Claude Opus 5.5 vs GPT-6 Sol: Which One Should Run Your Agents?

Updated 27 October 2026. OpenAI released GPT-6.1 Terra on 26 October: $1 input / $0.05 cached input / $5 output per million tokens, with a 1,050,000-token context window and 128,000 maximum output. It is a fast option for visual reasoning, computer use and natural writing. GPT-6.1 Sol and GPT-6 Astra remain better candidates for the most complex coding; Terra does not replace every frontier model. See OpenAI’s GPT-6.1 Terra announcement and our current ChatGPT model guide.

Scope: the named model comparison below retains its original models and dated examples. Terra is an additional current option, not a silent replacement for either model.

Published 23 September 2026 by Jake Hissitt, Founder of Stob.AI.

Two frontier models launched on the same day, 22 September 2026, and between them they now cover most serious production work. Claude Opus 5.5 at $4 in / $20 out, and GPT-6 Sol at $2 in / $10 out.

They are close enough on capability that price, caching and failure behaviour decide the answer for most teams. Here is the comparison we use when choosing for client systems.

The specification table

	Claude Opus 5.5	GPT-6 Sol
Input price / 1M	$4.00	$2.00
Output price / 1M	$20.00	$10.00
Cached input / 1M	$0.20	$0.20
Cache write / 1M	$5.00 (5-minute)	$2.50
Context window	1,000,000	1,050,000
Max output	128,000	128,000
Long-prompt penalty	None published	Above 272K input: $4 in / $15 out
Reasoning control	Adaptive thinking, always on, medium default	Reasoning effort none to max
Knowledge cutoff	June 2026	2026
Batch discount	50%	Available

Where each one wins

GPT-6 Sol wins on price at the same capability tier

Sol is half the price of Opus 5.5 on both input and output while scoring at the same level on our reasoning and coding measures. It also gives you an explicit reasoning-effort dial from none to max, which matters when a workflow has a mix of trivial and hard steps and you do not want to pay for deliberation on the trivial ones.

That dial is the single most useful cost lever in the GPT-6 line. Set effort low for classification and routing, high for the two or three steps that genuinely need it.

Claude Opus 5.5 wins on long-running agent discipline

Opus 5.5 has adaptive thinking always on, with the model deciding how much deliberation a step deserves rather than waiting for you to set it. In long agent runs that tends to produce fewer wasted turns and fewer confidently wrong tool calls.

It is also the model with the better-documented agent tooling around it, and the one we reach for when an agent runs unattended against real business systems for hours rather than seconds.

Prompt length changes the maths

Sol carries a long-prompt penalty: prompts above 272,000 input tokens bill at $4 in / $15 out for the whole request. At that point Sol costs the same on input as Opus 5.5 and less on output, so it stays competitive, but the headline half-price advantage disappears. If your workflow regularly loads enormous documents, price both at the size you actually send, not the size in the pricing page headline.

A worked monthly cost

Take a realistic agent workload: 40 million input tokens and 8 million output tokens a month, with 60% of input served from cache.

Model	Cached input	Fresh input	Output	Monthly total
GPT-6 Sol	$4.80	$32.00	$80.00	$116.80
Claude Opus 5.5	$4.80	$64.00	$160.00	$228.80

Cache reads cost the same on both, so heavy caching narrows the gap. The difference is real but small in absolute terms at this volume. It becomes decisive at ten times the volume, and irrelevant if one model needs noticeably more retries.

The number that decides it is cost per accepted outcome, not cost per token. A model that is half the price and needs three attempts is more expensive.

How to choose

Choose GPT-6 Sol for most new builds: coding agents, research pipelines, document generation and mixed-difficulty workflows where the reasoning-effort dial lets you tune spend step by step.
Choose Claude Opus 5.5 for long unattended agent runs against production systems, and for professional knowledge work where the writing quality and judgement are the product.
Choose GPT-6 Astra only when both fall short on the hardest reasoning, and you have tested that the $10 / $50 tier actually clears the bar the others missed. See our GPT-6 tier guide.
Run both if you can. Routing by task type is normal engineering, not fence-sitting.

Frequently asked questions

Is GPT-6 Sol better than Claude Opus 5.5?

They score level on reasoning and coding in our comparison. Sol is half the price and gives you a reasoning-effort control; Opus 5.5 tends to be steadier in long unattended agent runs. Neither is universally better.

How much does each model cost?

GPT-6 Sol is $2 per million input tokens and $10 per million output tokens. Claude Opus 5.5 is $4 and $20. Cached input is $0.20 per million on both.

Which has the larger context window?

GPT-6 Sol at 1,050,000 tokens against 1,000,000 for Opus 5.5. Both produce up to 128,000 output tokens. The difference is not meaningful in practice.

Does GPT-6 Sol have a long-prompt surcharge?

Yes. Prompts above 272,000 input tokens bill at $4 in / $15 out for the entire request.

Which should I use for coding agents?

Start with GPT-6 Sol at high reasoning effort. Move to Opus 5.5 if your agent runs for long unattended stretches and you see fewer recoverable errors from it on your own task set.

What to do next

Build a set of twenty representative tasks with known good outcomes, run both models, and compare completed work and total cost. Then compare all 230+ models side by side in our AI model comparison tool, or price high-volume options in the cheapest production models guide.