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

Qwen 3.7 vs Claude Fable 5

Multilingual open-weights — strong on Chinese reasoning. vs The newest Anthropic model — first Mythos-class made generally available.

Qwen 3.7 · context256K tokens
Claude Fable 5 · context200K tokens
Qwen 3.7 · priceOpen weights · free for individuals
Claude Fable 5 · priceAnthropic API pricing
Qwen 3.7 · vendorAlibaba
Claude Fable 5 · vendorAnthropic

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 Claude Fable 5, 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.

Claude Fable 5 · Selected from Agent OS for the highest-stakes one-shot work — replacing Opus 4.8 as the safety net on hard prompts. Bench scoring pending.

Side-by-side on 5 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
Claude Fable 5
Game
🥈Qwen 3.7 on Arcade
— not attempted —
Page
Qwen 3.7 on Landing
— not attempted —
Sim
🥉
— not attempted —
Sim
🥉Qwen 3.7 on Orbit
— not attempted —
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

Claude Fable 5

Strengths

  • Anthropic's most capable publicly-available model — vendor claim: 'capabilities exceed those of any model we've ever made generally available'
  • Tops external SWE-bench Verified at 95.0% in Julian's three-dragons writeup
  • Top-tier plan quality (9.1/10) on Kilo's plan-vs-build rubric

Trade-offs

  • No goldiebench per-task scores yet — bench rank pending a published head-to-head guide
  • Premium pricing; Fusion premium panel reportedly out-scores it at half the API cost

Pricing & context — the spec sheet

Spec Qwen 3.7 Claude Fable 5
VendorAlibabaAnthropic
Context window256,000 tokens200,000 tokens (1M with extended thinking)
PriceOpen weights · free for individualsAnthropic API pricing
Pricing detailAlibaba's open-weights release — downloadable from Hugging Face, runnable locally or via Alibaba Cloud's free tier for individuals.Released alongside Mythos 5 on June 9, 2026 as the publicly-available member of the new Mythos class. Premium per-token pricing on the Anthropic API; available everywhere Opus 4.8 ships.
Release2026-062026-06-09
Bench coverage5/5 scored · avg 7.50/100/0 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 Claude Fable 5 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, mission-critical one-shot builds where you want anthropic's newest reasoning → Claude Fable 5. That's the same setup I run for the 3,600+ founders inside the AI Profit Boardroom.

FAQ — Qwen 3.7 vs Claude Fable 5

Which is better, Qwen 3.7 or Claude Fable 5?

On Goldie Bench, Qwen 3.7 averages no scored verdicts yet across the shared tasks, with 0 gold, 1 silver, 2 bronze overall. Claude Fable 5 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 Claude Fable 5?

Qwen 3.7: Alibaba's open-weights release — downloadable from Hugging Face, runnable locally or via Alibaba Cloud's free tier for individuals. Claude Fable 5: Released alongside Mythos 5 on June 9, 2026 as the publicly-available member of the new Mythos class. Premium per-token pricing on the Anthropic API; available everywhere Opus 4.8 ships.

What's the context window for Qwen 3.7 vs Claude Fable 5?

Qwen 3.7 has a 256,000 tokens context window. Claude Fable 5 has a 200,000 tokens (1M with extended thinking) context window.

When should I pick Qwen 3.7 over Claude Fable 5?

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 Claude Fable 5 over Qwen 3.7?

Pick Claude Fable 5 for: Mission-critical one-shot builds where you want Anthropic's newest reasoning; Long-context work using extended thinking up to 1M tokens; Plan-heavy multi-step tasks where intelligence in the plan matters more than the build. The trade-off is the weaknesses we logged on the bench: No goldiebench per-task scores yet — bench rank pending a published head-to-head guide; Premium pricing; Fusion premium panel reportedly out-scores it at half the API cost.

How does Goldie Bench score Qwen 3.7 vs Claude Fable 5?

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