Claude Opus 5.5 vs GPT-6 Sol and Luna: Which One Should You Actually Use?

TL;DR: On September 22, 2026, Anthropic cut Claude Opus prices by 20% with the Opus 5.5 release, and OpenAI answered within the same news cycle with two new, much cheaper GPT-6 variants — Sol (for coding and agent work) and Luna (for high-volume routine tasks). None of these three is a strict upgrade over the others: Opus 5.5 leads on raw reasoning benchmarks and real-world test comparisons, while GPT-6 Sol and Luna win decisively on price-per-task. Which one is “better” depends entirely on whether you’re optimizing for quality or cost.

A Quick Note on How This Was Put Together

This is a research roundup, not a hands-on benchmark we ran ourselves. The pricing and benchmark figures below come from Anthropic’s and OpenAI’s own release pages plus Artificial Analysis’ independent tracking, and the real-world usage impressions come from Tom’s Guide’s own side-by-side prompt testing (credited below). If you’re choosing a model for a specific workload, running your own actual task through both is still the only way to know for sure — benchmarks and a handful of test prompts only get you so far.

What Actually Shipped This Week

Three separate models, from two companies, in the same short window:

• Claude Opus 5.5 (Anthropic, September 22) — a price cut and performance bump on the existing Opus line. Not a new model family, an upgraded version of Opus.
• GPT-6 Sol (OpenAI) — a cheaper, faster GPT-6 variant tuned for recurring coding and agent work, built on the same training foundation as OpenAI’s flagship GPT-6 Astra model.
• GPT-6 Luna (OpenAI) — an even cheaper variant, roughly ten times less expensive than Sol, aimed at high-volume routine tasks like summarization and data extraction rather than complex reasoning.

Worth clearing up since it trips people up in search: Sol and Luna are not OpenAI’s flagship model. That’s GPT-6 Astra, which shipped earlier in September. Sol and Luna are cost-optimized siblings built on Astra’s foundation — think of them as the “how do I run this at scale without the flagship price tag” answer, not the top-of-the-line option. We break down what Astra itself actually does — including a new safety threshold it triggered internally — in our separate GPT-6 Astra explainer.

Pricing, Side by Side

Model	Input (per 1M tokens)	Output (per 1M tokens)	Best for
Claude Opus 5.5	$4.00	$20.00	Complex reasoning, long-context work, agentic coding
GPT-6 Sol	$2.00	$10.00	Recurring coding and agent tasks at lower cost
GPT-6 Luna	$0.10	$0.50	High-volume summarization and extraction

Anthropic also cut cached-input pricing by 60% (to $0.20/1M tokens) with Opus 5.5, and OpenAI discounted cached reads by 90% for the new GPT-6 variants — both companies are clearly optimizing for the reality that most production usage today is repeated, cached-context calls rather than one-off prompts.

Benchmarks, With the Usual Caveat

Independent tracking from Artificial Analysis puts Claude Opus 5.5 (max effort) at an Intelligence Index score of 58, against GPT-6 Sol’s max configuration at 48 — a meaningful gap in raw capability. Anthropic’s own release figures show similar jumps on agentic-coding-specific tests: Opus 5.5 scored 66.4% on Terminal-Bench 4.0 versus Opus 5’s 55.8%, and 40% on AutomationBench versus 26.9% for the previous version.

On the OpenAI side, Sol scored 33.2% on AutomationBench and 68.8% on DeepSWE v1.1, with OpenAI stating it makes “about half as many mistakes” as its GPT-5.6 predecessor on comparable tasks.

The catch with any of these numbers: they’re benchmark scores, not your actual workload. Artificial Analysis also tracks cost-per-task, and there GPT-6 Sol comes out clearly ahead — about $0.13 per task on their reference measure, against roughly $0.55 for a comparable Claude Opus 5.5 configuration. So the honest read is: Opus 5.5 is the stronger model on paper, and GPT-6 Sol is the cheaper one to actually run at volume.

What a Real Side-by-Side Test Found

Tom’s Guide ran both models through five everyday, non-technical prompts — birthday party planning, rewriting an announcement, a vacation rental decision, a municipal budget question, and a personal organization system — and Claude won four out of five. The pattern they noticed: Claude tended to keep digging into second-order considerations (safety details, hidden costs, behavioral nuance) that ChatGPT’s response skipped, which mostly worked in Claude’s favor except for one case — an “exhausted parent” organization prompt — where ChatGPT’s more streamlined answer was judged the better fit specifically because it didn’t add extra cognitive load.

That’s a small, informal test, not a scientific benchmark, but it lines up with the general shape of the numbers above: Claude Opus 5.5 tends to go deeper, GPT-6’s variants tend to be faster and lighter.

So Which Should You Actually Use?

If you’re doing complex reasoning, long documents, or agentic coding where quality matters more than per-call cost — Claude Opus 5.5 is the stronger pick based on both the benchmarks and the hands-on test above.

If you’re running the same kind of task thousands of times a day — coding agents, support triage, batch summarization — GPT-6 Sol or Luna’s cost-per-task advantage adds up fast, and the quality gap matters less when the task itself is narrow and repetitive.

If you’re not sure which category you’re in — start with whichever model you already have access to and only switch if you’re hitting a specific cost or quality wall. None of these releases make an existing setup broken overnight; they mostly shift the cost-quality tradeoff at the margins.

FAQ

Is GPT-6 Sol the same as GPT-6 Astra? No. Astra is OpenAI’s flagship GPT-6 model. Sol and Luna are cheaper variants built on Astra’s training foundation, aimed at cost-sensitive, high-volume use rather than maximum capability.

Did Claude Opus 5.5 replace Claude Opus 5? Yes, in the sense that it’s the new default in that tier — same product line, updated version, lower price, better agentic-coding scores.

Is any of this a reason to switch providers if I’m already happy with what I use? Probably not on its own. The gaps here are meaningful at scale (cost-per-task, benchmark deltas) but most individual users won’t feel a dramatic difference in day-to-day use unless they’re running high-volume automated workloads.

Final Thoughts

This is really a story about pricing pressure, not a single clear winner. Anthropic pushed Opus 5.5’s capability up while cutting price, and OpenAI responded not by matching Opus on quality but by undercutting it hard on cost with Sol and Luna. If you’re picking based on raw output quality, the current data points toward Claude. If you’re picking based on running things at scale affordably, GPT-6’s new variants make a real case. Worth revisiting this comparison again once OpenAI’s flagship Astra and Anthropic’s next release cycle actually go head-to-head on the same footing.

Sources: SiliconANGLE, Artificial Analysis, Tom’s Guide