Claude Opus 5.5 vs GPT-6 Sol: Price, Benchmarks, Verdict

Claude Opus 5.5 vs GPT-6 Sol is the first real test of a new pricing era. Anthropic and OpenAI shipped both models on the same day, September 22, 2026, within hours of each other, and pitched them the same way: more capability for less money.

For anyone building an app on top of these models, the choice comes down to a clean trade. Opus 5.5 is the stronger model. Sol is the cheaper one.

This guide shows you where that line falls, using each vendor's own numbers and the one independent test that ran both. The goal is to help you pick the right model for what you are building, not for what a launch chart wants you to believe.

GPT-6 Sol wins price, Claude Opus 5.5 wins capability

Start with the one number nobody disputes. Sol costs half of Opus 5.5 on standard input and output tokens. If your workload is large and your task is well-defined, that gap compounds into a real budget difference.

Capability runs the other way. On the independent Opus 5.5 benchmarks, the model takes the top spot on the Artificial Analysis Intelligence Index at 58, the highest score measured there as of September 2026. Sol reaches 48 at the same maximum-effort setting.

So the headline is a two-line summary. Opus 5.5 does harder work. Sol does cheaper work. Everything below is about finding the exact point where one crosses the other.

What each model is, and what it is not

Version numbers matter more here than in any recent launch, and getting them wrong will scramble your comparison.

1. Claude Opus 5.5 is Anthropic's new flagship

Claude Opus 5.5 is Anthropic's newest flagship and the first model in a new Claude 5.5 family. It is built for long-running coding agents and knowledge work, and Anthropic positions it as reaching Fable 5.1-level quality at a lower running cost than Opus 5. It is a genuine successor to Opus 5, not a rename, and Anthropic reports higher scores at a lower cost per task.

2. GPT-6 Sol is OpenAI's mid-tier model

GPT-6 Sol is the mid-tier of OpenAI's GPT-6 family. It sits below the flagship GPT-6 Astra and above OpenAI's budget Luna tier. For the per-test detail, the GPT-6 Sol benchmarks break every score down by effort level. The cheaper GPT-6 Luna benchmarks sit one tier below. Sol is also not the same model as GPT-5.6 Sol, which is still live on promotional pricing and shares the Sol name. When someone quotes a "Sol" score, check the generation first.

3. The vendors never tested the two against each other

That gap is the whole reason vendor charts look confusing. OpenAI benchmarked Sol against Opus 5. Anthropic benchmarked Opus 5.5 against Astra and the earlier Sol. Neither ran the two launch-day models against each other.

How the benchmarks compare, by source

Here is the part every comparison gets loose about. Almost every number in circulation comes from one of the two labs, measured on its own setup. Read each figure for the harness it was run on.

Only one source ran both new models on the same test. Artificial Analysis puts both through its Intelligence Index, and that is the closest thing to a neutral head-to-head that exists today.

Table 1 - Artificial Analysis figures, both models on one harness. The Intelligence Index and cost rows use matched maximum effort; the Terminal-Bench and speed rows show each model near its top effort, not an identical setting. As of September 2026.

The independent read is consistent. On Artificial Analysis's Intelligence Index, Opus 5.5 scores higher at the efforts tested. In the configurations Artificial Analysis reports, Sol shows higher medium-effort throughput and a lower cost per task. Neither result cancels the other out.

The vendor-reported numbers sit in a separate bucket, because the labs did not share a harness.

Table 2 - Vendor-reported headline scores. Anthropic compared Opus 5.5 with Astra and the earlier Sol; OpenAI compared Sol with Opus 5 and Fable 5. Treat cross-model gaps as directional. As of September 2026.

Two footnotes are worth carrying into any decision. Anthropic's own 66.4% on Terminal-Bench is its best-run xhigh score, and Artificial Analysis measured the same test lower, at 59.6%, on its own harness. On OpenAI's side, Sol's best scores on some tests like DeepSWE and OSWorld sit a touch below the earlier Sol's, so on those the upgrade OpenAI is selling is cost per task, not a new high score.

Pricing compared, including the fine print

1. Sol costs half of Opus 5.5 on standard tokens

The sticker price is the easy half. Opus 5.5 costs twice Sol on standard input and output. Cache reads cost the same on both.

Table 3 - Standard short-context API pricing. Anthropic lists two Opus 5.5 cache-write durations; Sol uses a single rate. Source: Anthropic and OpenAI, as of September 2026.

2. The long-context surcharge narrows Sol's price edge

The fine print is where the simple two-to-one story breaks. Sol adds a long-context surcharge: any request over 272,000 input tokens is billed at twice the input and cache rates and 1.5 times the output rate, across the whole call. Opus 5.5 charges one flat rate across its full 1 million-token window.

Above that threshold, Sol's fresh-input rate rises to match Opus 5.5's, so the headline input discount goes away. Two lines cut the other way. Sol still lists a lower output rate in long context, $15 versus Opus 5.5's $20 per 1 million tokens, while Opus is cheaper on cached reads, $0.20 versus Sol's $0.40 in that tier. Neither model is uniformly cheaper once long-context billing applies. For short, high-volume calls, Sol's advantage holds in full.

3. Caching and prompt length decide the real bill

One more line that rarely makes the headline. Cache reads cost $0.20 on both models, and for agent workloads that reuse a long prefix, they can be a large share of the bill. Where caching dominates, the two models are closer than the base rates suggest.

Put real volume through it and the base gap is exactly what you would expect. At standard short-context rates, a coding agent that burns 5 million input and 1 million output tokens a day costs about $40 on Opus 5.5 and about $20 on Sol before caching. Caching then pulls the two closer, because cache reads bill at the same $0.20 on both, so the more of that input you reuse, the smaller the difference gets.

Long prompts tell a more mixed story. A single 300,000-token request to Sol crosses the 272,000-token line, so its input bills at about $4 per 1 million instead of $2, near $1.20 for that input, the same as Opus 5.5 at its flat rate. Above the threshold Sol loses its input discount but keeps a lower output rate, while Opus bills cached reads at half Sol's long-context rate. Which one is cheaper then depends on the output and cache mix, not prompt length alone.

The effort dial changes the math

The single most useful idea in this comparison is that neither model has one price or one score. Both expose an effort setting, and it moves both numbers at once.

Opus 5.5 runs adaptive thinking and defaults to medium. Sol offers settings from none up to max. Turn the dial up and quality rises with cost; turn it down and you trade accuracy for savings.

This reframes the whole question. Opus 5.5 at its default medium setting can beat Sol at max, for a modestly higher cost per task. Sol is the cheaper route to any score up to about the mid-40s on the independent index. Above that it runs out of headroom, and within this matchup Opus 5.5 is the only one of the two that climbs higher.

The practical takeaway is to compare each model at the setting you would actually ship, not at the setting that produces the prettiest chart.

Which model to pick for which job

Match the model to the shape of the work, not to the leaderboard.

The honest caveat sits under all four rows. No neutral lab has run both models through the same setup on your kind of task, so the safest move is to run a handful of your real jobs through both and measure cost per finished result, not cost per token.

Build with either model on Emergent

If you are describing an app rather than writing the code for it, the model debate becomes a setting you can change. Emergent lets you build a full-stack app from a plain description, and the model running underneath is your choice.

Claude Opus 5.5 is live on Emergent from launch day, and the GPT and Gemini families are supported through the same account. Access runs through one Universal LLM Key, so you get single-credential access and unified billing rather than a separate key and invoice per provider.

Start Building on Emergent and pick the model that fits what you are making.