DeepSeek V3.2 vs GLM-4.7
Head-to-head specifications
| Metric | DeepSeek V3.2 | GLM-4.7 | Difference |
|---|---|---|---|
| Intelligence Index | 28.0 | 30.0 | -6.7% |
| Coding Index | 44.2 | 45.3 | -2.4% |
| Agentic Index | 18.3 | 25.4 | — |
| Context window | 200K tokens | 256K tokens | — |
| Blended price ($/1M tokens) | $0.11 | $0.60 | -81.7% |
| Access | Open weights | Open weights | — |
- GLM-4.7 leads overall capability (Intelligence Index 30.0 vs 28.0).
- DeepSeek V3.2 is the cheaper model to run at $0.11/1M blended tokens — about 5.5× cheaper.
- GLM-4.7 offers the larger context window (256K tokens), useful for long documents and codebases.
Verdict: DeepSeek V3.2 or GLM-4.7?
DeepSeek V3.2 advantages
- Affordability (+82%)
GLM-4.7 advantages
- General intelligence (+7%)
- Agentic task performance (+28%)
- Context window (+22%)
Which should you choose?
- Choose the DeepSeek V3.2 if you want the lowest cost per token at scale.
- Choose the GLM-4.7 if you need the strongest overall reasoning and accuracy.
Value for money
DeepSeek V3.2 offers more intelligence per dollar (5.1× 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.
DeepSeek V3.2 vs GLM-4.7: which should you choose?
DeepSeek V3.2 — DeepSeek text model with an Intelligence Index of 28, a 200K-token context window and a blended price of $0.11/1M 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).
DeepSeek V3.2 vs GLM-4.7: GLM-4.7 scores higher on the Intelligence Index. GLM-4.7 leads overall capability (Intelligence Index 30.0 vs 28.0). DeepSeek V3.2 is the cheaper model to run at $0.11/1M blended tokens — about 5.5× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GLM-4.7 scores 30.0 versus 28.0. For software development, the Coding Index puts GLM-4.7 ahead (45.3 vs 44.2). On agentic, multi-step tool-use tasks, GLM-4.7 measures stronger. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The GLM-4.7 accepts up to 256K 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, DeepSeek V3.2 is the cheaper model to run ($0.11 vs $0.60 per 1M tokens). DeepSeek V3.2 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 DeepSeek V3.2 better than the GLM-4.7?
GLM-4.7 takes the overall edge, though DeepSeek V3.2 wins in specific areas worth weighing. GLM-4.7 leads overall capability (Intelligence Index 30.0 vs 28.0).
What is the main difference between the DeepSeek V3.2 and the GLM-4.7?
GLM-4.7 leads overall capability (Intelligence Index 30.0 vs 28.0). DeepSeek V3.2 is the cheaper model to run at $0.11/1M blended tokens — about 5.5× cheaper.
Which is better value?
DeepSeek V3.2 offers more intelligence per dollar (5.1× 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 DeepSeek V3.2 if you want the lowest cost per token at scale. Choose the GLM-4.7 if you need the strongest overall reasoning and accuracy.
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.