GLM-4.6 vs Ling-1T
GLM-4.6 comes out ahead, 42 to 37 on our weighted score.
GLM-4.6 is our pick
GLM-4.6 is the better all-round choice, scoring 42/100 against Ling-1T (37). It leads on inputs & features and context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceSame priceGLM-4.6 $1.00 · Ling-1T $1.00 per 1M tokens (3:1 blend)
- Longest contextGLM-4.6GLM-4.6 204,800 · Ling-1T 128,000 tokens
- Widest inputsSame inputsGLM-4.6: Text · Ling-1T: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-4.6 | Ling-1T |
|---|---|---|---|
| Price | 50% | 50 | 50 |
| Inputs & features | 30% | 35 | 25 |
| Context window | 20% | 32 | 24 |
| Overall | 100% | 42/100 | 37/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification |  GLM-4.6Z.ai (Zhipu) |  Ling-1TinclusionAI |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | $0.60 | $0.57 (best) |
| Output | $2.20 (best) | $2.29 |
| Cached input | $0.11 | — |
| Blended (3:1) | $1.00 | $1.00 |
| Long-context rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Bailing API |
| Limits | ||
| Context window | 204,800 tokens (best) | 128,000 tokens |
| Max output | 131,072 tokens (best) | 32,000 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yes | No |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | Open | Open |
| API model ID | glm-4.6 | Ling-1T |
| API providers | 18 (best) | 1 |
| Released | Sep 30, 2025 | Oct 2025 |
| Knowledge cutoff | Apr 2025 | Jun 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GLM-4.6$10.40
- Ling-1T$10.28
Which should you choose?
Which is better: GLM-4.6 or Ling-1T?
GLM-4.6 is the better all-round choice, scoring 42/100 against Ling-1T (37). It leads on inputs & features and context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, GLM-4.6 or Ling-1T?
GLM-4.6 and Ling-1T cost the same: $0.60 input / $2.20 output per million tokens.
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. GLM-4.6 has not been scored yet and Ling-1T has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.6 and Ling-1T yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Both support tool calling for agent workflows.
Which has the bigger context window?
GLM-4.6 has the largest context window at 204,800 tokens, against 128,000 for Ling-1T. Maximum output per response: GLM-4.6 up to 131,072, Ling-1T up to 32,000 tokens.
Which can read images, PDFs, audio or video?
GLM-4.6 accepts text; Ling-1T accepts text. They handle the same number of input types.
Are any of these open source?
Yes, both publish their weights, so you can self-host them.
Which is newer?
Ling-1T is the newest, released Oct 2025. GLM-4.6 came out Sep 30, 2025. Knowledge cutoff: GLM-4.6 Apr 2025, Ling-1T Jun 2024.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
