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Comparison · 3 models · Updated Oct 4, 2026

Inkling Small vs Kimi K2.7 Code vs Qwen3.8 27B

Inkling Small comes out ahead, 70 to 67 and 64 on our weighted score, and it is the cheaper option too.

  1. Our pick

    Thinking Machines

    Inkling Small

    Released Jul 30, 2026

    70/100
    • ECI150.2
    • Price$0.50 / $1.20
    • Context1.05M
    Full specs Remove Inkling Small

  2. Moonshot AI

    Kimi K2.7 Code

    Released Jun 12, 2026

    64/100
    • ECI150.0
    • Price$0.95 / $4.00
    • Context262K
    Full specs Remove Kimi K2.7 Code

  3. Alibaba (Qwen)

    Qwen3.8 27B

    Released Aug 14, 2026

    67/100
    • ECI149.4
    • Price$0.40 / $2.50
    • Context262K
    Full specs Remove Qwen3.8 27B
01 — Verdict

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 · Kimi K2.7 Code 262,144 · Qwen3.8 27B 262,144 tokens
  • Widest inputsSame inputsInkling Small: Text, Images, Audio · Kimi K2.7 Code: Text, Images, Video · Qwen3.8 27B: Text, Images, Video
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightInkling SmallKimi K2.7 CodeQwen3.8 27B
CapabilityCapabilities Index (ECI)50%787877
Price25%583951
Inputs & features15%708080
Context window10%613737
Overall100%70/10064/10067/100
02 — Side by side

Every spec in one table

Highlighted cells lead their row. Dashes mean the data is not published.

Inkling Small vs Kimi K2.7 Code vs Qwen3.8 27B specifications side by side
SpecificationInkling SmallThinking MachinesKimi K2.7 CodeMoonshot AIQwen3.8 27BAlibaba (Qwen)
Capability
Capabilities Index (ECI)150.2 (best)150.0149.4
ECI rank#47 of 148 (best)#49 of 148#53 of 148
GPQA DiamondGraduate-level science questions88.5% (best)87.9%—
FrontierMath Tiers 1–3Research-level mathematics46.3%54.0% (best)—
OTIS Mock AIME 2024–2025Competition mathematics90.0%95.6% (best)—
SimpleQA VerifiedShort factual questions19.1%36.5% (best)—
Price per million tokens
Input$0.50$0.95$0.40 (best)
Output$1.20 (best)$4.00$2.50
Cached input—$0.19—
Blended (3:1)$0.675 (best)$1.71$0.925
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 11 providersOfficial Moonshot AI APIMedian of 39 providers
Limits
Context window1,048,576 tokens (best)262,144 tokens262,144 tokens
Max output1,048,576 tokens (best)262,144 tokens32,768 tokens
Inputs and features
TextYesYesYes
ImagesYesYesYes
PDFsNoNoNo
AudioYesNoNo
VideoNoYesYes
ReasoningYesYesYes
Tool callingYesYesYes
Structured outputNoYesYes
Availability
WeightsOpenApache-2.0OpenOpen
API model ID—kimi-k2.7-code—
API providers1151 (best)41
ReleasedJul 30, 2026Jun 12, 2026Aug 14, 2026
Knowledge cutoff—Jan 2025—
03 — Cost

What would a month cost?

Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.

  • Inkling Small$7.40
  • Kimi K2.7 Code$17.50
  • Qwen3.8 27B$9.00
04 — Questions

Which should you choose?

Which is better: Inkling Small, Kimi K2.7 Code or Qwen3.8 27B?

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, Inkling Small, Kimi K2.7 Code or Qwen3.8 27B?

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 Inkling Small, Kimi K2.7 Code and Qwen3.8 27B 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 Kimi K2.7 Code and 262,144 for Qwen3.8 27B. Maximum output per response: Inkling Small up to 1,048,576, Kimi K2.7 Code up to 262,144, Qwen3.8 27B up to 32,768 tokens.

Which can read images, PDFs, audio or video?

Inkling Small accepts text, images and audio; Kimi K2.7 Code accepts text, images and video; Qwen3.8 27B accepts text, images and video. 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.

05 — Keep comparing

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