Kimi K2 Thinking vs Qwen3.5 27B vs Qwen3.5 Plus
Too close to call on our weighted score (Qwen3.5 27B 61, Qwen3.5 Plus 59, Kimi K2 Thinking 42). The right pick depends on what you value most.
Moonshot AIKimi K2 Thinking
42/100- ECI146.0
- Price$0.60 / $2.50
- Context262K
Alibaba (Qwen)Qwen3.5 27B
61/100- ECI—
- Price$0.30 / $2.40
- Context262K
- Alibaba (Qwen)
Qwen3.5 Plus
59/100- ECI146.8
- Price$0.40 / $2.40
- Context1M
Too close to call
It is close. Our weighted score puts them within 2 points (Qwen3.5 27B 61/100, Qwen3.5 Plus 59/100, Kimi K2 Thinking 42/100), so choose by what matters most for your work: Qwen3.5 27B on price and Qwen3.5 Plus 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 priceQwen3.5 27BQwen3.5 27B $0.825 · Qwen3.5 Plus $0.90 · Kimi K2 Thinking $1.07 per 1M tokens (3:1 blend)
- Longest contextQwen3.5 PlusQwen3.5 Plus 1,000,000 · Kimi K2 Thinking 262,144 · Qwen3.5 27B 262,144 tokens
- Widest inputsQwen3.5 27BKimi K2 Thinking: Text · Qwen3.5 27B: Text, Images, Audio, Video · Qwen3.5 Plus: Text, Images, Video
- Self-hostingKimi K2 Thinking and Qwen3.5 27BPublishes downloadable weights
| Measure | Weight | Kimi K2 Thinking | Qwen3.5 27B | Qwen3.5 Plus |
|---|---|---|---|---|
| Price | 50% | 48 | 54 | 52 |
| Inputs & features | 30% | 35 | 90 | 70 |
| Context window | 20% | 37 | 37 | 60 |
| Overall | 100% | 42/100 | 61/100 | 59/100 |
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 ThinkingMoonshot AI | Qwen3.5 27BAlibaba (Qwen) | Qwen3.5 PlusAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 146.0 | — | 146.8 (best) |
| ECI rank | #72 of 148 | — | #65 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 84.2% | — | 84.9% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 83.1% | — | 86.7% (best) |
| SimpleQA VerifiedShort factual questions | — | — | 25.4% |
| Price per million tokens | |||
| Input | $0.60 | $0.30 (best) | $0.40 |
| Output | $2.50 | $2.40 (best) | $2.40 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $1.07 | $0.825 (best) | $0.90 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 10 providers | Official Alibaba API | Official Alibaba API |
| Limits | |||
| Context window | 262,144 tokens | 262,144 tokens | 1,000,000 tokens (best) |
| Max output | 262,144 tokens (best) | 65,536 tokens | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | Yes | No |
| Video | No | Yes | Yes |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | — | qwen3.5-27b | qwen3.5-plus |
| API providers | 10 | 16 (best) | 10 |
| Released | Nov 6, 2025 | Feb 23, 2026 | Feb 16, 2026 |
| Knowledge cutoff | Aug 2024 | — | Apr 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 Thinking$11.00
- Qwen3.5 27B$7.80
- Qwen3.5 Plus$8.80
Which should you choose?
Which is better: Kimi K2 Thinking, Qwen3.5 27B or Qwen3.5 Plus?
It is close. Our weighted score puts them within 2 points (Qwen3.5 27B 61/100, Qwen3.5 Plus 59/100, Kimi K2 Thinking 42/100), so choose by what matters most for your work: Qwen3.5 27B on price and Qwen3.5 Plus 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 Thinking, Qwen3.5 27B or Qwen3.5 Plus?
Qwen3.5 27B is cheaper at $0.30 input / $2.40 output per million tokens (official Alibaba API price). Qwen3.5 Plus costs $0.40 input / $2.40 output per million tokens (official Alibaba API price); Kimi K2 Thinking costs $0.60 input / $2.50 output per million tokens (median across 10 API providers). At a typical mix of three input tokens to one output token, that is $0.825 per million tokens for Qwen3.5 27B versus $0.90 for Qwen3.5 Plus (1.1× as much) and $1.07 for Kimi K2 Thinking (1.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Kimi K2 Thinking has an ECI of 146.0, Qwen3.5 27B has not been scored yet and Qwen3.5 Plus has an ECI of 146.8.
Which is better for coding?
There are no published SWE-bench Verified results for Kimi K2 Thinking, Qwen3.5 27B and Qwen3.5 Plus 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?
Qwen3.5 Plus has the largest context window at 1,000,000 tokens, against 262,144 for Kimi K2 Thinking and 262,144 for Qwen3.5 27B. Maximum output per response: Kimi K2 Thinking up to 262,144, Qwen3.5 27B up to 65,536, Qwen3.5 Plus up to 65,536 tokens.
Which can read images, PDFs, audio or video?
Kimi K2 Thinking accepts text; Qwen3.5 27B accepts text, images, audio and video; Qwen3.5 Plus accepts text, images and video. Qwen3.5 27B handles the widest range of inputs.
Are any of these open source?
Kimi K2 Thinking and Qwen3.5 27B publishes its weights and can be self-hosted; Qwen3.5 Plus is proprietary.
Which is newer?
Qwen3.5 27B is the newest, released Feb 23, 2026. Qwen3.5 Plus came out Feb 16, 2026; Kimi K2 Thinking came out Nov 6, 2025. Knowledge cutoff: Kimi K2 Thinking Aug 2024, Qwen3.5 Plus Apr 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 Oct 4, 2026.
