Qwen3 14B vs Kimi K2.6 vs DeepSeek-V3.1
Kimi K2.6 comes out ahead, 65 to 55 and 54 on our weighted score, though DeepSeek-V3.1 is 2.8× cheaper per token.
Alibaba (Qwen)Qwen3 14B
54/100- ECI138.2
- Price$0.35 / $1.40
- Context131K
- Our pick
Moonshot AIKimi K2.6
65/100- ECI151.1
- Price$0.95 / $4.00
- Context262K
DeepSeekDeepSeek-V3.1
55/100- ECI139.9
- Price$0.385 / $1.25
- Context131K
Kimi K2.6 is our pick
Kimi K2.6 is the better all-round choice, scoring 65/100 against DeepSeek-V3.1 (55) and Qwen3 14B (54). It leads on capability, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityKimi K2.6Capabilities Index (ECI): Kimi K2.6 151.1 · DeepSeek-V3.1 139.9 · Qwen3 14B 138.2
- Lowest priceDeepSeek-V3.1DeepSeek-V3.1 $0.601 · Qwen3 14B $0.613 · Kimi K2.6 $1.71 per 1M tokens (3:1 blend)
- Longest contextKimi K2.6Kimi K2.6 262,144 · Qwen3 14B 131,072 · DeepSeek-V3.1 131,072 tokens
- Widest inputsKimi K2.6Qwen3 14B: Text · Kimi K2.6: Text, Images, Video · DeepSeek-V3.1: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Qwen3 14B | Kimi K2.6 | DeepSeek-V3.1 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 63 | 79 | 65 |
| Price | 25% | 60 | 39 | 60 |
| Inputs & features | 15% | 35 | 80 | 35 |
| Context window | 10% | 24 | 37 | 24 |
| Overall | 100% | 54/100 | 65/100 | 55/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Qwen3 14BAlibaba (Qwen) | Kimi K2.6Moonshot AI | DeepSeek-V3.1DeepSeek |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 138.2 | 151.1 (best) | 139.9 |
| ECI rank | #107 of 148 | #45 of 148 (best) | #100 of 148 |
| GPQA DiamondGraduate-level science questions | 63.8% | 90.8% (best) | — |
| FrontierMath Tiers 1–3Research-level mathematics | — | 57.2% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 66.4% | 96.1% (best) | — |
| SWE-bench VerifiedFixing real GitHub issues | — | 76.7% | — |
| SimpleQA VerifiedShort factual questions | — | 34.9% | — |
| Price per million tokens | |||
| Input | $0.35 (best) | $0.95 | $0.385 |
| Output | $1.40 | $4.00 | $1.25 (best) |
| Cached input | — | $0.16 | — |
| Blended (3:1) | $0.613 | $1.71 | $0.601 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Official Moonshot AI API | Median of 8 providers |
| Limits | |||
| Context window | 131,072 tokens | 262,144 tokens (best) | 131,072 tokens |
| Max output | 8,192 tokens | 262,144 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 | Yes | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Open | OpenMIT License |
| API model ID | qwen3-14b | kimi-k2.6 | — |
| API providers | 1 | 46 (best) | 8 |
| Released | Apr 29, 2025 | Apr 21, 2026 | Aug 21, 2025 |
| 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 14B$6.30
- Kimi K2.6$17.50
- DeepSeek-V3.1$6.35
Which should you choose?
Which is better: Qwen3 14B, Kimi K2.6 or DeepSeek-V3.1?
Kimi K2.6 is the better all-round choice, scoring 65/100 against DeepSeek-V3.1 (55) and Qwen3 14B (54). It leads on capability, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Qwen3 14B, Kimi K2.6 or DeepSeek-V3.1?
DeepSeek-V3.1 is cheaper at $0.385 input / $1.25 output per million tokens (median across 8 API providers). Qwen3 14B costs $0.35 input / $1.40 output per million tokens (official Alibaba API price); Kimi K2.6 costs $0.95 input / $4.00 output per million tokens (official Moonshot AI API price). At a typical mix of three input tokens to one output token, that is $0.601 per million tokens for DeepSeek-V3.1 versus $0.613 for Qwen3 14B (1× as much) and $1.71 for Kimi K2.6 (2.8× as much).
Which scores higher on benchmarks?
Kimi K2.6 scores higher on the Capabilities Index (ECI): Kimi K2.6 151.1 (#45 of 148), DeepSeek-V3.1 139.9 (#100 of 148) and Qwen3 14B 138.2 (#107 of 148). Their confidence ranges do not overlap (149.1–152.8 vs 136.1–143.3), so the gap is a real one.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3 14B and DeepSeek-V3.1 yet, so there is no like-for-like coding score. On overall capability, Kimi K2.6 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?
Kimi K2.6 has the largest context window at 262,144 tokens, against 131,072 for Qwen3 14B and 131,072 for DeepSeek-V3.1. Maximum output per response: Qwen3 14B up to 8,192, Kimi K2.6 up to 262,144, DeepSeek-V3.1 up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Qwen3 14B accepts text; Kimi K2.6 accepts text, images and video; DeepSeek-V3.1 accepts text. Kimi K2.6 handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights (MIT License), so you can self-host them.
Which is newer?
Kimi K2.6 is the newest, released Apr 21, 2026. DeepSeek-V3.1 came out Aug 21, 2025; Qwen3 14B came out Apr 29, 2025. Knowledge cutoff: Qwen3 14B Apr 2025, Kimi K2.6 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 Oct 4, 2026.
