Relace Apply 3 vs GLM-4.6 vs Ling-1T
GLM-4.6 comes out ahead, 41 to 36 and 32 on our weighted score, though Relace Apply 3 is 5% cheaper per token.
Relace
Relace Apply 3
32/100- ECI—
- Price$0.85 / $1.25
- Context256K
- Our pick
Z.ai (Zhipu)GLM-4.6
41/100- ECI—
- Price$0.60 / $2.20
- Context205K
inclusionAILing-1T
36/100- ECI—
- Price$0.57 / $2.29
- Context128K
GLM-4.6 is our pick
GLM-4.6 is the better all-round choice, scoring 41/100 against Ling-1T (36) and Relace Apply 3 (32). It leads on inputs & features. Relace Apply 3 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 priceRelace Apply 3Relace Apply 3 $0.95 · GLM-4.6 $1.00 · Ling-1T $1.00 per 1M tokens (3:1 blend)
- Longest contextRelace Apply 3Relace Apply 3 256,000 · GLM-4.6 204,800 · Ling-1T 128,000 tokens
- Widest inputsSame inputsRelace Apply 3: Text · GLM-4.6: Text · Ling-1T: Text
- Self-hostingGLM-4.6 and Ling-1TPublishes downloadable weights
| Measure | Weight | Relace Apply 3 | GLM-4.6 | Ling-1T |
|---|---|---|---|---|
| Price | 50% | 49 | 48 | 48 |
| Inputs & features | 30% | 0 | 35 | 25 |
| Context window | 20% | 36 | 32 | 24 |
| Overall | 100% | 32/100 | 41/100 | 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 | Relace Apply 3Relace | GLM-4.6Z.ai (Zhipu) | Ling-1TinclusionAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.85 | $0.60 | $0.57 (best) |
| Output | $1.25 (best) | $2.20 | $2.29 |
| Cached input | — | $0.11 | — |
| Blended (3:1) | $0.95 (best) | $1.00 | $1.00 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 3 providers | Official Z.AI API | Official Bailing API |
| Limits | |||
| Context window | 256,000 tokens (best) | 204,800 tokens | 128,000 tokens |
| Max output | 128,000 tokens | 131,072 tokens (best) | 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 | No | Yes | No |
| Tool calling | No | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | — | glm-4.6 | Ling-1T |
| API providers | 3 | 29 (best) | 1 |
| Released | Sep 26, 2025 | Sep 30, 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.
- Relace Apply 3$11.00
- GLM-4.6$10.40
- Ling-1T$10.28
Which should you choose?
Which is better: Relace Apply 3, GLM-4.6 or Ling-1T?
GLM-4.6 is the better all-round choice, scoring 41/100 against Ling-1T (36) and Relace Apply 3 (32). It leads on inputs & features. Relace Apply 3 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, Relace Apply 3, GLM-4.6 or Ling-1T?
Relace Apply 3 is cheaper at $0.85 input / $1.25 output per million tokens (median across 3 API providers). GLM-4.6 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 $0.95 per million tokens for Relace Apply 3 versus $1.00 for GLM-4.6 (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. Relace Apply 3 has not been scored yet, GLM-4.6 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 Relace Apply 3, GLM-4.6 and Ling-1T yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Relace Apply 3 does not support tool calling, which most coding agents need.
Which has the bigger context window?
Relace Apply 3 has the largest context window at 256,000 tokens, against 204,800 for GLM-4.6 and 128,000 for Ling-1T. Maximum output per response: Relace Apply 3 up to 128,000, GLM-4.6 up to 131,072, Ling-1T up to 32,000 tokens.
Which can read images, PDFs, audio or video?
Relace Apply 3 accepts text; GLM-4.6 accepts text; Ling-1T accepts text. They handle the same number of input types.
Are any of these open source?
GLM-4.6 and Ling-1T publishes its weights and can be self-hosted; Relace Apply 3 is proprietary.
Which is newer?
Ling-1T is the newest, released Oct 2025. GLM-4.6 came out Sep 30, 2025; Relace Apply 3 came out Sep 26, 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.
