Qwen3.6 Plus vs Kimi K2 Instruct 0905 vs Kimi K2.5
Qwen3.6 Plus comes out ahead, 59 to 50 and 38 on our weighted score.
- Our pick
Alibaba (Qwen)Qwen3.6 Plus
59/100- ECI147.8
- Price$0.50 / $3.00
- Context1M
Moonshot AIKimi K2 Instruct 0905
38/100- ECI—
- Price$0.60 / $2.50
- Context262K
- Moonshot AI
Kimi K2.5
50/100- ECI148.1
- Price$0.60 / $3.00
- Context262K
Qwen3.6 Plus is our pick
Qwen3.6 Plus is the better all-round choice, scoring 59/100 against Kimi K2.5 (50) and Kimi K2 Instruct 0905 (38). It leads on inputs & features and context window. 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 · Qwen3.6 Plus $1.13 · Kimi K2.5 $1.20 per 1M tokens (3:1 blend)
- Longest contextQwen3.6 PlusQwen3.6 Plus 1,000,000 · Kimi K2 Instruct 0905 262,144 · Kimi K2.5 262,144 tokens
- Widest inputsQwen3.6 Plus and Kimi K2.5Qwen3.6 Plus: Text, Images, Video · Kimi K2 Instruct 0905: Text · Kimi K2.5: Text, Images, Video
- Self-hostingKimi K2 Instruct 0905 and Kimi K2.5Publishes downloadable weights (Modified MIT)
| Measure | Weight | Qwen3.6 Plus | Kimi K2 Instruct 0905 | Kimi K2.5 |
|---|---|---|---|---|
| Price | 50% | 45 | 46 | 44 |
| Inputs & features | 30% | 80 | 25 | 70 |
| Context window | 20% | 60 | 37 | 37 |
| Overall | 100% | 59/100 | 38/100 | 50/100 |
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 | Qwen3.6 PlusAlibaba (Qwen) | Kimi K2 Instruct 0905Moonshot AI | Kimi K2.5Moonshot AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 147.8 | — | 148.1 (best) |
| ECI rank | #69 of 165 | — | #68 of 165 (best) |
| GPQA DiamondGraduate-level science questions | 88.4% (best) | — | 87.6% |
| FrontierMath Tiers 1–3Research-level mathematics | 38.3% | — | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 93.3% (best) | — | 92.2% |
| SWE-bench VerifiedFixing real GitHub issues | 57.9% | — | 73.8% (best) |
| SimpleQA VerifiedShort factual questions | 44.1% (best) | — | 34.3% |
| Price per million tokens | |||
| Input | $0.50 (best) | $0.60 | $0.60 |
| Output | $3.00 | $2.50 (best) | $3.00 |
| Cached input | $0.05 | — | — |
| Blended (3:1) | $1.13 | $1.07 (best) | $1.20 |
| Long-context rate | Over 256K: $2.00 / $6.00 | Same rate | Same rate |
| Price source | Official Alibaba API | Median of 12 providers | Median of 33 providers |
| Limits | |||
| Context window | 1,000,000 tokens (best) | 262,144 tokens | 262,144 tokens |
| Max output | 65,536 tokens | 98,304 tokens | 262,144 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | Yes | No | Yes |
| Reasoning | Yes | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Proprietary | OpenModified MIT | Open |
| API model ID | qwen3.6-plus | — | — |
| API providers | 22 | 13 | 33 (best) |
| Released | Apr 2, 2026 | Sep 5, 2025 | Jan 27, 2026 |
| Knowledge cutoff | Apr 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.
- Qwen3.6 Plus$11.00
- Kimi K2 Instruct 0905$11.00
- Kimi K2.5$12.00
Which should you choose?
Which is better: Qwen3.6 Plus, Kimi K2 Instruct 0905 or Kimi K2.5?
Qwen3.6 Plus is the better all-round choice, scoring 59/100 against Kimi K2.5 (50) and Kimi K2 Instruct 0905 (38). It leads on inputs & features and context window. 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, Qwen3.6 Plus, Kimi K2 Instruct 0905 or Kimi K2.5?
Kimi K2 Instruct 0905 is cheaper at $0.60 input / $2.50 output per million tokens (median across 12 API providers). Qwen3.6 Plus costs $0.50 input / $3.00 output per million tokens (official Alibaba API price); Kimi K2.5 costs $0.60 input / $3.00 output per million tokens (median across 33 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 $1.13 for Qwen3.6 Plus (1× as much) and $1.20 for Kimi K2.5 (1.1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen3.6 Plus has an ECI of 147.8, Kimi K2 Instruct 0905 has not been scored yet and Kimi K2.5 has an ECI of 148.1.
Which is better for coding?
There are no published SWE-bench Verified results for 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?
Qwen3.6 Plus has the largest context window at 1,000,000 tokens, against 262,144 for Kimi K2 Instruct 0905 and 262,144 for Kimi K2.5. Maximum output per response: Qwen3.6 Plus up to 65,536, Kimi K2 Instruct 0905 up to 98,304, Kimi K2.5 up to 262,144 tokens.
Which can read images, PDFs, audio or video?
Qwen3.6 Plus accepts text, images and video; Kimi K2 Instruct 0905 accepts text; Kimi K2.5 accepts text, images and video. Qwen3.6 Plus handles the widest range of inputs.
Are any of these open source?
Kimi K2 Instruct 0905 and Kimi K2.5 publishes its weights (Modified MIT) and can be self-hosted; Qwen3.6 Plus is proprietary.
Which is newer?
Qwen3.6 Plus is the newest, released Apr 2, 2026. Kimi K2.5 came out Jan 27, 2026; Kimi K2 Instruct 0905 came out Sep 5, 2025. Knowledge cutoff: Qwen3.6 Plus Apr 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.
