TL;DR

  • Both models were released September 22, 2026, so neither has a long independent track record yet.
  • Opus 5.5 lists at $4 per million input and $20 per million output. GPT-6 Sol lists at $2 and $10.
  • Both models bill cached reads at $0.20 per million tokens, and both support adjustable reasoning effort.
  • Neither vendor has published a direct Opus 5.5 versus Sol benchmark, so your own task suite is the tiebreaker.
  • Opus 5.5 leads with long-running agents and computer use. Sol leads with ecosystem breadth and the GPT-6 family ladder.

Anthropic and OpenAI released their new models on the same day. Claude Opus 5.5 and GPT-6 Sol both arrived September 22, 2026, which makes the comparison unusually direct and unusually hard at the same time: the specs and prices are public, but nobody outside the two vendors has had time to publish a settled, independent comparison.

For the individual release details, read Claude Opus 5.5 explained and GPT-6 Sol explained. Here is what the primary sources do settle, and where your own testing has to fill the gap.

The two models at a glance

Dimension Claude Opus 5.5 GPT-6 Sol
Released September 22, 2026 September 22, 2026
API model id claude-opus-5-5 gpt-6-sol
Input price, per 1M tokens $4 $2
Output price, per 1M tokens $20 $10
Cached read, per 1M tokens $0.20 $0.20
Context window 1M tokens 1.05M tokens
Reasoning control adaptive thinking with effort settings reasoning effort parameter
Tools and extras computer use, vision, memory across sessions function calling, web search, file search, computer use, vision
Availability Claude apps, Claude Code, API, AWS, Google Cloud, Microsoft Foundry ChatGPT Work, Codex, API

Sources for each column: Anthropic’s Opus 5.5 launch and Opus page, OpenAI’s Sol model docs and announcement.

How the prices differ

Sol is half of Opus 5.5 at list price on both input and output. A workload that sends 10K input tokens and generates 2K output tokens per task costs about $0.04 on Sol and $0.08 on Opus 5.5. At consumer-app volume, that difference is the difference between two feature budgets.

The gap narrows once caching does its work. Both vendors bill cached reads at $0.20 per million tokens, and both say caching is where long-running and repeated-context workloads save the most. Anthropic’s launch post is unusually explicit that cache reads make up the majority of agentic and coding costs. Anthropic also claims Opus 5.5 uses fewer tokens per task, which is the other half of its “40% cheaper than Opus 5” estimate; OpenAI makes no equivalent tokens-per-task claim for Sol. Those are different kinds of claims, and only your own logs can compare them.

Sol also has structural discounts Opus 5.5 does not match line for line: Batch and Flex processing at 50%, and a long-context step where prompts over 272K tokens bill input at double and output at 1.5x. Opus 5.5 has Fast mode at 2x for speed, and US-only inference at 1.1x. Model your real prompt sizes before assuming either list price is what you will pay.

How the positioning differs

The specs are close, so the sharper difference is what each vendor says the model is for.

Anthropic pitches Opus 5.5 as the model for long-running, lightly supervised agents: work that plans, uses memory across sessions, coordinates subagents, and reports back. The launch examples are big autonomous jobs, like a 680,000-line code migration an early tester ran in under a day, and Anthropic emphasizes computer use and document vision alongside coding.

OpenAI pitches Sol as the scale tier of a family. The announcement frames Sol and Luna as bringing Astra’s strengths to everyday work, with the GPT-6 ladder underneath for routing: Luna at $0.10 per million input for high-volume simple tasks, Sol for complex work, Astra at $10 and $50 for the hardest reasoning. If you want one vendor with a cheap lane and a premium lane, that ladder is the pitch.

Both models support adjustable reasoning effort, both support computer use, and both ship with upgraded safety claims. Anthropic says Opus 5.5 posts its best behavioral audit score to date and is more resistant to prompt injection than Opus 5. OpenAI says Sol and Luna build on Astra’s alignment work and improve on their GPT-5.6 counterparts.

What the benchmarks actually show

Here is the uncomfortable part. Anthropic’s launch benchmarks compare Opus 5.5 against GPT-6 Astra, not Sol, and Astra’s figures are “as reported by OpenAI,” with noted standard errors of a couple of points. OpenAI has not published a Sol versus Opus 5.5 comparison, and Anthropic’s own post warns that at these capability levels, benchmark margins have become a less reliable guide to real-world differences.

So the honest read of day-two evidence: no benchmark settles Opus 5.5 versus Sol. Anyone claiming a clean winner this week is reading more into vendor charts than the charts say. The reliable move is a small task suite from your own product, maybe twenty prompts that represent real traffic, run on both models at matched effort settings, scored by the people who own the feature.

Which should a team pick?

A few patterns hold up across both ecosystems:

  • If the workload is long-running, agentic, and benefits from memory across sessions, Opus 5.5’s positioning fits it best, at double the list price.
  • If the workload is high-volume and you want a routing ladder underneath, Sol fits, with Luna for the cheap lane and Astra above it.
  • If your stack already lives in one ecosystem, the migration cost usually beats the price difference; both models are a model-id change from their predecessors.
  • If the model powers a paid in-app feature, store billing rules apply either way, whichever model you pick. That boundary is covered in app monetization.

Inside each family, the harder calls are Opus 5.5 versus Sonnet 5 at half the price, and Sol versus Luna at a twentieth of the price. Those comparisons live in Opus 5.5 vs Sonnet 5 and GPT-6 Sol vs GPT-6 Luna.

Sources

FAQ

Is Claude Opus 5.5 better than GPT-6 Sol?
There is no independent benchmark yet that settles it. Anthropic's launch benchmarks compare Opus 5.5 against GPT-6 Astra, not Sol, and vendor-published scores use different harnesses and safeguards. Teams picking between them should run their own tasks on both.
Which is cheaper, Claude Opus 5.5 or GPT-6 Sol?
At list price, GPT-6 Sol is half of Opus 5.5: $2 versus $4 per million input tokens, and $10 versus $20 per million output tokens. Both bill cached reads at $0.20 per million.
Do Opus 5.5 and GPT-6 Sol have the same context window?
Close. Anthropic lists a 1M token context window for Opus 5.5. OpenAI lists 1.05M for GPT-6 Sol, with a 128K token maximum output.
Which should I use for coding?
Both are positioned for serious coding and agents. Opus 5.5 is pitched at long-running autonomous work in large codebases. Sol is pitched at complex coding and agentic workflows at scale. If the choice is close, price and ecosystem usually decide it.