Real head-to-head · same prompt, one shot
Opus 4.8 vs Kimi K2.7
The reasoning king — deepest thinking, premium price. vs The heavy lifter — frontier coder at flat-rate.
Head-to-head verdict: Opus 4.8 wins 10–2 with 1 tie.
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 Opus 4.8 and Kimi K2.7, side by side, on 15 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.
Opus 4.8 · The default when the build has to ship on the first prompt — Opus is the safety net inside Agent OS for hard one-shots.
Kimi K2.7 · Wired into the Agent OS as the heavy-lifter for game/sim prototypes and Kanban-dispatched code work. Mode toggled per task: Quality for one-shot games, Fast for short bursts.
Side-by-side on 25 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 ↓
Opus 4.8
Kimi K2.7
Game
Game
Game
Game
Game
Page
Sim
Sim
Sim
Sim
Sim
Sim
Sim
Visual
Visual
Sim
— not attempted —
Sim
— not attempted —
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— not attempted —
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— not attempted —
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— not attempted —
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Where Opus 4.8 beat Kimi K2.7
The tasks where I gave Opus 4.8 a higher 0–10 score on the same prompt — with the actual commentary from my source guides.
Blackhole
Sim
Opus 4.8 9.0
·
Kimi K2.7 6.0
(+3.0)
· winner · hit the brief
What I saw: Opus nailed it — a pure-black event horizon, a bright photon ring, and the disk bent up and over the top exactly like the film's lensing. GLM came in strong with a clean ring and a starfield warping past the hole. Kimi's disk is fine, but the background is a soft grey blur instea…
Orbit
Sim
Opus 4.8 9.0
·
Kimi K2.7 6.0
(+3.0)
· winner · accuracy
What I saw: Opus nailed the brief — labelled planet orbits, a real NEO / close-pass panel, a sim clock. GLM went for drama: a glowing nebula swirl that's gorgeous but reads more galaxy than orbit map. Kimi's is accurate but dim and sparse.
Landing
Page
Opus 4.8 9.0
·
Kimi K2.7 6.5
(+2.5)
· tie · top
What I saw: Funniest result of the lot: GLM and Opus independently produced near-identical premium 'Introducing Nova 1 — Intelligence, reimagined / distilled' keynote pages — gradient hero, full nav, pricing tiers. A dead heat. Kimi's was a plainer set of feature cards.
Voxel
Visual
Opus 4.8 8.5
·
Kimi K2.7 6.0
(+2.5)
What I saw: GLM built the densest, most detailed city — windowed skyscrapers, a speed + coins HUD. Opus ran the furthest with the cleanest motion (Score 303). Kimi's runner plays fine but is unforgiving — it crashes within seconds.
Fluid
Sim
Opus 4.8 7.0
·
Kimi K2.7 5.0
(+2.0)
What I saw: GLM filled the bowl with glowing liquid that actually sloshes — the most convincing 'liquid in a bowl'. Opus's particles glowed but clumped to the centre. Kimi's collapsed into a tiny blob.
Where Kimi K2.7 beat Opus 4.8
The tasks where I gave Kimi K2.7 a higher 0–10 score on the same prompt — with the actual commentary from my source guides.
