GLM-5.2 vs Gemini 2.5 Pro (May)
Head-to-head specifications
| Metric | GLM-5.2 | Gemini 2.5 Pro (May) | Difference |
|---|---|---|---|
| Intelligence Index | 51.0 | 27.0 | +88.9% |
| Context window | 1M tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $0.65 | $0.86 | -24.4% |
| Access | Open weights | Proprietary API | — |
- GLM-5.2 leads overall capability (Intelligence Index 51.0 vs 27.0).
- GLM-5.2 is the cheaper model to run at $0.65/1M blended tokens — about 1.3× cheaper.
Verdict: GLM-5.2 or Gemini 2.5 Pro (May)?
GLM-5.2 advantages
- General intelligence (+47%)
- Affordability (+24%)
Gemini 2.5 Pro (May) advantages
- No decisive advantage on the tracked metrics.
Which should you choose?
- Choose the GLM-5.2 if you need the strongest overall reasoning and accuracy.
- Choose the GLM-5.2 if you want the lowest cost per token at scale.
Value for money
GLM-5.2 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.
GLM-5.2 vs Gemini 2.5 Pro (May): which should you choose?
GLM-5.2 — Z.ai (Zhipu) text model with an Intelligence Index of 51, a 1M-token context window and a blended price of $0.65/1M tokens (open weights).
Gemini 2.5 Pro (May) — Google multimodal model with an Intelligence Index of 27, a 1M-token context window and a blended price of $0.86/1M tokens.
GLM-5.2 vs Gemini 2.5 Pro (May): GLM-5.2 scores higher on the Intelligence Index. GLM-5.2 leads overall capability (Intelligence Index 51.0 vs 27.0). GLM-5.2 is the cheaper model to run at $0.65/1M blended tokens — about 1.3× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GLM-5.2 scores 51.0 versus 27.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The GLM-5.2 accepts up to 1 million 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-5.2 is the cheaper model to run ($0.65 vs $0.86 per 1M tokens). GLM-5.2 is open weights and Gemini 2.5 Pro (May) 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-5.2 better than the Gemini 2.5 Pro (May)?
GLM-5.2 is the clearly stronger overall choice, winning most of the dimensions that matter. GLM-5.2 leads overall capability (Intelligence Index 51.0 vs 27.0).
What is the main difference between the GLM-5.2 and the Gemini 2.5 Pro (May)?
GLM-5.2 leads overall capability (Intelligence Index 51.0 vs 27.0). GLM-5.2 is the cheaper model to run at $0.65/1M blended tokens — about 1.3× cheaper.
Which is better value?
GLM-5.2 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 GLM-5.2 if you need the strongest overall reasoning and accuracy. Choose the GLM-5.2 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.