Opus 5.5 vs GPT-6 Sol: Which Model Wins Real Tasks?
A hands-on test of Opus 5.5 vs GPT-6 Sol across websites, video edits, and dashboards, comparing quality, speed, and cost per task.
What happened when Opus 5.5 and GPT-6 Sol were tested on the same tasks?
In a head-to-head test covering website design, video editing, sizzle reels, and dashboard generation, Opus 5.5 consistently produced more polished, higher-energy outputs than GPT-6 Sol, but at roughly double to quadruple the cost. Both models handled the underlying tasks competently, meaning neither failed outright, but Opus 5.5’s outputs tended to look more premium: cleaner animations, better pacing in video edits, and more convincing product design. GPT-6 Sol was faster and cheaper in most cases, making it the better choice when budget or speed matters more than polish.
TL;DR
- Opus 5.5 costs roughly double GPT-6 Sol on paper (around $4 input / $20 output vs $2 input / $10 output per million tokens), but the test measured output quality per dollar rather than sticker price alone.
- Opus 5.5 won every creative use case tested, including website design, a sizzle reel built from raw event footage, and an Instagram reel edit, often by a noticeable margin in polish and energy.
- GPT-6 Sol got a factual retrieval task wrong, misidentifying the date of a specific mention in a transcript archive, while Opus 5.5 found the correct answer.
- Cost gaps varied by task: on a website build, Opus cost about three times more; on an Instagram reel, about four times more; on a sizzle reel, only about double.
- Both models can run tasks in parallel inside their respective coding harnesses (Claude Code style workflows on one side, a Codex-style app on the other), and both can build multi-part outputs like dashboards, decks, and landing pages from the same dataset.
- Speed differences were inconsistent: Opus was slower on some tasks and faster on others, so raw processing time alone doesn’t predict which model finishes first.
- The dashboard and analytics test showed both models can chain outputs (spreadsheet, pitch deck, dashboard, landing page) from a single data dump, though the polish and branding consistency varied.
Plans first. Then code.
Remy writes the spec, manages the build, and ships the app.
How do Opus 5.5 and GPT-6 Sol compare on pricing?
Opus 5.5 is priced at roughly $4 per million input tokens and $20 per million output tokens. GPT-6 Sol comes in at about $2 input and $10 output, making it half the price of Opus on a per-token basis. That pricing gap shapes expectations before any test even runs: a more expensive model naturally invites a “better output” assumption, but the more useful framing is value per dollar spent. If you handed each model $100 worth of compute, which one gives you a better result for that spend?
In practice, across the tasks tested, Opus 5.5 did cost meaningfully more per task, sometimes three to four times as much as GPT-6 Sol. But it also delivered outputs that were, in several cases, clearly higher quality. Whether that quality gap justifies the price gap depends on the use case. For quick iteration or cheap first drafts, GPT-6 Sol’s lower cost adds up fast if you’re running many passes. For polished, ship-ready creative output, Opus 5.5 earned its premium in this test.
Which model performed better on website design?
Given an identical prompt to build a scroll-driven, layered product landing page, Opus 5.5 produced the stronger result. Its version included smoother scroll animations, more convincing product imagery, layered background elements that shifted with mouse movement, and text animations that felt deliberate rather than generic. GPT-6 Sol’s version followed the same narrative and branding cues but used less realistic product visuals (illustrated packaging instead of photo-style images) and felt noticeably less premium overall.
Both builds took about 35 minutes to complete, with Opus running slightly longer. On cost, Opus came in around $18.32 versus about $5.89 for GPT-6 Sol, roughly three times more expensive. Despite that gap, Opus 5.5 was the clear quality winner on this task.
How did the two models handle video editing tasks?
Two separate video tasks were tested: a 30-second sizzle reel built from about 100 gigabytes of raw event footage, and an Instagram reel edit using the same source clips and prompt for both models.
On the sizzle reel, Opus 5.5 produced a genuinely high-energy result with good music, sound effect placement, and pacing that made the footage feel exciting. GPT-6 Sol’s version covered similar ground but lacked the same intensity and felt less suited to marketing use. Opus finished about 5 minutes faster on this task and cost roughly double, making it the stronger pick on both speed and quality here.
