Kimi K2 Thinking vs Qwen3 14B vs Qwen3.5 Plus
Qwen3.5 Plus comes out ahead, 66 to 58 and 54 on our weighted score, though Qwen3 14B is 32% cheaper per token.
Moonshot AIKimi K2 Thinking
58/100- ECI146.0
- Price$0.60 / $2.50
- Context262K
Alibaba (Qwen)Qwen3 14B
54/100- ECI138.2
- Price$0.35 / $1.40
- Context131K
- Our pickAlibaba (Qwen)
Qwen3.5 Plus
66/100- ECI146.8
- Price$0.40 / $2.40
- Context1M
Qwen3.5 Plus is our pick
Qwen3.5 Plus is the better all-round choice, scoring 66/100 against Kimi K2 Thinking (58) and Qwen3 14B (54). It leads on inputs & features and context window. Qwen3 14B wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityQwen3.5 PlusCapabilities Index (ECI): Qwen3.5 Plus 146.8 · Kimi K2 Thinking 146.0 · Qwen3 14B 138.2
- Lowest priceQwen3 14BQwen3 14B $0.613 · 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 14B 131,072 tokens
- Widest inputsQwen3.5 PlusKimi K2 Thinking: Text · Qwen3 14B: Text · Qwen3.5 Plus: Text, Images, Video
- Self-hostingKimi K2 Thinking and Qwen3 14BPublishes downloadable weights
| Measure | Weight | Kimi K2 Thinking | Qwen3 14B | Qwen3.5 Plus |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 73 | 63 | 74 |
| Price | 25% | 48 | 60 | 52 |
| Inputs & features | 15% | 35 | 35 | 70 |
| Context window | 10% | 37 | 24 | 60 |
| Overall | 100% | 58/100 | 54/100 | 66/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Kimi K2 ThinkingMoonshot AI | Qwen3 14BAlibaba (Qwen) | Qwen3.5 PlusAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 146.0 | 138.2 | 146.8 (best) |
| ECI rank | #72 of 148 | #107 of 148 | #65 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 84.2% | 63.8% | 84.9% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 83.1% | 66.4% | 86.7% (best) |
| SimpleQA VerifiedShort factual questions | — | — | 25.4% |
| Price per million tokens | |||
| Input | $0.60 | $0.35 (best) | $0.40 |
| Output | $2.50 | $1.40 (best) | $2.40 |
| Cached input | — | — | — |
| Blended (3:1) | $1.07 | $0.613 (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 | 131,072 tokens | 1,000,000 tokens (best) |
| Max output | 262,144 tokens (best) | 8,192 tokens | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | Yes |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | — | qwen3-14b | qwen3.5-plus |
| API providers | 10 (best) | 1 | 10 (best) |
| Released | Nov 6, 2025 | Apr 29, 2025 | Feb 16, 2026 |
| Knowledge cutoff | Aug 2024 | Apr 2025 | 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 14B$6.30
- Qwen3.5 Plus$8.80
Which should you choose?
Which is better: Kimi K2 Thinking, Qwen3 14B or Qwen3.5 Plus?
Qwen3.5 Plus is the better all-round choice, scoring 66/100 against Kimi K2 Thinking (58) and Qwen3 14B (54). It leads on inputs & features and context window. Qwen3 14B wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Kimi K2 Thinking, Qwen3 14B or Qwen3.5 Plus?
Qwen3 14B is cheaper at $0.35 input / $1.40 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 $0.613 per million tokens for Qwen3 14B versus $0.90 for Qwen3.5 Plus (1.5× as much) and $1.07 for Kimi K2 Thinking (1.8× as much).
Which scores higher on benchmarks?
Qwen3.5 Plus scores higher on the Capabilities Index (ECI): Qwen3.5 Plus 146.8 (#65 of 148), Kimi K2 Thinking 146.0 (#72 of 148) and Qwen3 14B 138.2 (#107 of 148). The confidence ranges of the top two overlap (144.6–148.1 vs 143.4–147.6), so treat the gap as small. On individual benchmarks: GPQA Diamond — Qwen3.5 Plus 84.9%, Kimi K2 Thinking 84.2%, Qwen3 14B 63.8%; OTIS Mock AIME 2024–2025 — Qwen3.5 Plus 86.7%, Kimi K2 Thinking 83.1%, Qwen3 14B 66.4%.
Which is better for coding?
There are no published SWE-bench Verified results for Kimi K2 Thinking, Qwen3 14B and Qwen3.5 Plus yet, so there is no like-for-like coding score. On overall capability, Qwen3.5 Plus leads, which tends to carry over to coding, but test on your own codebase. 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 131,072 for Qwen3 14B. Maximum output per response: Kimi K2 Thinking up to 262,144, Qwen3 14B up to 8,192, Qwen3.5 Plus up to 65,536 tokens.
Which can read images, PDFs, audio or video?
Kimi K2 Thinking accepts text; Qwen3 14B accepts text; Qwen3.5 Plus accepts text, images and video. Qwen3.5 Plus handles the widest range of inputs.
Are any of these open source?
Kimi K2 Thinking and Qwen3 14B publishes its weights and can be self-hosted; Qwen3.5 Plus is proprietary.
Which is newer?
Qwen3.5 Plus is the newest, released Feb 16, 2026. Kimi K2 Thinking came out Nov 6, 2025; Qwen3 14B came out Apr 29, 2025. Knowledge cutoff: Kimi K2 Thinking Aug 2024, Qwen3 14B Apr 2025, 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.
