GLM-4.6 vs Kimi K2 Instruct 0905 vs Ling-1T
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.
Z.ai (Zhipu)GLM-4.6
41/100- ECI—
- Price$0.60 / $2.20
- Context205K
Moonshot AIKimi K2 Instruct 0905
38/100- ECI—
- Price$0.60 / $2.50
- Context262K
inclusionAILing-1T
36/100- ECI—
- Price$0.57 / $2.29
- Context128K
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: GLM-4.6 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 priceGLM-4.6 and Ling-1TGLM-4.6 $1.00 · Ling-1T $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 inputsGLM-4.6: Text · Kimi K2 Instruct 0905: Text · Ling-1T: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-4.6 | Kimi K2 Instruct 0905 | Ling-1T |
|---|---|---|---|---|
| Price | 50% | 48 | 46 | 48 |
| Inputs & features | 30% | 35 | 25 | 25 |
| Context window | 20% | 32 | 37 | 24 |
| Overall | 100% | 41/100 | 38/100 | 36/100 |
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) | Kimi K2 Instruct 0905Moonshot AI | Ling-1TinclusionAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.60 | $0.60 | $0.57 (best) |
| Output | $2.20 (best) | $2.50 | $2.29 |
| 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 Z.AI API | Median of 12 providers | Official Bailing API |
| Limits | |||
| Context window | 204,800 tokens | 262,144 tokens (best) | 128,000 tokens |
| Max output | 131,072 tokens (best) | 98,304 tokens | 32,000 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 | OpenModified MIT | Open |
| API model ID | glm-4.6 | — | Ling-1T |
| API providers | 29 (best) | 13 | 1 |
| Released | Sep 30, 2025 | Sep 5, 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
- Kimi K2 Instruct 0905$11.00
- Ling-1T$10.28
Which should you choose?
Which is better: GLM-4.6, Kimi K2 Instruct 0905 or Ling-1T?
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: GLM-4.6 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, GLM-4.6, Kimi K2 Instruct 0905 or Ling-1T?
GLM-4.6 is cheaper at $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); 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 GLM-4.6 versus $1.00 for Ling-1T (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. GLM-4.6 has not been scored yet, Kimi K2 Instruct 0905 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, Kimi K2 Instruct 0905 and Ling-1T 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: GLM-4.6 up to 131,072, Kimi K2 Instruct 0905 up to 98,304, Ling-1T up to 32,000 tokens.
Which can read images, PDFs, audio or video?
GLM-4.6 accepts text; Kimi K2 Instruct 0905 accepts text; Ling-1T 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: 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 Nov 2, 2026.