Kimi K2.5 vs Kimi K2 Instruct vs Qwen3.6 Plus
Qwen3.6 Plus comes out ahead, 66 to 62 and 50 on our weighted score, though Kimi K2 Instruct is 11% cheaper per token.
Moonshot AIKimi K2.5
62/100- ECI148.1
- Price$0.60 / $3.00
- Context262K
- Moonshot AI
Kimi K2 Instruct
50/100- ECI139.9
- Price$0.57 / $2.30
- Context131K
- Our pick
Alibaba (Qwen)Qwen3.6 Plus
66/100- ECI147.8
- Price$0.50 / $3.00
- Context1M
Qwen3.6 Plus is our pick
Qwen3.6 Plus is the better all-round choice, scoring 66/100 against Kimi K2.5 (62) and Kimi K2 Instruct (50). It leads on inputs & features and context window. Kimi K2 Instruct wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityKimi K2.5Capabilities Index (ECI): Kimi K2.5 148.1 · Qwen3.6 Plus 147.8 · Kimi K2 Instruct 139.9
- Lowest priceKimi K2 InstructKimi K2 Instruct $1.00 · 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 K2.5 262,144 · Kimi K2 Instruct 131,072 tokens
- Widest inputsKimi K2.5 and Qwen3.6 PlusKimi K2.5: Text, Images, Video · Kimi K2 Instruct: Text · Qwen3.6 Plus: Text, Images, Video
- Self-hostingKimi K2.5 and Kimi K2 InstructPublishes downloadable weights (Modified MIT)
| Measure | Weight | Kimi K2.5 | Kimi K2 Instruct | Qwen3.6 Plus |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 74 | 64 | 74 |
| Price | 25% | 44 | 48 | 45 |
| Inputs & features | 15% | 70 | 25 | 80 |
| Context window | 10% | 37 | 25 | 60 |
| Overall | 100% | 62/100 | 50/100 | 66/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Kimi K2.5Moonshot AI | Kimi K2 InstructMoonshot AI | Qwen3.6 PlusAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 148.1 (best) | 139.9 | 147.8 |
| ECI rank | #68 of 165 (best) | #112 of 165 | #69 of 165 |
| GPQA DiamondGraduate-level science questions | 87.6% | — | 88.4% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | — | — | 38.3% |
| OTIS Mock AIME 2024–2025Competition mathematics | 92.2% | — | 93.3% (best) |
| SWE-bench VerifiedFixing real GitHub issues | 73.8% (best) | — | 57.9% |
| SimpleQA VerifiedShort factual questions | 34.3% | — | 44.1% (best) |
| Price per million tokens | |||
| Input | $0.60 | $0.57 | $0.50 (best) |
| Output | $3.00 | $2.30 (best) | $3.00 |
| Cached input | — | — | $0.05 |
| Blended (3:1) | $1.20 | $1.00 (best) | $1.13 |
| Long-context rate | Same rate | Same rate | Over 256K: $2.00 / $6.00 |
| Price source | Median of 33 providers | Median of 11 providers | Official Alibaba API |
| Limits | |||
| Context window | 262,144 tokens | 131,072 tokens | 1,000,000 tokens (best) |
| Max output | 262,144 tokens (best) | 32,768 tokens | 65,536 tokens |
| 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 | No | No | Yes |
| Availability | |||
| Weights | Open | OpenModified MIT | Proprietary |
| API model ID | — | — | qwen3.6-plus |
| API providers | 33 (best) | 11 | 22 |
| Released | Jan 27, 2026 | Jul 11, 2025 | Apr 2, 2026 |
| Knowledge cutoff | Jan 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 K2.5$12.00
- Kimi K2 Instruct$10.30
- Qwen3.6 Plus$11.00
Which should you choose?
Which is better: Kimi K2.5, Kimi K2 Instruct or Qwen3.6 Plus?
Qwen3.6 Plus is the better all-round choice, scoring 66/100 against Kimi K2.5 (62) and Kimi K2 Instruct (50). It leads on inputs & features and context window. Kimi K2 Instruct wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Kimi K2.5, Kimi K2 Instruct or Qwen3.6 Plus?
Kimi K2 Instruct is cheaper at $0.57 input / $2.30 output per million tokens (median across 11 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 $1.00 per million tokens for Kimi K2 Instruct versus $1.13 for Qwen3.6 Plus (1.1× as much) and $1.20 for Kimi K2.5 (1.2× as much).
Which scores higher on benchmarks?
Kimi K2.5 scores higher on the Capabilities Index (ECI): Kimi K2.5 148.1 (#68 of 165), Qwen3.6 Plus 147.8 (#69 of 165) and Kimi K2 Instruct 139.9 (#112 of 165). The confidence ranges of the top two overlap (146.3–149.5 vs 145.8–149.6), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for Kimi K2 Instruct yet, so there is no like-for-like coding score. On overall capability, Kimi K2.5 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?
Qwen3.6 Plus has the largest context window at 1,000,000 tokens, against 262,144 for Kimi K2.5 and 131,072 for Kimi K2 Instruct. Maximum output per response: Kimi K2.5 up to 262,144, Kimi K2 Instruct up to 32,768, Qwen3.6 Plus up to 65,536 tokens.
Which can read images, PDFs, audio or video?
Kimi K2.5 accepts text, images and video; Kimi K2 Instruct accepts text; Qwen3.6 Plus accepts text, images and video. Kimi K2.5 handles the widest range of inputs.
Are any of these open source?
Kimi K2.5 and Kimi K2 Instruct 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 came out Jul 11, 2025. Knowledge cutoff: Kimi K2.5 Jan 2025, Qwen3.6 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 Nov 2, 2026.
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