Real head-to-head · same prompt, one shot
Opus 4.8 vs Qwen 3.7
The reasoning king — deepest thinking, premium price. vs Multilingual open-weights — strong on Chinese reasoning.
Head-to-head verdict: Opus 4.8 wins 4–0 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 Qwen 3.7, side by side, on 5 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.
Qwen 3.7 · Wired alongside GLM-5.2 in Agent OS for open-weights agent loops where you want vendor diversity.
Side-by-side on 17 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
Qwen 3.7
Game
Page
Sim
Sim
Visual
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Where Opus 4.8 beat Qwen 3.7
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.
Orbit
Sim
Opus 4.8 9.0
·
Qwen 3.7 7.5
(+1.5)
· 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.
Voxel
Visual
Opus 4.8 8.5
·
Qwen 3.7 7.0
(+1.5)
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.
Landing
Page
Opus 4.8 9.0
·
Qwen 3.7 8.0
(+1.0)
· 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.
Arcade
Game
Opus 4.8 8.5
·
Qwen 3.7 8.0
(+0.5)
· winner · game-feel
What I saw: All three shipped a genuinely juicy game. Opus's breakout had the most game-feel — particle bursts and a live combo. Kimi's breakout was clean and solid. GLM went its own way with fullscreen neon asteroids. The closest of the practical five.
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
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
Pricing & context — the spec sheet
| Spec | Opus 4.8 | Qwen 3.7 |
|---|---|---|
| Vendor | Anthropic | Alibaba |
| Context window | 200,000 tokens (1M with extended thinking) | 256,000 tokens |
| Price | $15 / $75 per M tokens | Open weights · free for individuals |
| 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. | Alibaba's open-weights release — downloadable from Hugging Face, runnable locally or via Alibaba Cloud's free tier for individuals. |
| Release | 2026-05 | 2026-06 |
| Bench coverage | 13/17 scored · avg 8.46/10 | 5/5 scored · avg 7.50/10 |
The verdict — which should you pick?
Across 5 scored shared tasks, Opus 4.8 averaged 8.40/10, beating Qwen 3.7's 7.50/10 by 0.90 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 Qwen 3.7 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, open-weights alternative to glm-5.2 when you want a different model family → Qwen 3.7. That's the same setup I run for the 3,600+ founders inside the AI Profit Boardroom.
FAQ — Opus 4.8 vs Qwen 3.7
Which is better, Opus 4.8 or Qwen 3.7?
On Goldie Bench, Opus 4.8 averages 8.40/10 across the shared tasks, with 8 gold, 5 silver, 0 bronze overall. Qwen 3.7 averages 7.50/10, with 0 gold, 3 silver, 2 bronze. Opus 4.8 wins the head-to-head 4–0.
How much does Opus 4.8 cost vs Qwen 3.7?
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. Qwen 3.7: Alibaba's open-weights release — downloadable from Hugging Face, runnable locally or via Alibaba Cloud's free tier for individuals.
What's the context window for Opus 4.8 vs Qwen 3.7?
Opus 4.8 has a 200,000 tokens (1M with extended thinking) context window. Qwen 3.7 has a 256,000 tokens context window.
When should I pick Opus 4.8 over Qwen 3.7?
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 Qwen 3.7 over Opus 4.8?
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.
How does Goldie Bench score Opus 4.8 vs Qwen 3.7?
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 Qwen 3.7 vs GLM-5.2 Opus 4.8 vs Kimi K2.7 Qwen 3.7 vs Kimi K2.7Full model pages: Opus 4.8 · Qwen 3.7 · 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