Relace Apply 3 vs Kimi K2 Instruct 0905
Kimi K2 Instruct 0905 comes out ahead, 38 to 32 on our weighted score, though Relace Apply 3 is 12% cheaper per token.
Relace
Relace Apply 3
32/100- ECI—
- Price$0.85 / $1.25
- Context256K
- Our pick
Moonshot AIKimi K2 Instruct 0905
38/100- ECI—
- Price$0.60 / $2.50
- Context262K
Add a model
Make it a three-way comparison.
Kimi K2 Instruct 0905 is our pick
Kimi K2 Instruct 0905 is the better all-round choice, scoring 38/100 against Relace Apply 3 (32). It leads on inputs & features. Relace Apply 3 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 priceRelace Apply 3Relace Apply 3 $0.95 · Kimi K2 Instruct 0905 $1.07 per 1M tokens (3:1 blend)
- Longest contextKimi K2 Instruct 0905Kimi K2 Instruct 0905 262,144 · Relace Apply 3 256,000 tokens
- Widest inputsSame inputsRelace Apply 3: Text · Kimi K2 Instruct 0905: Text
- Self-hostingKimi K2 Instruct 0905Publishes downloadable weights (Modified MIT)
| Measure | Weight | Relace Apply 3 | Kimi K2 Instruct 0905 |
|---|---|---|---|
| Price | 50% | 49 | 46 |
| Inputs & features | 30% | 0 | 25 |
| Context window | 20% | 36 | 37 |
| Overall | 100% | 32/100 | 38/100 |
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 | Kimi K2 Instruct 0905Moonshot AI |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | $0.85 | $0.60 (best) |
| Output | $1.25 (best) | $2.50 |
| Cached input | — | — |
| Blended (3:1) | $0.95 (best) | $1.07 |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 3 providers | Median of 12 providers |
| Limits | ||
| Context window | 256,000 tokens | 262,144 tokens (best) |
| Max output | 128,000 tokens (best) | 98,304 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | No | No |
| Tool calling | No | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | Proprietary | OpenModified MIT |
| API model ID | — | — |
| API providers | 3 | 13 (best) |
| Released | Sep 26, 2025 | Sep 5, 2025 |
| Knowledge cutoff | — | — |
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
- Kimi K2 Instruct 0905$11.00
Which should you choose?
Which is better: Relace Apply 3 or Kimi K2 Instruct 0905?
Kimi K2 Instruct 0905 is the better all-round choice, scoring 38/100 against Relace Apply 3 (32). It leads on inputs & features. Relace Apply 3 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, Relace Apply 3 or Kimi K2 Instruct 0905?
Relace Apply 3 is cheaper at $0.85 input / $1.25 output per million tokens (median across 3 API providers). 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 $0.95 per million tokens for Relace Apply 3 versus $1.07 for Kimi K2 Instruct 0905 (1.1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Relace Apply 3 has not been scored yet and Kimi K2 Instruct 0905 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Relace Apply 3 and Kimi K2 Instruct 0905 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?
Kimi K2 Instruct 0905 has the largest context window at 262,144 tokens, against 256,000 for Relace Apply 3. Maximum output per response: Relace Apply 3 up to 128,000, Kimi K2 Instruct 0905 up to 98,304 tokens.
Which can read images, PDFs, audio or video?
Relace Apply 3 accepts text; Kimi K2 Instruct 0905 accepts text. They handle the same number of input types.
Are any of these open source?
Kimi K2 Instruct 0905 publishes its weights (Modified MIT) and can be self-hosted; Relace Apply 3 is proprietary.
Which is newer?
Relace Apply 3 is the newest, released Sep 26, 2025. Kimi K2 Instruct 0905 came out Sep 5, 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.
