GLM-5 vs Mercury 2
Head-to-head specifications
| Metric | GLM-5 | Mercury 2 | Difference |
|---|---|---|---|
| Intelligence Index | 33.0 | 29.0 | +13.8% |
| Context window | 256K tokens | 131K tokens | — |
| Blended price ($/1M tokens) | $0.52 | $0.14 | +271.4% |
| Output speed (tokens/s) | 46 | 763 | -94.0% |
| Access | Open weights | Open weights | — |
- GLM-5 leads overall capability (Intelligence Index 33.0 vs 29.0).
- Mercury 2 is the cheaper model to run at $0.14/1M blended tokens — about 3.7× cheaper.
- GLM-5 offers the larger context window (256K tokens), useful for long documents and codebases.
Verdict: GLM-5 or Mercury 2?
GLM-5 advantages
- General intelligence (+12%)
- Context window (+49%)
Mercury 2 advantages
- Affordability (+73%)
- Output speed (+94%)
Which should you choose?
- Choose the GLM-5 if you need the strongest overall reasoning and accuracy.
- Choose the Mercury 2 if you want the lowest cost per token at scale.
- Choose the GLM-5 if you work with long documents or large codebases.
Value for money
Mercury 2 offers more intelligence per dollar (3.3× 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 vs Mercury 2: which should you choose?
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).
Mercury 2 — Inception Labs multimodal model with an Intelligence Index of 29, a 131K-token context window and a blended price of $0.14/1M tokens (open weights).
GLM-5 vs Mercury 2: GLM-5 scores higher on the Intelligence Index. GLM-5 leads overall capability (Intelligence Index 33.0 vs 29.0). Mercury 2 is the cheaper model to run at $0.14/1M blended tokens — about 3.7× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GLM-5 scores 33.0 versus 29.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The GLM-5 accepts up to 256K tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, Mercury 2 generates faster (763 vs 46 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, Mercury 2 is the cheaper model to run ($0.14 vs $0.52 per 1M tokens). GLM-5 is open weights and Mercury 2 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 GLM-5 better than the Mercury 2?
These two are closely matched — the right pick comes down to which specific strengths you value and the price you actually pay. GLM-5 leads overall capability (Intelligence Index 33.0 vs 29.0).
What is the main difference between the GLM-5 and the Mercury 2?
GLM-5 leads overall capability (Intelligence Index 33.0 vs 29.0). Mercury 2 is the cheaper model to run at $0.14/1M blended tokens — about 3.7× cheaper.
Which is better value?
Mercury 2 offers more intelligence per dollar (3.3× 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 if you need the strongest overall reasoning and accuracy. Choose the Mercury 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.