GPT-5.6 Sol vs Opus 4.8

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 GPT-5.6 Sol and Opus 4.8, side by side, on 47 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.

GPT-5.6 Sol · Benched on GoldieBench as the flagship Sol at medium reasoning, one-shot, then headless-playtested. In the Agent OS it's the top tier of a routed stack — Sol on the hard calls, Terra for the bulk, Luna for the everyday 90%.

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.

Where GPT-5.6 Sol beat Opus 4.8

The tasks where I gave GPT-5.6 Sol a higher 0–10 score on the same prompt — with the actual commentary from my source guides.

Where Opus 4.8 beat GPT-5.6 Sol

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.

Strengths & weaknesses I logged

Pricing & context — the spec sheet

The verdict — which should you pick?

Across 47 scored shared tasks, GPT-5.6 Sol averaged 8.16/10, beating Opus 4.8's 7.51/10 by 0.65 points. Pick GPT-5.6 Sol 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 GPT-5.6 Sol and Opus 4.8 both into the Agent Operating System and dispatch each from the kanban by task type — the hardest reasoning and code where being right beats being cheap → GPT-5.6 Sol, mission-critical one-shot builds where 'has to work the first time' matters → Opus 4.8. That's the same setup I run for the 4,000+ founders inside the AI Profit Boardroom.

FAQ — GPT-5.6 Sol vs Opus 4.8

Which is better, GPT-5.6 Sol or Opus 4.8?

On Goldie Bench, GPT-5.6 Sol averages 8.16/10 across the shared tasks, with 11 gold, 11 silver, 7 bronze overall. Opus 4.8 averages 7.51/10, with 3 gold, 1 silver, 6 bronze. GPT-5.6 Sol wins the head-to-head 40–7.

How much does GPT-5.6 Sol cost vs Opus 4.8?

GPT-5.6 Sol: GPT-5.6 shipped as three models — Luna ($1/$6 per M), Terra ($2.50/$15) and Sol ($5/$30) — each with a same-price pro variant that ships a higher default reasoning effort. All share a ~1.05M-token context window and are rated High capability. Benched here on the flagship, Sol, at medium reasoning effort via OpenRouter. 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.

What's the context window for GPT-5.6 Sol vs Opus 4.8?

GPT-5.6 Sol has a 1,050,000 tokens context window. Opus 4.8 has a 200,000 tokens (1M with extended thinking) context window.

When should I pick GPT-5.6 Sol over Opus 4.8?

Pick GPT-5.6 Sol for: The hardest reasoning and code where being right beats being cheap; One-shot game/sim prototypes you want shippable on the first prompt; The flagship slot in a routed Agent OS — Sol for the hard 10%, Luna/Terra for the rest. The trade-off is the weaknesses we logged on the bench: Priciest tier on the bench at $30/M output — only worth routing the hardest 10% of work to Sol; Reasoning can eat the token budget on big open-world briefs (one 0-byte failure until the budget was raised, then it built clean).

When should I pick Opus 4.8 over GPT-5.6 Sol?

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.

How does Goldie Bench score GPT-5.6 Sol vs Opus 4.8?

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:

Full model pages: GPT-5.6 Sol · Opus 4.8 · back to the leaderboard