Ling-1T vs Relace Apply 3
Ling-1T comes out ahead, 36 to 32 on our weighted score, though Relace Apply 3 is 5% cheaper per token.
- Our pick
inclusionAILing-1T
36/100- ECI—
- Price$0.57 / $2.29
- Context128K
Relace
Relace Apply 3
32/100- ECI—
- Price$0.85 / $1.25
- Context256K
Add a model
Make it a three-way comparison.
Ling-1T is our pick
Ling-1T is the better all-round choice, scoring 36/100 against 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 · Ling-1T $1.00 per 1M tokens (3:1 blend)
- Longest contextRelace Apply 3Relace Apply 3 256,000 · Ling-1T 128,000 tokens
- Widest inputsSame inputsLing-1T: Text · Relace Apply 3: Text
- Self-hostingLing-1TPublishes downloadable weights
| Measure | Weight | Ling-1T | Relace Apply 3 |
|---|---|---|---|
| Price | 50% | 48 | 49 |
| Inputs & features | 30% | 25 | 0 |
| Context window | 20% | 24 | 36 |
| Overall | 100% | 36/100 | 32/100 |
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 | Relace Apply 3Relace |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | $0.57 (best) | $0.85 |
| Output | $2.29 | $1.25 (best) |
| Cached input | — | — |
| Blended (3:1) | $1.00 | $0.95 (best) |
| Long-context rate | Same rate | Same rate |
| Price source | Official Bailing API | Median of 3 providers |
| Limits | ||
| Context window | 128,000 tokens | 256,000 tokens (best) |
| Max output | 32,000 tokens | 128,000 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | No | No |
| Tool calling | Yes | No |
| Structured output | No | No |
| Availability | ||
| Weights | Open | Proprietary |
| API model ID | Ling-1T | — |
| API providers | 1 | 3 (best) |
| Released | Oct 2025 | Sep 26, 2025 |
| Knowledge cutoff | 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.
- Ling-1T$10.28
- Relace Apply 3$11.00
Which should you choose?
Which is better: Ling-1T or Relace Apply 3?
Ling-1T is the better all-round choice, scoring 36/100 against 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, Ling-1T or Relace Apply 3?
Relace Apply 3 is cheaper at $0.85 input / $1.25 output per million tokens (median across 3 API providers). 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 Ling-1T (1.1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Ling-1T has not been scored yet and Relace Apply 3 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Ling-1T and Relace Apply 3 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 128,000 for Ling-1T. Maximum output per response: Ling-1T up to 32,000, Relace Apply 3 up to 128,000 tokens.
Which can read images, PDFs, audio or video?
Ling-1T accepts text; Relace Apply 3 accepts text. They handle the same number of input types.
Are any of these open source?
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. Relace Apply 3 came out Sep 26, 2025. Knowledge cutoff: 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.
