GLM-4.7 vs Qwen3 Max Thinking (Preview)
Head-to-head specifications
| Metric | GLM-4.7 | Qwen3 Max Thinking (Preview) | Difference |
|---|---|---|---|
| Intelligence Index | 30.0 | 28.0 | +7.1% |
| Context window | 256K tokens | 512K tokens | — |
| Blended price ($/1M tokens) | $0.60 | $0.90 | -33.3% |
| Output speed (tokens/s) | 87 | 55 | +58.2% |
| Access | Open weights | Open weights | — |
- GLM-4.7 leads overall capability (Intelligence Index 30.0 vs 28.0).
- GLM-4.7 is the cheaper model to run at $0.60/1M blended tokens — about 1.5× cheaper.
- Qwen3 Max Thinking (Preview) offers the larger context window (512K tokens), useful for long documents and codebases.
Verdict: GLM-4.7 or Qwen3 Max Thinking (Preview)?
GLM-4.7 advantages
- General intelligence (+7%)
- Affordability (+33%)
- Output speed (+37%)
Qwen3 Max Thinking (Preview) advantages
- Context window (+50%)
Which should you choose?
- Choose the GLM-4.7 if you need the strongest overall reasoning and accuracy.
- Choose the Qwen3 Max Thinking (Preview) if you work with long documents or large codebases.
- Choose the GLM-4.7 if you want the lowest cost per token at scale.
Value for money
GLM-4.7 offers more intelligence per dollar (1.6× 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-4.7 vs Qwen3 Max Thinking (Preview): 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).
Qwen3 Max Thinking (Preview) — Alibaba text model with an Intelligence Index of 28, a 512K-token context window and a blended price of $0.9/1M tokens (open weights).
GLM-4.7 vs Qwen3 Max Thinking (Preview): GLM-4.7 scores higher on the Intelligence Index. GLM-4.7 leads overall capability (Intelligence Index 30.0 vs 28.0). GLM-4.7 is the cheaper model to run at $0.60/1M blended tokens — about 1.5× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GLM-4.7 scores 30.0 versus 28.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The Qwen3 Max Thinking (Preview) accepts up to 512K 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 55 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 $0.90 per 1M tokens). GLM-4.7 is open weights and Qwen3 Max Thinking (Preview) 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-4.7 better than the Qwen3 Max Thinking (Preview)?
GLM-4.7 is the clearly stronger overall choice, winning most of the dimensions that matter. GLM-4.7 leads overall capability (Intelligence Index 30.0 vs 28.0).
What is the main difference between the GLM-4.7 and the Qwen3 Max Thinking (Preview)?
GLM-4.7 leads overall capability (Intelligence Index 30.0 vs 28.0). GLM-4.7 is the cheaper model to run at $0.60/1M blended tokens — about 1.5× cheaper.
Which is better value?
GLM-4.7 offers more intelligence per dollar (1.6× 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-4.7 if you need the strongest overall reasoning and accuracy. Choose the Qwen3 Max Thinking (Preview) 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.