Qwen3.8 27B vs Kimi K2.7 Code vs Inkling Small
Inkling Small comes out ahead, 70 to 67 and 64 on our weighted score, and it is the cheaper option too.
Alibaba (Qwen)Qwen3.8 27B
67/100- ECI149.4
- Price$0.40 / $2.50
- Context262K
Moonshot AIKimi K2.7 Code
64/100- ECI150.0
- Price$0.95 / $4.00
- Context262K
- Our pick
Thinking MachinesInkling Small
70/100- ECI150.2
- Price$0.50 / $1.20
- Context1.05M
Inkling Small is our pick
Inkling Small is the better all-round choice, scoring 70/100 against Qwen3.8 27B (67) and Kimi K2.7 Code (64). It leads on price and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityInkling SmallCapabilities Index (ECI): Inkling Small 150.2 · Kimi K2.7 Code 150.0 · Qwen3.8 27B 149.4
- Lowest priceInkling SmallInkling Small $0.675 · Qwen3.8 27B $0.925 · Kimi K2.7 Code $1.71 per 1M tokens (3:1 blend)
- Longest contextInkling SmallInkling Small 1,048,576 · Qwen3.8 27B 262,144 · Kimi K2.7 Code 262,144 tokens
- Widest inputsSame inputsQwen3.8 27B: Text, Images, Video · Kimi K2.7 Code: Text, Images, Video · Inkling Small: Text, Images, Audio
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Qwen3.8 27B | Kimi K2.7 Code | Inkling Small |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 77 | 78 | 78 |
| Price | 25% | 51 | 39 | 58 |
| Inputs & features | 15% | 80 | 80 | 70 |
| Context window | 10% | 37 | 37 | 61 |
| Overall | 100% | 67/100 | 64/100 | 70/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Qwen3.8 27BAlibaba (Qwen) | Kimi K2.7 CodeMoonshot AI | Inkling SmallThinking Machines |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 149.4 | 150.0 | 150.2 (best) |
| ECI rank | #53 of 148 | #49 of 148 | #47 of 148 (best) |
| GPQA DiamondGraduate-level science questions | — | 87.9% | 88.5% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | — | 54.0% (best) | 46.3% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 95.6% (best) | 90.0% |
| SimpleQA VerifiedShort factual questions | — | 36.5% (best) | 19.1% |
| Price per million tokens | |||
| Input | $0.40 (best) | $0.95 | $0.50 |
| Output | $2.50 | $4.00 | $1.20 (best) |
| Cached input | — | $0.19 | — |
| Blended (3:1) | $0.925 | $1.71 | $0.675 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 39 providers | Official Moonshot AI API | Median of 11 providers |
| Limits | |||
| Context window | 262,144 tokens | 262,144 tokens | 1,048,576 tokens (best) |
| Max output | 32,768 tokens | 262,144 tokens | 1,048,576 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | Yes |
| Video | Yes | Yes | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | No |
| Availability | |||
| Weights | Open | Open | OpenApache-2.0 |
| API model ID | — | kimi-k2.7-code | — |
| API providers | 41 | 51 (best) | 11 |
| Released | Aug 14, 2026 | Jun 12, 2026 | Jul 30, 2026 |
| Knowledge cutoff | — | 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.
- Qwen3.8 27B$9.00
- Kimi K2.7 Code$17.50
- Inkling Small$7.40
Which should you choose?
Which is better: Qwen3.8 27B, Kimi K2.7 Code or Inkling Small?
Inkling Small is the better all-round choice, scoring 70/100 against Qwen3.8 27B (67) and Kimi K2.7 Code (64). It leads on price and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Qwen3.8 27B, Kimi K2.7 Code or Inkling Small?
Inkling Small is cheaper at $0.50 input / $1.20 output per million tokens (median across 11 API providers). Qwen3.8 27B costs $0.40 input / $2.50 output per million tokens (median across 39 API providers); Kimi K2.7 Code 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 $0.675 per million tokens for Inkling Small versus $0.925 for Qwen3.8 27B (1.4× as much) and $1.71 for Kimi K2.7 Code (2.5× as much).
Which scores higher on benchmarks?
Inkling Small scores higher on the Capabilities Index (ECI): Inkling Small 150.2 (#47 of 148), Kimi K2.7 Code 150.0 (#49 of 148) and Qwen3.8 27B 149.4 (#53 of 148). The confidence ranges of the top two overlap (147.5–152.1 vs 148.1–151.8), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3.8 27B, Kimi K2.7 Code and Inkling Small yet, so there is no like-for-like coding score. On overall capability, Inkling Small 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?
Inkling Small has the largest context window at 1,048,576 tokens, against 262,144 for Qwen3.8 27B and 262,144 for Kimi K2.7 Code. Maximum output per response: Qwen3.8 27B up to 32,768, Kimi K2.7 Code up to 262,144, Inkling Small up to 1,048,576 tokens.
Which can read images, PDFs, audio or video?
Qwen3.8 27B accepts text, images and video; Kimi K2.7 Code accepts text, images and video; Inkling Small accepts text, images and audio. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights (Apache-2.0), so you can self-host them.
Which is newer?
Qwen3.8 27B is the newest, released Aug 14, 2026. Inkling Small came out Jul 30, 2026; Kimi K2.7 Code came out Jun 12, 2026. Knowledge cutoff: Kimi K2.7 Code 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.
