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

Opus 4.8 vs Fusion

The reasoning king — deepest thinking, premium price. vs Multi-model panel — Fable 5 + GPT-5.5, ensembled. Beats Fable 5 at half the price.

Opus 4.8 · context200K tokens
Fusion · contextVaries (per-panel)
Opus 4.8 · price$15 / $75 per M tokens
Fusion · priceOpenRouter Fusion API pricing
Opus 4.8 · vendorAnthropic
Fusion · vendorOpenRouter

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 Fusion, 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.

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.

Fusion · Dispatched from Agent OS for research-heavy prompts where ensemble accuracy outweighs single-model speed.

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
Fusion
Game
🥇Opus 4.8 on Arcade
— not attempted —
Game
🥇Opus 4.8 on Doom
— not attempted —
Game
🥈Opus 4.8 on Neoncity
— not attempted —
Game
🥇Opus 4.8 on Outrun
— not attempted —
Game
🥈Opus 4.8 on Raycaster
— not attempted —
Page
🥇Opus 4.8 on Landing
— not attempted —
Sim
🥇Opus 4.8 on Blackhole
— not attempted —
Sim
Opus 4.8 on Cloth
— not attempted —
Sim
🥉Opus 4.8 on Fluid
— not attempted —
Sim
🥈Opus 4.8 on Fractal
— not attempted —
Sim
🥇Opus 4.8 on Galaxy
— not attempted —
Sim
🥇Opus 4.8 on Orbit
— not attempted —
Opus 4.8 on Pathtracer
— not attempted —
Opus 4.8 on Reactiondiff
— not attempted —
Sim
🥇Opus 4.8 on Solar
— not attempted —
Visual
Opus 4.8 on Terrain
— not attempted —
Visual
🥈Opus 4.8 on Voxel
— not attempted —

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

Fusion

Strengths

  • Premium Fusion panel scored 69.0% on DRACO deep-research benchmark — beats solo Fable 5 by +3.7 points
  • Budget panel ties Fable 5 at ~64.7% for roughly half the cost
  • Vendor-agnostic — model panel can swap as new frontier releases land

Trade-offs

  • Ensemble latency higher than any single model (panel calls run in parallel but the slowest still gates the response)
  • No per-task goldiebench scoring yet — bench rank pending

Pricing & context — the spec sheet

Spec Opus 4.8 Fusion
VendorAnthropicOpenRouter
Context window200,000 tokens (1M with extended thinking)Varies — depends on which panel models are dispatched
Price$15 / $75 per M tokensOpenRouter Fusion API pricing
Pricing detailPremium pricing via the Anthropic API: $15 per million input tokens, $75 per million output tokens. Extended thinking is included but adds latency.OpenRouter's Fusion API dispatches a single prompt to multiple frontier models and ensembles the answers. Premium panel: Fable 5 + GPT-5.5. Budget panel: cheaper open-weights models. Roughly half the per-token cost of a Fable 5 solo call.
Release2026-052026-06-14
Bench coverage13/17 scored · avg 8.46/100/0 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 Opus 4.8 and Fusion 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, deep-research workflows where panel consensus beats single-model answers → Fusion. That's the same setup I run for the 3,600+ founders inside the AI Profit Boardroom.

FAQ — Opus 4.8 vs Fusion

Which is better, Opus 4.8 or Fusion?

On Goldie Bench, Opus 4.8 averages no scored verdicts yet across the shared tasks, with 8 gold, 4 silver, 1 bronze overall. Fusion 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 Opus 4.8 cost vs Fusion?

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. Fusion: OpenRouter's Fusion API dispatches a single prompt to multiple frontier models and ensembles the answers. Premium panel: Fable 5 + GPT-5.5. Budget panel: cheaper open-weights models. Roughly half the per-token cost of a Fable 5 solo call.

What's the context window for Opus 4.8 vs Fusion?

Opus 4.8 has a 200,000 tokens (1M with extended thinking) context window. Fusion has a Varies — depends on which panel models are dispatched context window.

When should I pick Opus 4.8 over Fusion?

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 Fusion over Opus 4.8?

Pick Fusion for: Deep-research workflows where panel consensus beats single-model answers; Cost-sensitive operators who want Fable-5-class output at ~half the bill; Production agents that benefit from vendor-redundancy on every call. The trade-off is the weaknesses we logged on the bench: Ensemble latency higher than any single model (panel calls run in parallel but the slowest still gates the response); No per-task goldiebench scoring yet — bench rank pending.

How does Goldie Bench score Opus 4.8 vs Fusion?

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