GLM-4.5 vs Ling-1T vs Qwen3-Next 80B-A3B Instruct
Too close to call on our weighted score (GLM-4.5 40, Qwen3-Next 80B-A3B Instruct 39, Ling-1T 37). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-4.5
40/100- ECI—
- Price$0.60 / $2.20
- Context131K
inclusionAILing-1T
37/100- ECI—
- Price$0.57 / $2.29
- Context128K
Alibaba (Qwen)Qwen3-Next 80B-A3B Instruct
39/100- ECI—
- Price$0.50 / $2.00
- Context131K
Too close to call
It is close. Our weighted score puts them within 2 points (GLM-4.5 40/100, Qwen3-Next 80B-A3B Instruct 39/100, Ling-1T 37/100), so choose by what matters most for your work: Qwen3-Next 80B-A3B Instruct on price. 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 priceQwen3-Next 80B-A3B InstructQwen3-Next 80B-A3B Instruct $0.875 · GLM-4.5 $1.00 · Ling-1T $1.00 per 1M tokens (3:1 blend)
- Longest contextGLM-4.5 and Qwen3-Next 80B-A3B InstructGLM-4.5 131,072 · Qwen3-Next 80B-A3B Instruct 131,072 · Ling-1T 128,000 tokens
- Widest inputsSame inputsGLM-4.5: Text · Ling-1T: Text · Qwen3-Next 80B-A3B Instruct: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-4.5 | Ling-1T | Qwen3-Next 80B-A3B Instruct |
|---|---|---|---|---|
| Price | 50% | 50 | 50 | 53 |
| Inputs & features | 30% | 35 | 25 | 25 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 40/100 | 37/100 | 39/100 |
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-4.5Z.ai (Zhipu) | Ling-1TinclusionAI | Qwen3-Next 80B-A3B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.60 | $0.57 | $0.50 (best) |
| Output | $2.20 | $2.29 | $2.00 (best) |
| Cached input | $0.11 | — | — |
| Blended (3:1) | $1.00 | $1.00 | $0.875 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Bailing API | Official Alibaba API |
| Limits | |||
| Context window | 131,072 tokens (best) | 128,000 tokens | 131,072 tokens (best) |
| Max output | 98,304 tokens (best) | 32,000 tokens | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | glm-4.5 | Ling-1T | qwen3-next-80b-a3b-instruct |
| API providers | 14 (best) | 1 | 13 |
| Released | Jul 28, 2025 | Oct 2025 | Sep 2025 |
| Knowledge cutoff | Apr 2025 | Jun 2024 | 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.
- GLM-4.5$10.40
- Ling-1T$10.28
- Qwen3-Next 80B-A3B Instruct$9.00
Which should you choose?
Which is better: GLM-4.5, Ling-1T or Qwen3-Next 80B-A3B Instruct?
It is close. Our weighted score puts them within 2 points (GLM-4.5 40/100, Qwen3-Next 80B-A3B Instruct 39/100, Ling-1T 37/100), so choose by what matters most for your work: Qwen3-Next 80B-A3B Instruct on price. 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-4.5, Ling-1T or Qwen3-Next 80B-A3B Instruct?
Qwen3-Next 80B-A3B Instruct is cheaper at $0.50 input / $2.00 output per million tokens (official Alibaba API price). GLM-4.5 costs $0.60 input / $2.20 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). At a typical mix of three input tokens to one output token, that is $0.875 per million tokens for Qwen3-Next 80B-A3B Instruct versus $1.00 for GLM-4.5 (1.1× as much) and $1.00 for Ling-1T (1.1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.5 has not been scored yet, Ling-1T has not been scored yet and Qwen3-Next 80B-A3B Instruct has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.5, Ling-1T and Qwen3-Next 80B-A3B Instruct 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-4.5 and Qwen3-Next 80B-A3B Instruct have the largest context windows (131,072 and 131,072 tokens), against 128,000 for Ling-1T. Maximum output per response: GLM-4.5 up to 98,304, Ling-1T up to 32,000, Qwen3-Next 80B-A3B Instruct up to 32,768 tokens.
Which can read images, PDFs, audio or video?
GLM-4.5 accepts text; Ling-1T accepts text; Qwen3-Next 80B-A3B Instruct accepts text. They handle the same number of input types.
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. Qwen3-Next 80B-A3B Instruct came out Sep 2025; GLM-4.5 came out Jul 28, 2025. Knowledge cutoff: GLM-4.5 Apr 2025, Ling-1T Jun 2024, Qwen3-Next 80B-A3B Instruct 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.
