Ling-1T vs Qwen3-Next 80B-A3B Instruct vs GLM-4.6
GLM-4.6 comes out ahead, 42 to 39 and 37 on our weighted score, though Qwen3-Next 80B-A3B Instruct is 13% cheaper per token.
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
- Our pick
Z.ai (Zhipu)GLM-4.6
42/100- ECI—
- Price$0.60 / $2.20
- Context205K
GLM-4.6 is our pick
GLM-4.6 is the better all-round choice, scoring 42/100 against Qwen3-Next 80B-A3B Instruct (39) and Ling-1T (37). It leads on inputs & features and context window. Qwen3-Next 80B-A3B Instruct wins 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 · Ling-1T $1.00 · GLM-4.6 $1.00 per 1M tokens (3:1 blend)
- Longest contextGLM-4.6GLM-4.6 204,800 · Qwen3-Next 80B-A3B Instruct 131,072 · Ling-1T 128,000 tokens
- Widest inputsSame inputsLing-1T: Text · Qwen3-Next 80B-A3B Instruct: Text · GLM-4.6: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Ling-1T | Qwen3-Next 80B-A3B Instruct | GLM-4.6 |
|---|---|---|---|---|
| Price | 50% | 50 | 53 | 50 |
| Inputs & features | 30% | 25 | 25 | 35 |
| Context window | 20% | 24 | 24 | 32 |
| Overall | 100% | 37/100 | 39/100 | 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 | Qwen3-Next 80B-A3B InstructAlibaba (Qwen) | GLM-4.6Z.ai (Zhipu) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.57 | $0.50 (best) | $0.60 |
| Output | $2.29 | $2.00 (best) | $2.20 |
| Cached input | — | — | $0.11 |
| Blended (3:1) | $1.00 | $0.875 (best) | $1.00 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Bailing API | Official Alibaba API | Official Z.AI API |
| Limits | |||
| Context window | 128,000 tokens | 131,072 tokens | 204,800 tokens (best) |
| Max output | 32,000 tokens | 32,768 tokens | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | Ling-1T | qwen3-next-80b-a3b-instruct | glm-4.6 |
| API providers | 1 | 13 | 18 (best) |
| Released | Oct 2025 | Sep 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
- Qwen3-Next 80B-A3B Instruct$9.00
- GLM-4.6$10.40
Which should you choose?
Which is better: Ling-1T, Qwen3-Next 80B-A3B Instruct or GLM-4.6?
GLM-4.6 is the better all-round choice, scoring 42/100 against Qwen3-Next 80B-A3B Instruct (39) and Ling-1T (37). It leads on inputs & features and context window. Qwen3-Next 80B-A3B Instruct wins 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, Ling-1T, Qwen3-Next 80B-A3B Instruct or GLM-4.6?
Qwen3-Next 80B-A3B Instruct is cheaper at $0.50 input / $2.00 output per million tokens (official Alibaba 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 $0.875 per million tokens for Qwen3-Next 80B-A3B Instruct 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, Qwen3-Next 80B-A3B Instruct 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, Qwen3-Next 80B-A3B Instruct 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 131,072 for Qwen3-Next 80B-A3B Instruct and 128,000 for Ling-1T. Maximum output per response: Ling-1T up to 32,000, Qwen3-Next 80B-A3B Instruct up to 32,768, GLM-4.6 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Ling-1T accepts text; Qwen3-Next 80B-A3B Instruct accepts text; GLM-4.6 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. GLM-4.6 came out Sep 30, 2025; Qwen3-Next 80B-A3B Instruct came out Sep 2025. Knowledge cutoff: Ling-1T Jun 2024, Qwen3-Next 80B-A3B Instruct 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.
