Apertus 70B vs Relace Apply 3 vs Kimi K2 Instruct 0905
Kimi K2 Instruct 0905 comes out ahead, 38 to 32 and 32 on our weighted score, though Relace Apply 3 is 12% cheaper per token.
Swiss AI
Apertus 70B
32/100- ECI—
- Price$0.82 / $2.42
- Context66K
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
Kimi K2 Instruct 0905 is our pick
Kimi K2 Instruct 0905 is the better all-round choice, scoring 38/100 against Apertus 70B (32) and Relace Apply 3 (32). 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 · Apertus 70B $1.22 per 1M tokens (3:1 blend)
- Longest contextKimi K2 Instruct 0905Kimi K2 Instruct 0905 262,144 · Relace Apply 3 256,000 · Apertus 70B 65,536 tokens
- Widest inputsSame inputsApertus 70B: Text · Relace Apply 3: Text · Kimi K2 Instruct 0905: Text
- Self-hostingApertus 70B and Kimi K2 Instruct 0905Publishes downloadable weights (Apache-2.0 and Modified MIT)
| Measure | Weight | Apertus 70B | Relace Apply 3 | Kimi K2 Instruct 0905 |
|---|---|---|---|---|
| Price | 50% | 44 | 49 | 46 |
| Inputs & features | 30% | 25 | 0 | 25 |
| Context window | 20% | 12 | 36 | 37 |
| Overall | 100% | 32/100 | 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 | Apertus 70BSwiss AI | Relace Apply 3Relace | Kimi K2 Instruct 0905Moonshot AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.82 | $0.85 | $0.60 (best) |
| Output | $2.42 | $1.25 (best) | $2.50 |
| Cached input | — | — | — |
| Blended (3:1) | $1.22 | $0.95 (best) | $1.07 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 3 providers | Median of 3 providers | Median of 12 providers |
| Limits | |||
| Context window | 65,536 tokens | 256,000 tokens | 262,144 tokens (best) |
| Max output | 8,192 tokens | 128,000 tokens (best) | 98,304 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 | No | No |
| Tool calling | Yes | No | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenApache-2.0 | Proprietary | OpenModified MIT |
| API model ID | — | — | — |
| API providers | 3 | 3 | 13 (best) |
| Released | Sep 2, 2025 | Sep 26, 2025 | Sep 5, 2025 |
| Knowledge cutoff | Sep 2025 | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Apertus 70B$13.04
- Relace Apply 3$11.00
- Kimi K2 Instruct 0905$11.00
Which should you choose?
Which is better: Apertus 70B, Relace Apply 3 or Kimi K2 Instruct 0905?
Kimi K2 Instruct 0905 is the better all-round choice, scoring 38/100 against Apertus 70B (32) and Relace Apply 3 (32). 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, Apertus 70B, 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); Apertus 70B costs $0.82 input / $2.42 output per million tokens (median across 3 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) and $1.22 for Apertus 70B (1.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Apertus 70B has not been scored 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 Apertus 70B, 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 and 65,536 for Apertus 70B. Maximum output per response: Apertus 70B up to 8,192, Relace Apply 3 up to 128,000, Kimi K2 Instruct 0905 up to 98,304 tokens.
Which can read images, PDFs, audio or video?
Apertus 70B accepts text; 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?
Apertus 70B and Kimi K2 Instruct 0905 publishes its weights (Apache-2.0 and 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; Apertus 70B came out Sep 2, 2025. Knowledge cutoff: Apertus 70B Sep 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.
