Apertus 70B vs Kimi K2.5 vs Kimi K2 Instruct 0905
Kimi K2.5 comes out ahead, 50 to 38 and 32 on our weighted score, though Kimi K2 Instruct 0905 is 10% cheaper per token.
Swiss AI
Apertus 70B
32/100- ECI—
- Price$0.82 / $2.42
- Context66K
- Our pick
Moonshot AIKimi K2.5
50/100- ECI148.1
- Price$0.60 / $3.00
- Context262K
- Moonshot AI
Kimi K2 Instruct 0905
38/100- ECI—
- Price$0.60 / $2.50
- Context262K
Kimi K2.5 is our pick
Kimi K2.5 is the better all-round choice, scoring 50/100 against Kimi K2 Instruct 0905 (38) and Apertus 70B (32). It leads on inputs & features. Kimi K2 Instruct 0905 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 priceKimi K2 Instruct 0905Kimi K2 Instruct 0905 $1.07 · Kimi K2.5 $1.20 · Apertus 70B $1.22 per 1M tokens (3:1 blend)
- Longest contextKimi K2.5 and Kimi K2 Instruct 0905Kimi K2.5 262,144 · Kimi K2 Instruct 0905 262,144 · Apertus 70B 65,536 tokens
- Widest inputsKimi K2.5Apertus 70B: Text · Kimi K2.5: Text, Images, Video · Kimi K2 Instruct 0905: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Apertus 70B | Kimi K2.5 | Kimi K2 Instruct 0905 |
|---|---|---|---|---|
| Price | 50% | 44 | 44 | 46 |
| Inputs & features | 30% | 25 | 70 | 25 |
| Context window | 20% | 12 | 37 | 37 |
| Overall | 100% | 32/100 | 50/100 | 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 | Kimi K2.5Moonshot AI | Kimi K2 Instruct 0905Moonshot AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 148.1 | — |
| ECI rank | — | #68 of 165 | — |
| GPQA DiamondGraduate-level science questions | — | 87.6% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 92.2% | — |
| SWE-bench VerifiedFixing real GitHub issues | — | 73.8% | — |
| SimpleQA VerifiedShort factual questions | — | 34.3% | — |
| Price per million tokens | |||
| Input | $0.82 | $0.60 (best) | $0.60 (best) |
| Output | $2.42 (best) | $3.00 | $2.50 |
| Cached input | — | — | — |
| Blended (3:1) | $1.22 | $1.20 | $1.07 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 3 providers | Median of 33 providers | Median of 12 providers |
| Limits | |||
| Context window | 65,536 tokens | 262,144 tokens (best) | 262,144 tokens (best) |
| Max output | 8,192 tokens | 262,144 tokens (best) | 98,304 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | No | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenApache-2.0 | Open | OpenModified MIT |
| API model ID | — | — | — |
| API providers | 3 | 33 (best) | 13 |
| Released | Sep 2, 2025 | Jan 27, 2026 | Sep 5, 2025 |
| Knowledge cutoff | Sep 2025 | Jan 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
- Kimi K2.5$12.00
- Kimi K2 Instruct 0905$11.00
Which should you choose?
Which is better: Apertus 70B, Kimi K2.5 or Kimi K2 Instruct 0905?
Kimi K2.5 is the better all-round choice, scoring 50/100 against Kimi K2 Instruct 0905 (38) and Apertus 70B (32). It leads on inputs & features. Kimi K2 Instruct 0905 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, Kimi K2.5 or Kimi K2 Instruct 0905?
Kimi K2 Instruct 0905 is cheaper at $0.60 input / $2.50 output per million tokens (median across 12 API providers). Kimi K2.5 costs $0.60 input / $3.00 output per million tokens (median across 33 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 $1.07 per million tokens for Kimi K2 Instruct 0905 versus $1.20 for Kimi K2.5 (1.1× as much) and $1.22 for Apertus 70B (1.1× 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, Kimi K2.5 has an ECI of 148.1 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 and Kimi K2 Instruct 0905 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 K2.5 and Kimi K2 Instruct 0905 have the largest context windows (262,144 and 262,144 tokens), against 65,536 for Apertus 70B. Maximum output per response: Apertus 70B up to 8,192, Kimi K2.5 up to 262,144, Kimi K2 Instruct 0905 up to 98,304 tokens.
Which can read images, PDFs, audio or video?
Apertus 70B accepts text; Kimi K2.5 accepts text, images and video; Kimi K2 Instruct 0905 accepts text. Kimi K2.5 handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights (Apache-2.0 and Modified MIT), so you can self-host them.
Which is newer?
Kimi K2.5 is the newest, released Jan 27, 2026. Kimi K2 Instruct 0905 came out Sep 5, 2025; Apertus 70B came out Sep 2, 2025. Knowledge cutoff: Apertus 70B Sep 2025, Kimi K2.5 Jan 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.
