on the Intelligence Index. GLM-4.7 leads overall capability (Intelligence Index 30.0 vs 27.0). Grok 3 mini Reasoning is the cheaper model to run at $0.16/1M blended tokens — about 3.8× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GLM-4.7 scores 30.0 versus 27.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The Grok 3 mini Reasoning 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, GLM-4.7 generates faster (87 vs 66 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, Grok 3 mini Reasoning is the cheaper model to run ($0.16 vs $0.60 per 1M tokens). GLM-4.7 is open weights and Grok 3 mini Reasoning 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-4.7 better than the Grok 3 mini Reasoning?
These two are closely matched — the right pick comes down to which specific strengths you value and the price you actually pay. GLM-4.7 leads overall capability (Intelligence Index 30.0 vs 27.0).
What is the main difference between the GLM-4.7 and the Grok 3 mini Reasoning?
GLM-4.7 leads overall capability (Intelligence Index 30.0 vs 27.0). Grok 3 mini Reasoning is the cheaper model to run at $0.16/1M blended tokens — about 3.8× cheaper.
Which is better value?
Grok 3 mini Reasoning offers more intelligence per dollar (3.4× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Which should I choose?
Choose the GLM-4.7 if you need the strongest overall reasoning and accuracy. Choose the Grok 3 mini Reasoning if you work with long documents or large codebases.
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.