Ling-1T vs GLM-4.5V vs GLM-4.6
GLM-4.5V comes out ahead, 49 to 42 and 37 on our weighted score, and it is the cheaper option too.
GLM-4.5V is our pick
GLM-4.5V is the better all-round choice, scoring 49/100 against GLM-4.6 (42) and Ling-1T (37). It leads on price and inputs & features. GLM-4.6 wins on 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 priceGLM-4.5VGLM-4.5V $0.90 · Ling-1T $1.00 · GLM-4.6 $1.00 per 1M tokens (3:1 blend)
- Longest contextGLM-4.6GLM-4.6 204,800 · Ling-1T 128,000 · GLM-4.5V 64,000 tokens
- Widest inputsGLM-4.5VLing-1T: Text · GLM-4.5V: Text, Images, Video · GLM-4.6: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Ling-1T | GLM-4.5V | GLM-4.6 |
|---|---|---|---|---|
| Price | 50% | 50 | 52 | 50 |
| Inputs & features | 30% | 25 | 70 | 35 |
| Context window | 20% | 24 | 12 | 32 |
| Overall | 100% | 37/100 | 49/100 | 42/100 |
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 |  Ling-1TinclusionAI |  GLM-4.5VZ.ai (Zhipu) | GLM-4.6Z.ai (Zhipu) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.57 (best) | $0.60 | $0.60 |
| Output | $2.29 | $1.80 (best) | $2.20 |
| Cached input | — | — | $0.11 |
| Blended (3:1) | $1.00 | $0.90 (best) | $1.00 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Bailing API | Official Z.AI API | Official Z.AI API |
| Limits | |||
| Context window | 128,000 tokens | 64,000 tokens | 204,800 tokens (best) |
| Max output | 32,000 tokens | 16,384 tokens | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | No | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | Ling-1T | glm-4.5v | glm-4.6 |
| API providers | 1 | 11 | 18 (best) |
| Released | Oct 2025 | Aug 11, 2025 | Sep 30, 2025 |
| Knowledge cutoff | Jun 2024 | Apr 2025 | Apr 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Ling-1T$10.28
- GLM-4.5V$9.60
- GLM-4.6$10.40
Which should you choose?
Which is better: Ling-1T, GLM-4.5V or GLM-4.6?
GLM-4.5V is the better all-round choice, scoring 49/100 against GLM-4.6 (42) and Ling-1T (37). It leads on price and inputs & features. GLM-4.6 wins on 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, Ling-1T, GLM-4.5V or GLM-4.6?
GLM-4.5V is cheaper at $0.60 input / $1.80 output per million tokens (official Z.AI API price). Ling-1T costs $0.57 input / $2.29 output per million tokens (official Bailing API price); GLM-4.6 costs $0.60 input / $2.20 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $0.90 per million tokens for GLM-4.5V versus $1.00 for Ling-1T (1.1× as much) and $1.00 for GLM-4.6 (1.1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Ling-1T has not been scored yet, GLM-4.5V has not been scored yet and GLM-4.6 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Ling-1T, GLM-4.5V and GLM-4.6 yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three 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 and 64,000 for GLM-4.5V. Maximum output per response: Ling-1T up to 32,000, GLM-4.5V up to 16,384, GLM-4.6 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Ling-1T accepts text; GLM-4.5V accepts text, images and video; GLM-4.6 accepts text. GLM-4.5V handles the widest range of inputs.
Are any of these open source?
Yes, all three 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; GLM-4.5V came out Aug 11, 2025. Knowledge cutoff: Ling-1T Jun 2024, GLM-4.5V Apr 2025, GLM-4.6 Apr 2025.
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.
