Kimi K2 Instruct 0905 vs Qwen3 Max Preview vs Apertus 70B
Kimi K2 Instruct 0905 comes out ahead, 38 to 33 and 32 on our weighted score, and it is the cheaper option too.
- Our pick
Moonshot AIKimi K2 Instruct 0905
38/100- ECI—
- Price$0.60 / $2.50
- Context262K
Alibaba (Qwen)Qwen3 Max Preview
33/100- ECI—
- Price$0.975 / $4.14
- Context262K
Swiss AI
Apertus 70B
32/100- ECI—
- Price$0.82 / $2.42
- Context66K
Kimi K2 Instruct 0905 is our pick
Kimi K2 Instruct 0905 is the better all-round choice, scoring 38/100 against Qwen3 Max Preview (33) and Apertus 70B (32). It leads 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 · Apertus 70B $1.22 · Qwen3 Max Preview $1.77 per 1M tokens (3:1 blend)
- Longest contextKimi K2 Instruct 0905 and Qwen3 Max PreviewKimi K2 Instruct 0905 262,144 · Qwen3 Max Preview 262,144 · Apertus 70B 65,536 tokens
- Widest inputsSame inputsKimi K2 Instruct 0905: Text · Qwen3 Max Preview: Text · Apertus 70B: Text
- Self-hostingKimi K2 Instruct 0905 and Apertus 70BPublishes downloadable weights (Modified MIT and Apache-2.0)
| Measure | Weight | Kimi K2 Instruct 0905 | Qwen3 Max Preview | Apertus 70B |
|---|---|---|---|---|
| Price | 50% | 46 | 37 | 44 |
| Inputs & features | 30% | 25 | 25 | 25 |
| Context window | 20% | 37 | 37 | 12 |
| Overall | 100% | 38/100 | 33/100 | 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 | Kimi K2 Instruct 0905Moonshot AI | Qwen3 Max PreviewAlibaba (Qwen) | Apertus 70BSwiss AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.60 (best) | $0.975 | $0.82 |
| Output | $2.50 | $4.14 | $2.42 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $1.07 (best) | $1.77 | $1.22 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 12 providers | Median of 2 providers | Median of 3 providers |
| Limits | |||
| Context window | 262,144 tokens (best) | 262,144 tokens (best) | 65,536 tokens |
| Max output | 98,304 tokens (best) | 32,768 tokens | 8,192 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 | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenModified MIT | Proprietary | OpenApache-2.0 |
| API model ID | — | — | — |
| API providers | 13 (best) | 2 | 3 |
| Released | Sep 5, 2025 | Sep 5, 2025 | Sep 2, 2025 |
| Knowledge cutoff | — | Apr 2025 | 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.
- Kimi K2 Instruct 0905$11.00
- Qwen3 Max Preview$18.03
- Apertus 70B$13.04
Which should you choose?
Which is better: Kimi K2 Instruct 0905, Qwen3 Max Preview or Apertus 70B?
Kimi K2 Instruct 0905 is the better all-round choice, scoring 38/100 against Qwen3 Max Preview (33) and Apertus 70B (32). It leads 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, Kimi K2 Instruct 0905, Qwen3 Max Preview or Apertus 70B?
Kimi K2 Instruct 0905 is cheaper at $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); Qwen3 Max Preview costs $0.975 input / $4.14 output per million tokens (median across 2 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 $1.22 for Apertus 70B (1.1× as much) and $1.77 for Qwen3 Max Preview (1.6× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Kimi K2 Instruct 0905 has not been scored yet, Qwen3 Max Preview has not been scored yet and Apertus 70B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Kimi K2 Instruct 0905, Qwen3 Max Preview and Apertus 70B 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 Instruct 0905 and Qwen3 Max Preview have the largest context windows (262,144 and 262,144 tokens), against 65,536 for Apertus 70B. Maximum output per response: Kimi K2 Instruct 0905 up to 98,304, Qwen3 Max Preview up to 32,768, Apertus 70B up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Kimi K2 Instruct 0905 accepts text; Qwen3 Max Preview accepts text; Apertus 70B accepts text. They handle the same number of input types.
Are any of these open source?
Kimi K2 Instruct 0905 and Apertus 70B publishes its weights (Modified MIT and Apache-2.0) and can be self-hosted; Qwen3 Max Preview is proprietary.
Which is newer?
Kimi K2 Instruct 0905 is the newest, released Sep 5, 2025. Qwen3 Max Preview came out Sep 5, 2025; Apertus 70B came out Sep 2, 2025. Knowledge cutoff: Qwen3 Max Preview Apr 2025, 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.
