GLM-4.7 vs Ring-2.6-1T
Head-to-head specifications
| Metric | GLM-4.7 | Ring-2.6-1T | Difference |
|---|---|---|---|
| Intelligence Index | 30.0 | 32.0 | -6.3% |
| Coding Index | 45.3 | 42.8 | +5.8% |
| Agentic Index | 25.4 | 18.9 | — |
| Context window | 256K tokens | 400K tokens | — |
| Blended price ($/1M tokens) | $0.60 | $0.43 | +39.5% |
| Output speed (tokens/s) | 87 | 124 | -29.8% |
| Access | Open weights | Open weights | — |
- Ring-2.6-1T leads overall capability (Intelligence Index 32.0 vs 30.0).
- Ring-2.6-1T is the cheaper model to run at $0.43/1M blended tokens — about 1.4× cheaper.
- Ring-2.6-1T offers the larger context window (400K tokens), useful for long documents and codebases.
Verdict: GLM-4.7 or Ring-2.6-1T?
GLM-4.7 advantages
- Coding ability (+6%)
- Agentic task performance (+26%)
Ring-2.6-1T advantages
- General intelligence (+6%)
- Context window (+36%)
- Affordability (+28%)
- Output speed (+30%)
Which should you choose?
- Choose the GLM-4.7 if coding and software development are your main workload.
- Choose the Ring-2.6-1T if you need the strongest overall reasoning and accuracy.
- Choose the GLM-4.7 if you build agents or multi-step tool-use workflows.
Value for money
Ring-2.6-1T offers more intelligence per dollar (1.5× 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 Ring-2.6-1T: 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).
Ring-2.6-1T — Ant Group text model with an Intelligence Index of 32, a 400K-token context window and a blended price of $0.43/1M tokens (open weights).
GLM-4.7 vs Ring-2.6-1T: Ring-2.6-1T scores higher on the Intelligence Index. Ring-2.6-1T leads overall capability (Intelligence Index 32.0 vs 30.0). Ring-2.6-1T is the cheaper model to run at $0.43/1M blended tokens — about 1.4× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Ring-2.6-1T scores 32.0 versus 30.0. For software development, the Coding Index puts GLM-4.7 ahead (45.3 vs 42.8). 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 Ring-2.6-1T accepts up to 400K tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, Ring-2.6-1T generates faster (124 vs 87 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, Ring-2.6-1T is the cheaper model to run ($0.43 vs $0.60 per 1M tokens). GLM-4.7 is open weights and Ring-2.6-1T 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 Ring-2.6-1T?
Ring-2.6-1T takes the overall edge, though GLM-4.7 wins in specific areas worth weighing. Ring-2.6-1T leads overall capability (Intelligence Index 32.0 vs 30.0).
What is the main difference between the GLM-4.7 and the Ring-2.6-1T?
Ring-2.6-1T leads overall capability (Intelligence Index 32.0 vs 30.0). Ring-2.6-1T is the cheaper model to run at $0.43/1M blended tokens — about 1.4× cheaper.
Which is better value?
Ring-2.6-1T offers more intelligence per dollar (1.5× 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 coding and software development are your main workload. Choose the Ring-2.6-1T 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.