Fractal
Sim
Kimi K2.7 9.0
·
Opus 4.8 8.5
(+0.5)
· winner · pure wow
What I saw: All three are genuinely good. Kimi's is the jaw-dropper — a deep rainbow plunge into a seahorse spiral, dense with self-similar detail. Opus zooms smoothly into the seahorse valley with a tasteful cycling palette. GLM frames the whole iconic set in a fire palette with a live coor…
Raycaster
Game
Kimi K2.7 8.5
·
Opus 4.8 8.0
(+0.5)
· winner · cleanest
What I saw: Kimi nailed it — brick walls, a checkered floor, a clean minimap, textbook Wolfenstein, runs clean out of the box. Opus's is close and more atmospheric: warm fog and a vignette down a stone corridor (A/D to turn, W/S to move). GLM's engine is genuinely good — brick and mossy-ston…
Strengths & weaknesses I logged
Opus 4.8
Strengths
- Most consistent across the Goldie Bench bench — no weak build, 8.46/10 average
- Deepest one-shot reasoning, especially on game-feel and physics
- Extended thinking mode handles up to 1M tokens of context
Trade-offs
- 5–10× the per-token cost of every other model on the bench
- Less flair on cinematic visuals than GLM-5.2 — playing it safer wins on accuracy, costs you on showpiece moments
Kimi K2.7
Strengths
- Best-of-three on interactive games — raycaster, DOOM, monster AI
- Three speed modes (Fast / No-Think / Quality) you can swap per task
- Flat-rate plan eliminates the per-token meter, so iteration is free
Trade-offs
- Plays plainest on abstract visual prompts — synthwave grids, fluid sims, aurora — where GLM and Opus add more flair
- Bronze average on the Goldie Bench bench despite the gold-medal games — its visual builds are accurate but understated
Pricing & context — the spec sheet
| Spec | Opus 4.8 | Kimi K2.7 |
|---|---|---|
| Vendor | Anthropic | Moonshot AI |
| Context window | 200,000 tokens (1M with extended thinking) | 256,000 tokens |
| Price | $15 / $75 per M tokens | Flat plan (no per-token bill) |
| Pricing detail | Premium pricing via the Anthropic API: $15 per million input tokens, $75 per million output tokens. Extended thinking is included but adds latency. | Available on Moonshot's flat-rate subscription plan — no per-token billing for individual builders. The plan covers all three speed modes (Fast, No-Think, Quality). |
| Release | 2026-05 | 2026-06 |
| Bench coverage | 13/17 scored · avg 8.46/10 | 14/23 scored · avg 7.25/10 |
The verdict — which should you pick?
Across 13 scored shared tasks, Opus 4.8 averaged 8.46/10, beating Kimi K2.7's 7.31/10 by 1.15 points. Pick Opus 4.8 when the build has to ship on the first prompt and you can afford the trade-offs in the comparison below.
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 Opus 4.8 and Kimi K2.7 both into the Agent Operating System and dispatch each from the kanban by task type — mission-critical one-shot builds where 'has to work the first time' matters → Opus 4.8, interactive game prototypes you want shippable on the first prompt → Kimi K2.7. That's the same setup I run for the 3,600+ founders inside the AI Profit Boardroom.
FAQ — Opus 4.8 vs Kimi K2.7
Which is better, Opus 4.8 or Kimi K2.7?
On Goldie Bench, Opus 4.8 averages 8.46/10 across the shared tasks, with 8 gold, 5 silver, 0 bronze overall. Kimi K2.7 averages 7.31/10, with 3 gold, 2 silver, 9 bronze. Opus 4.8 wins the head-to-head 10–2.
How much does Opus 4.8 cost vs Kimi K2.7?
Opus 4.8: Premium pricing via the Anthropic API: $15 per million input tokens, $75 per million output tokens. Extended thinking is included but adds latency. Kimi K2.7: Available on Moonshot's flat-rate subscription plan — no per-token billing for individual builders. The plan covers all three speed modes (Fast, No-Think, Quality).
What's the context window for Opus 4.8 vs Kimi K2.7?
Opus 4.8 has a 200,000 tokens (1M with extended thinking) context window. Kimi K2.7 has a 256,000 tokens context window.
When should I pick Opus 4.8 over Kimi K2.7?
Pick Opus 4.8 for: Mission-critical one-shot builds where 'has to work the first time' matters; Hard reasoning tasks (planning, multi-step) where you'll pay for the depth; Anything where vendor reliability beats the per-token bill. The trade-off is the weaknesses we logged on the bench: 5–10× the per-token cost of every other model on the bench; Less flair on cinematic visuals than GLM-5.2 — playing it safer wins on accuracy, costs you on showpiece moments.
When should I pick Kimi K2.7 over Opus 4.8?
Pick Kimi K2.7 for: Interactive game prototypes you want shippable on the first prompt; High-iteration agent loops where per-token cost would dominate; Long-context refactors using the 256K window inside Agent OS. The trade-off is the weaknesses we logged on the bench: Plays plainest on abstract visual prompts — synthwave grids, fluid sims, aurora — where GLM and Opus add more flair; Bronze average on the {{SITE_NAME}} bench despite the gold-medal games — its visual builds are accurate but understated.
How does Goldie Bench score Opus 4.8 vs Kimi K2.7?
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:
Opus 4.8 vs GLM-5.2 Kimi K2.7 vs GLM-5.2 Opus 4.8 vs Qwen 3.7 Kimi K2.7 vs Qwen 3.7Full model pages: Opus 4.8 · Kimi K2.7 · back to the leaderboard
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