Kimi K2 Instruct 0905 vs o4-mini-deep-research vs Apertus 70B
Too close to call on our weighted score (Kimi K2 Instruct 0905 38, o4-mini-deep-research 37, Apertus 70B 32). The right pick depends on what you value most.
Moonshot AIKimi K2 Instruct 0905
38/100- ECI—
- Price$0.60 / $2.50
- Context262K
OpenAIo4-mini-deep-research
37/100- ECI—
- Price$1.80 / $7.20
- Context200K
Swiss AI
Apertus 70B
32/100- ECI—
- Price$0.82 / $2.42
- Context66K
Too close to call
It is close. Our weighted score puts them within 1 points (Kimi K2 Instruct 0905 38/100, o4-mini-deep-research 37/100, Apertus 70B 32/100), so choose by what matters most for your work: Kimi K2 Instruct 0905 on price and Kimi K2 Instruct 0905 for long inputs. 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 · o4-mini-deep-research $3.15 per 1M tokens (3:1 blend)
- Longest contextKimi K2 Instruct 0905Kimi K2 Instruct 0905 262,144 · o4-mini-deep-research 200,000 · Apertus 70B 65,536 tokens
- Widest inputso4-mini-deep-researchKimi K2 Instruct 0905: Text · o4-mini-deep-research: Text, Images · 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 | o4-mini-deep-research | Apertus 70B |
|---|---|---|---|---|
| Price | 50% | 46 | 25 | 44 |
| Inputs & features | 30% | 25 | 60 | 25 |
| Context window | 20% | 37 | 32 | 12 |
| Overall | 100% | 38/100 | 37/100 | 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 | o4-mini-deep-researchOpenAI | Apertus 70BSwiss AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.60 (best) | $1.80 | $0.82 |
| Output | $2.50 | $7.20 | $2.42 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $1.07 (best) | $3.15 | $1.22 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 12 providers | Median of 1 providers | Median of 3 providers |
| Limits | |||
| Context window | 262,144 tokens (best) | 200,000 tokens | 65,536 tokens |
| Max output | 98,304 tokens | 100,000 tokens (best) | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | Yes | 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) | 1 | 3 |
| Released | Sep 5, 2025 | Jun 26, 2025 | Sep 2, 2025 |
| Knowledge cutoff | — | May 2024 | 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
- o4-mini-deep-research$32.40
- Apertus 70B$13.04
Which should you choose?
Which is better: Kimi K2 Instruct 0905, o4-mini-deep-research or Apertus 70B?
It is close. Our weighted score puts them within 1 points (Kimi K2 Instruct 0905 38/100, o4-mini-deep-research 37/100, Apertus 70B 32/100), so choose by what matters most for your work: Kimi K2 Instruct 0905 on price and Kimi K2 Instruct 0905 for long inputs. 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, o4-mini-deep-research 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); o4-mini-deep-research costs $1.80 input / $7.20 output per million tokens (median across 1 API provider). 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 $3.15 for o4-mini-deep-research (2.9× 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, o4-mini-deep-research 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, o4-mini-deep-research 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 has the largest context window at 262,144 tokens, against 200,000 for o4-mini-deep-research and 65,536 for Apertus 70B. Maximum output per response: Kimi K2 Instruct 0905 up to 98,304, o4-mini-deep-research up to 100,000, Apertus 70B up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Kimi K2 Instruct 0905 accepts text; o4-mini-deep-research accepts text and images; Apertus 70B accepts text. o4-mini-deep-research handles the widest range of inputs.
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; o4-mini-deep-research is proprietary.
Which is newer?
Kimi K2 Instruct 0905 is the newest, released Sep 5, 2025. Apertus 70B came out Sep 2, 2025; o4-mini-deep-research came out Jun 26, 2025. Knowledge cutoff: o4-mini-deep-research May 2024, 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.
