Ling-1T vs Kimi K2 Instruct 0905 vs GLM-4.6
Too close to call on our weighted score (GLM-4.6 41, Kimi K2 Instruct 0905 38, Ling-1T 36). The right pick depends on what you value most.
inclusionAILing-1T
36/100- ECI—
- Price$0.57 / $2.29
- Context128K
Moonshot AIKimi K2 Instruct 0905
38/100- ECI—
- Price$0.60 / $2.50
- Context262K
Z.ai (Zhipu)GLM-4.6
41/100- ECI—
- Price$0.60 / $2.20
- Context205K
Too close to call
It is close. Our weighted score puts them within 3 points (GLM-4.6 41/100, Kimi K2 Instruct 0905 38/100, Ling-1T 36/100), so choose by what matters most for your work: Ling-1T on price and Kimi K2 Instruct 0905 for long inputs. 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 priceLing-1T and GLM-4.6Ling-1T $1.00 · GLM-4.6 $1.00 · Kimi K2 Instruct 0905 $1.07 per 1M tokens (3:1 blend)
- Longest contextKimi K2 Instruct 0905Kimi K2 Instruct 0905 262,144 · GLM-4.6 204,800 · Ling-1T 128,000 tokens
- Widest inputsSame inputsLing-1T: Text · Kimi K2 Instruct 0905: Text · GLM-4.6: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Ling-1T | Kimi K2 Instruct 0905 | GLM-4.6 |
|---|---|---|---|---|
| Price | 50% | 48 | 46 | 48 |
| Inputs & features | 30% | 25 | 25 | 35 |
| Context window | 20% | 24 | 37 | 32 |
| Overall | 100% | 36/100 | 38/100 | 41/100 |
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 | Kimi K2 Instruct 0905Moonshot AI | 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 | $2.50 | $2.20 (best) |
| Cached input | — | — | $0.11 |
| Blended (3:1) | $1.00 (best) | $1.07 | $1.00 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Bailing API | Median of 12 providers | Official Z.AI API |
| Limits | |||
| Context window | 128,000 tokens | 262,144 tokens (best) | 204,800 tokens |
| Max output | 32,000 tokens | 98,304 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 | OpenModified MIT | Open |
| API model ID | Ling-1T | — | glm-4.6 |
| API providers | 1 | 13 | 29 (best) |
| Released | Oct 2025 | Sep 5, 2025 | Sep 30, 2025 |
| Knowledge cutoff | 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.
- Ling-1T$10.28
- Kimi K2 Instruct 0905$11.00
- GLM-4.6$10.40
Which should you choose?
Which is better: Ling-1T, Kimi K2 Instruct 0905 or GLM-4.6?
It is close. Our weighted score puts them within 3 points (GLM-4.6 41/100, Kimi K2 Instruct 0905 38/100, Ling-1T 36/100), so choose by what matters most for your work: Ling-1T on price and Kimi K2 Instruct 0905 for long inputs. 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, Kimi K2 Instruct 0905 or GLM-4.6?
Ling-1T is cheaper at $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); Kimi K2 Instruct 0905 costs $0.60 input / $2.50 output per million tokens (median across 12 API providers). At a typical mix of three input tokens to one output token, that is $1.00 per million tokens for Ling-1T versus $1.00 for GLM-4.6 (1× as much) and $1.07 for Kimi K2 Instruct 0905 (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, Kimi K2 Instruct 0905 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, Kimi K2 Instruct 0905 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?
Kimi K2 Instruct 0905 has the largest context window at 262,144 tokens, against 204,800 for GLM-4.6 and 128,000 for Ling-1T. Maximum output per response: Ling-1T up to 32,000, Kimi K2 Instruct 0905 up to 98,304, GLM-4.6 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Ling-1T accepts text; Kimi K2 Instruct 0905 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 (Modified MIT), 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; Kimi K2 Instruct 0905 came out Sep 5, 2025. Knowledge cutoff: Ling-1T Jun 2024, 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 Nov 2, 2026.