DeepSeek V3.1 Terminus vs GLM-5.1
Head-to-head specifications
| Metric | DeepSeek V3.1 Terminus | GLM-5.1 | Difference |
|---|---|---|---|
| Intelligence Index | 26.0 | 35.0 | -25.7% |
| Coding Index | 43.5 | 55.8 | -22.0% |
| Agentic Index | 18.1 | 29.9 | — |
| Context window | 200K tokens | 256K tokens | — |
| Blended price ($/1M tokens) | $0.31 | $0.66 | -53.0% |
| Access | Open weights | Open weights | — |
- GLM-5.1 leads overall capability (Intelligence Index 35.0 vs 26.0).
- DeepSeek V3.1 Terminus is the cheaper model to run at $0.31/1M blended tokens — about 2.1× cheaper.
- GLM-5.1 offers the larger context window (256K tokens), useful for long documents and codebases.
Verdict: DeepSeek V3.1 Terminus or GLM-5.1?
DeepSeek V3.1 Terminus advantages
- Affordability (+53%)
GLM-5.1 advantages
- General intelligence (+26%)
- Coding ability (+22%)
- Agentic task performance (+39%)
- Context window (+22%)
Which should you choose?
- Choose the DeepSeek V3.1 Terminus if you want the lowest cost per token at scale.
- Choose the GLM-5.1 if you need the strongest overall reasoning and accuracy.
Value for money
DeepSeek V3.1 Terminus 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.
DeepSeek V3.1 Terminus vs GLM-5.1: which should you choose?
DeepSeek V3.1 Terminus — DeepSeek text model with an Intelligence Index of 26, a 200K-token context window and a blended price of $0.31/1M tokens (open weights).
GLM-5.1 — Z.ai (Zhipu) text model with an Intelligence Index of 35, a 256K-token context window and a blended price of $0.66/1M tokens (open weights).
DeepSeek V3.1 Terminus vs GLM-5.1: GLM-5.1 scores higher on the Intelligence Index. GLM-5.1 leads overall capability (Intelligence Index 35.0 vs 26.0). DeepSeek V3.1 Terminus is the cheaper model to run at $0.31/1M blended tokens — about 2.1× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GLM-5.1 scores 35.0 versus 26.0. For software development, the Coding Index puts GLM-5.1 ahead (55.8 vs 43.5). On agentic, multi-step tool-use tasks, GLM-5.1 measures stronger. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The GLM-5.1 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 V3.1 Terminus is the cheaper model to run ($0.31 vs $0.66 per 1M tokens). DeepSeek V3.1 Terminus is open weights and GLM-5.1 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 V3.1 Terminus better than the GLM-5.1?
GLM-5.1 takes the overall edge, though DeepSeek V3.1 Terminus wins in specific areas worth weighing. GLM-5.1 leads overall capability (Intelligence Index 35.0 vs 26.0).
What is the main difference between the DeepSeek V3.1 Terminus and the GLM-5.1?
GLM-5.1 leads overall capability (Intelligence Index 35.0 vs 26.0). DeepSeek V3.1 Terminus is the cheaper model to run at $0.31/1M blended tokens — about 2.1× cheaper.
Which is better value?
DeepSeek V3.1 Terminus 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 DeepSeek V3.1 Terminus if you want the lowest cost per token at scale. Choose the GLM-5.1 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.