AI Model Comparison

Grok 4 vs GLM-5

Verdict
Grok 4 vs GLM-5: Grok 4 scores higher on the Intelligence Index

Head-to-head specifications

MetricGrok 4GLM-5Difference
Intelligence Index34.033.0+3.0%
Context window400K tokens256K tokens
Blended price ($/1M tokens)$1.68$0.52+223.1%
AccessProprietary APIOpen weights
  • Grok 4 leads overall capability (Intelligence Index 34.0 vs 33.0).
  • GLM-5 is the cheaper model to run at $0.52/1M blended tokens — about 3.2× cheaper.
  • Grok 4 offers the larger context window (400K tokens), useful for long documents and codebases.

Verdict: Grok 4 or GLM-5?

Our recommendation
GLM-5 takes the overall edge, though Grok 4 wins in specific areas worth weighing.

Grok 4 advantages

  • Context window (+36%)

GLM-5 advantages

  • Affordability (+69%)

Which should you choose?

  • Choose the Grok 4 if you work with long documents or large codebases.
  • Choose the GLM-5 if you want the lowest cost per token at scale.

Value for money

GLM-5 offers more intelligence per dollar (3.1× 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.

Grok 4 vs GLM-5: 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-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).

Grok 4 vs GLM-5: Grok 4 scores higher on the Intelligence Index. Grok 4 leads overall capability (Intelligence Index 34.0 vs 33.0). GLM-5 is the cheaper model to run at $0.52/1M blended tokens — about 3.2× cheaper.

Capability: intelligence, coding and agentic work

On the composite Intelligence Index the Grok 4 scores 34.0 versus 33.0. 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-5 is the cheaper model to run ($0.52 vs $1.68 per 1M tokens). Grok 4 is proprietary api 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 Grok 4 better than the GLM-5?

GLM-5 takes the overall edge, though Grok 4 wins in specific areas worth weighing. Grok 4 leads overall capability (Intelligence Index 34.0 vs 33.0).

What is the main difference between the Grok 4 and the GLM-5?

Grok 4 leads overall capability (Intelligence Index 34.0 vs 33.0). GLM-5 is the cheaper model to run at $0.52/1M blended tokens — about 3.2× cheaper.

Which is better value?

GLM-5 offers more intelligence per dollar (3.1× 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 Grok 4 if you work with long documents or large codebases. 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.

ER
EquivalentTo Research
Data & Benchmarks Team

We compile published benchmark results (Cinebench 2024, Geekbench 6, AnTuTu v10, 3DMark), manufacturer specifications and market pricing from nine regions into normalized, comparable datasets. Every figure traces to a named public source listed on each page.

Benchmark leaderboard compilationMulti-market pricing normalizationUnit & currency conversion
✓ Reviewed by EquivalentTo Editorial Review, Data Quality & Methodology.
Last updated 2026-07-01
Grok 4 profile → GLM-5 profile → Compare something else

Related comparisons