Qwen3.6 Max Preview vs GLM-4.7
Head-to-head specifications
| Metric | Qwen3.6 Max Preview | GLM-4.7 | Difference |
|---|---|---|---|
| Intelligence Index | 40.0 | 30.0 | +33.3% |
| Context window | 400K tokens | 256K tokens | — |
| Blended price ($/1M tokens) | $0.70 | $0.60 | +16.7% |
| Output speed (tokens/s) | 42 | 87 | -51.7% |
| Access | Open weights | Open weights | — |
- Qwen3.6 Max Preview leads overall capability (Intelligence Index 40.0 vs 30.0).
- GLM-4.7 is the cheaper model to run at $0.60/1M blended tokens — about 1.2× cheaper.
- Qwen3.6 Max Preview offers the larger context window (400K tokens), useful for long documents and codebases.
Verdict: Qwen3.6 Max Preview or GLM-4.7?
Qwen3.6 Max Preview advantages
- General intelligence (+25%)
- Context window (+36%)
GLM-4.7 advantages
- Affordability (+14%)
- Output speed (+52%)
Which should you choose?
- Choose the Qwen3.6 Max Preview 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 Qwen3.6 Max Preview if you work with long documents or large codebases.
Value for money
Qwen3.6 Max Preview offers more intelligence per dollar (1.1× 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.
Qwen3.6 Max Preview vs GLM-4.7: which should you choose?
Qwen3.6 Max Preview — Alibaba multimodal model with an Intelligence Index of 40, a 400K-token context window and a blended price of $0.7/1M tokens (open weights).
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).
Qwen3.6 Max Preview vs GLM-4.7: Qwen3.6 Max Preview scores higher on the Intelligence Index. Qwen3.6 Max Preview leads overall capability (Intelligence Index 40.0 vs 30.0). GLM-4.7 is the cheaper model to run at $0.60/1M blended tokens — about 1.2× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Qwen3.6 Max Preview scores 40.0 versus 30.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The Qwen3.6 Max Preview accepts up to 400K 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 42 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, GLM-4.7 is the cheaper model to run ($0.60 vs $0.70 per 1M tokens). Qwen3.6 Max Preview is open weights 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 Qwen3.6 Max Preview better than the GLM-4.7?
These two are closely matched — the right pick comes down to which specific strengths you value and the price you actually pay. Qwen3.6 Max Preview leads overall capability (Intelligence Index 40.0 vs 30.0).
What is the main difference between the Qwen3.6 Max Preview and the GLM-4.7?
Qwen3.6 Max Preview leads overall capability (Intelligence Index 40.0 vs 30.0). GLM-4.7 is the cheaper model to run at $0.60/1M blended tokens — about 1.2× cheaper.
Which is better value?
Qwen3.6 Max Preview offers more intelligence per dollar (1.1× 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 Qwen3.6 Max Preview 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.