Real head-to-head · same prompt, one shot

Qwen 3.7 vs Kimi K2.7 · No-Think

Multilingual open-weights — strong on Chinese reasoning. vs Pure execution mode — no chain of thought.

Qwen 3.7 · context256K tokens
Kimi K2.7 · No-Think · context256K tokens
Qwen 3.7 · priceOpen weights · free for individuals
Kimi K2.7 · No-Think · priceFlat plan (no per-token bill)
Qwen 3.7 · vendorAlibaba
Kimi K2.7 · No-Think · vendorMoonshot AI

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 Qwen 3.7 and Kimi K2.7 · No-Think, side by side, on 0 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.

Qwen 3.7 · Wired alongside GLM-5.2 in Agent OS for open-weights agent loops where you want vendor diversity.

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

Side-by-side on 8 shared tasks

Click any cell to play that model's actual one-shot attempt. Medals are derived from my 0–10 scores per task (highest = 🥇, second = 🥈, third = 🥉).

Task ↓
Qwen 3.7
Kimi K2.7 · No-Think
Game
🥈Qwen 3.7 on Arcade
— not attempted —
Game
— not attempted —
Kimi K2.7 · No-Think on Game
Page
🥉Qwen 3.7 on Landing
— not attempted —
Sim
🥈
— not attempted —
Sim
— not attempted —
Kimi K2.7 · No-Think on Galaxy
Sim
🥈Qwen 3.7 on Orbit
— not attempted —
Sim
— not attempted —
Kimi K2.7 · No-Think on Solar
Visual
🥉Qwen 3.7 on Voxel
— not attempted —

Strengths & weaknesses I logged

Qwen 3.7

Strengths

  • Open weights, free for individuals — same model class as GLM-5.2
  • Best-of-three on fluid simulation in the Goldie Bench bench
  • Multilingual depth — Chinese reasoning especially strong

Trade-offs

  • Only 5 tasks scored on the bench so far — small sample size
  • Trails GLM-5.2 on cinematic visual builds at similar pricing

Kimi K2.7 · No-Think

Strengths

  • Skips planning to ship straight to code
  • Useful when you've already done the reasoning in the prompt
  • Predictable latency for batched jobs

Trade-offs

  • Loses ground on multi-step tasks that benefit from planning
  • Not scored on the standalone bench — see methodology

Pricing & context — the spec sheet

Spec Qwen 3.7 Kimi K2.7 · No-Think
VendorAlibabaMoonshot AI
Context window256,000 tokens256,000 tokens
PriceOpen weights · free for individualsFlat plan (no per-token bill)
Pricing detailAlibaba's open-weights release — downloadable from Hugging Face, runnable locally or via Alibaba Cloud's free tier for individuals.Same flat-rate plan as standard Kimi K2.7 — No-Think disables the chain-of-thought layer at runtime.
Release2026-062026-06
Bench coverage5/5 scored · avg 7.50/100/3 scored · avg —

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 Qwen 3.7 and Kimi K2.7 · No-Think both into the Agent Operating System and dispatch each from the kanban by task type — open-weights alternative to glm-5.2 when you want a different model family → Qwen 3.7, templated transforms where the plan is in the prompt → Kimi K2.7 · No-Think. That's the same setup I run for the 3,600+ founders inside the AI Profit Boardroom.

FAQ — Qwen 3.7 vs Kimi K2.7 · No-Think

Which is better, Qwen 3.7 or Kimi K2.7 · No-Think?

On Goldie Bench, Qwen 3.7 averages no scored verdicts yet across the shared tasks, with 0 gold, 3 silver, 2 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 Qwen 3.7 cost vs Kimi K2.7 · No-Think?

Qwen 3.7: Alibaba's open-weights release — downloadable from Hugging Face, runnable locally or via Alibaba Cloud's free tier for individuals. Kimi K2.7 · No-Think: Same flat-rate plan as standard Kimi K2.7 — No-Think disables the chain-of-thought layer at runtime.

What's the context window for Qwen 3.7 vs Kimi K2.7 · No-Think?

Qwen 3.7 has a 256,000 tokens context window. Kimi K2.7 · No-Think has a 256,000 tokens context window.

When should I pick Qwen 3.7 over Kimi K2.7 · No-Think?

Pick Qwen 3.7 for: Open-weights alternative to GLM-5.2 when you want a different model family; Multilingual workloads (Chinese, multi-script content); Fluid and particle simulations. The trade-off is the weaknesses we logged on the bench: Only 5 tasks scored on the bench so far — small sample size; Trails GLM-5.2 on cinematic visual builds at similar pricing.

When should I pick Kimi K2.7 · No-Think over Qwen 3.7?

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 Qwen 3.7 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.

The same stack Julian uses

Run this stack yourself.

Every demo on this bench was built inside the Agent Operating System — one prompt, one shot, single HTML file out. The Agent OS, the prompts, the templates, the weekly walkthroughs and 3,600+ founders shipping with it every day all live inside the AI Profit Boardroom.

3,600+founders
258documented wins
38countries
$100k+/mocommunity MRR