Inkling vs Kimi K2.7 · No-Think

What I tested — same prompt, two models

I run the same fixed prompt set through every new model the day it drops — same string, one shot, single HTML file out — and I score the result 0–10 on whether it ran, how close it hit the brief, and how good it looked. Below is what came out when I gave the exact same prompts to Inkling and Kimi K2.7 · No-Think, side by side, on 47 shared tasks inside the Agent Operating System.

Both models were given identical prompts inside the Agent Operating System — no help, no iteration, no "best of N" tricks. I run each prompt once, save the HTML file the model produces, and score it 0–10 on whether it ran, how close it hit the brief, and how good it looked. The scoring is mine. The verdicts below are pulled from my source comparison guides at agentos.guide where I publish every score and the reasoning behind it.

Inkling · Benched on GoldieBench one-shot through Tinker's OpenAI-compatible endpoint at medium reasoning effort, then headless-playtested on the same rubric as the whole field. In the Agent OS it's wired into the opencode tab on your own Tinker key — the Ink Machine.

Kimi K2.7 · No-Think · Reserved for templated transforms where the plan is already in the prompt — the model just executes.

Strengths & weaknesses I logged

Pricing & context — the spec sheet

The verdict — which should you pick?

Not enough scored shared tasks yet for a head-to-head average. The live demos for both are on the matrix above — play them and form your own opinion.

If you only run one of these inside your stack, the head-to-head average above is the call. If you can run both, my honest play is to wire Inkling and Kimi K2.7 · No-Think both into the Agent Operating System and dispatch each from the kanban by task type — owning a frontier model instead of renting one — on your own key, pennies per build → Inkling, templated transforms where the plan is in the prompt → Kimi K2.7 · No-Think. That's the same setup I run for the 4,000+ founders inside the AI Profit Boardroom.

FAQ — Inkling vs Kimi K2.7 · No-Think

Which is better, Inkling or Kimi K2.7 · No-Think?

On Goldie Bench, Inkling averages no scored verdicts yet across the shared tasks, with 0 gold, 3 silver, 1 bronze overall. Kimi K2.7 · No-Think averages no scored verdicts yet, with 0 gold, 0 silver, 0 bronze. Not enough scored shared tasks yet to call a winner.

How much does Inkling cost vs Kimi K2.7 · No-Think?

Inkling: Inkling is open-weights — a 975B-parameter (41B active) Mixture-of-Experts model whose full weights are public on Hugging Face. You run it on your own key through Tinker's OpenAI-compatible endpoint (usage-based, ~$0.33/M sampling, 50% off at launch), or via Together / Fireworks / Modal / Databricks / Baseten. Benched here one-shot at medium reasoning effort via Tinker. Kimi K2.7 · No-Think: Same flat-rate plan as standard Kimi K2.7 — No-Think disables the chain-of-thought layer at runtime. Vendor: Moonshot AI (moonshot.ai).

What's the context window for Inkling vs Kimi K2.7 · No-Think?

Inkling has a 1,000,000 tokens context window. Kimi K2.7 · No-Think has a 256,000 tokens context window.

When should I pick Inkling over Kimi K2.7 · No-Think?

Pick Inkling for: Owning a frontier model instead of renting one — on your own key, pennies per build; Generative visuals, data-viz and single-file web builds you want one-shot; A customizable open base you can fine-tune on Tinker for your own domain. The trade-off is the weaknesses we logged on the bench: One-shot 3D games are weak — three.js dungeons/racers render a title screen but no playable scene, like most open models (crypt 2.5); Physics and particle sims are hit-or-miss — black-hole, plasma and cloth one-shots often render dark or static (2.3–3.5); Not the strongest overall — the closed frontier (Fable 5) still tops the raw benchmarks; Inkling trades peak for ownership.

When should I pick Kimi K2.7 · No-Think over Inkling?

Pick Kimi K2.7 · No-Think for: Templated transforms where the plan is in the prompt; Batched code generation jobs; Workflows where you want the model to stop second-guessing. The trade-off is the weaknesses we logged on the bench: Loses ground on multi-step tasks that benefit from planning; Not scored on the standalone bench — see methodology.

How does Goldie Bench score Inkling vs Kimi K2.7 · No-Think?

Every demo on this page was built by Julian Goldie inside the Agent Operating System — same fixed prompt for both models, one shot, single HTML file out. Each result gets a 0–10 score on whether it ran, how close it hit the brief, and how good it looked. The highest score on each task gets gold; second gets silver; third gets bronze. See methodology for full provenance.

Related comparisons

Other head-to-heads using the same scoring system:

Full model pages: Inkling · Kimi K2.7 · No-Think · back to the leaderboard