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Scoping, trade-offs, edge cases — the real work. Before a line of code.
The Instagram reel test showed a bigger gap. GPT-6 Sol’s edit had a distracting background hum, minimal animation, and no music, resulting in a flat, unpolished feel. Opus 5.5’s version used typing animations and sound effects that made the same script feel considerably more engaging. This came at a real cost: Opus took about twice as long and roughly four times the price (around $11 versus about $3 for GPT-6 Sol). That price gap raises a fair question: if you spent that extra budget running GPT-6 Sol through multiple iterations, would it eventually match Opus’s one-shot quality? The test didn’t answer that directly, but it’s the kind of tradeoff worth considering when picking a model for high-volume creative work.
Did either model make factual mistakes?
Yes. In a task asking both models to search through hours of call transcripts to find a specific past mention of a technical term, GPT-6 Sol returned an incorrect date. Opus 5.5 found the correct one. The error likely stemmed from inconsistent spelling of the term across transcripts, but it’s still a meaningful miss for a task that’s fundamentally about accurate retrieval rather than creative judgment. Both models finished in roughly the same amount of time, but only one got the factual answer right, which matters more for research or knowledge-base tasks than for creative generation.
How did the models perform on dashboards and data-heavy tasks?
Given a large dataset and asked to build a Google Sheet analytics view plus a dashboard, Opus 5.5 produced a clean, branded output with multiple sections (P&L, SaaS metrics, cash and runway, unit economics, pipeline, and more), consistent color theming, and working data visualizations. It also generated a pitch deck and a functional analytics dashboard with tab switching and highlighting effects, plus a scroll-animated landing page pulling from the same underlying data. The overall output looked cohesive across formats, even if individual pieces had a recognizably AI-generated feel.
This test highlighted a broader point: both models can now chain multiple deliverables (spreadsheet, deck, dashboard, webpage) from a single data source rather than requiring separate prompts for each. The consistency of branding and data across those outputs is where quality differences tend to show up most.
Is Opus 5.5 worth the higher price over GPT-6 Sol?
Based on this set of tests, Opus 5.5 delivered better creative output in every comparison, often by a clear margin, but at a real cost premium ranging from roughly double to four times the price of GPT-6 Sol depending on the task. For workflows where output polish directly affects results, marketing videos, landing pages, client-facing decks, that premium looks justified. For high-volume, lower-stakes tasks, or workflows where you plan to iterate heavily anyway, GPT-6 Sol’s lower per-task cost may deliver better value over many runs, especially since it wasn’t dramatically slower in most cases. The right choice depends on whether you’re optimizing for one-shot quality or cost-efficient iteration.
Frequently Asked Questions
What is GPT-6 Sol?
GPT-6 Sol is the OpenAI model compared against Anthropic’s Opus 5.5 in this test, priced at roughly $2 per million input tokens and $10 per million output tokens, about half the cost of Opus 5.5.
Is Opus 5.5 better than GPT-6 Sol?
In this round of tests covering websites, video edits, and reels, Opus 5.5 produced higher-quality, more polished outputs in every case tested, but consistently cost more, sometimes triple or quadruple the price of GPT-6 Sol for a given task.
How much more expensive is Opus 5.5 than GPT-6 Sol?
Everyone else built a construction worker.
We built the contractor.
One file at a time.
UI, API, database, deploy.
On a per-token basis, Opus 5.5 runs about double the price of GPT-6 Sol. In actual task costs during testing, the gap ranged from roughly double to about four times more expensive, depending on the complexity of the task.
Did GPT-6 Sol make any errors during testing?
Yes. On a task requiring it to search transcripts for a specific past mention, GPT-6 Sol returned an incorrect date, while Opus 5.5 found the correct one.
Which model is better for high-volume or budget-conscious work?
GPT-6 Sol’s lower per-task cost makes it more attractive for high-volume workflows or situations where heavy iteration is expected, since its cheaper price per run adds up to more room for retries within the same budget.