GPT-6 Sol vs Claude Opus 5.5: Cost, Coding and Agent Workflows

GPT-6 Sol vs Claude Opus 5.5 at a glance

	GPT-6 Sol	Claude Opus 5.5
Positioning	Below GPT-6 Astra; complex coding and agent workflows	High-capability Opus tier; difficult coding and long-horizon delivery
Model ID	gpt-6-sol	claude-opus-5-5
Context window	About 1.05M tokens	1M tokens
Maximum output	128K tokens	128K tokens
Reasoning	none, low, medium, high, xhigh, max; medium by default	Adaptive reasoning; thinking is enabled by default
Knowledge cutoff	About April 20, 2026	June 2026
Official input / output price	$2 / $10 per 1M tokens	$4 / $20 per 1M tokens
APIMaster live route range	$2 / $10, 1 active route	$1.7901 / $8.9503 to $4 / $20, 2 active routes

The two models have similarly large context windows and the same maximum output size, but they expose reasoning differently. Sol gives developers an explicit effort ladder, which is useful when you want to trade quality for latency or cost on a per-request basis. Opus 5.5 uses adaptive reasoning, so the model controls more of that decision for you.

Which model is better for coding?

For everyday coding agents, GPT-6 Sol is the better starting point when the loop is large or repetitive. It is positioned below Astra rather than as a flagship, but it is designed for complex coding and agent work, with a lower official price than the top tier and selectable reasoning effort. That makes it suitable for repository exploration, multi-file edits, test repair and repeated tool calls where total cost per accepted change matters.

Claude Opus 5.5 is the stronger escalation choice for difficult delivery work. Third-party results currently put it ahead on several coding and agent-oriented evaluations, including Terminal-Bench 4.0 and GDPval-AA, although it does not lead every benchmark. The useful question is not whether it wins every chart; it is whether its higher success rate reduces retries, review time and incomplete deliverables on your repository.

Do not compare only output tokens. A model that emits 119K tokens on a hard task can cost more than one that emits 31K, even when the nominal input/output price looks close. Measure the cost of a completed change, including tool calls, retries, cache reads, review and rollback.

Price and cache economics

At official list price, Opus 5.5 is twice GPT-6 Sol on both input and output:

Cost per 1M tokens	GPT-6 Sol	Claude Opus 5.5
Input	$2	$4
Cached input / cache read	$0.20	$0.20
Cache write	$2.50	$5 for 5 minutes
Output	$10	$20

The equal $0.20 cache-read price is important for agents. On a long session, most context can be reused, so cache hits may matter more than the headline input price. Opus 5.5's lower live APIMaster route also changes the comparison: its current minimum is below official list price, while the GPT-6 Sol route currently matches the official reference. Check the GPT-6 Sol marketplace card and Opus 5.5 marketplace card before routing volume.

GPT-6 Sol also has provider-side pricing rules worth accounting for: input beyond roughly 272K tokens is charged at twice the input rate, output is charged at 1.5 times the normal rate, Batch and Flex are half price, and Fast mode is charged at twice the normal rate. Opus 5.5 has a Fast mode priced at $8 / $40 with up to about 2.5x the speed. These modes are not directly comparable; test the mode you will actually run.

Which model should you choose?

Your workload	Better first choice	Why
Many coding loops, batch edits and cost-sensitive agents	GPT-6 Sol	Lower official unit price and selectable reasoning effort
Complex repository change where rework is expensive	Claude Opus 5.5	Higher capability ceiling and strong agent/coding results
Long context with repeated tool results	Either, then compare cache hit cost	Both advertise $0.20 per 1M cached input tokens
Simple classification, extraction or routine transformation	GPT-6 Luna or another smaller model	Sol and Opus 5.5 may be unnecessary for the task
Hardest OpenAI-side reasoning tasks	GPT-6 Astra	Astra remains above Sol in the GPT-6 lineup

The best production pattern is often a tiered route: use Luna or GPT-6 Sol for the normal path, escalate failed or high-risk tasks to Opus 5.5, and reserve Astra for tasks that specifically need OpenAI's highest GPT-6 tier. Keep the model ID in configuration so you can change the policy without rewriting application code.

A fair benchmark for your team

Run both models on the same five to ten real tasks rather than relying on a single public score. Use the same repository snapshot, tools, system instructions, context files and acceptance tests. Record:

• percentage of tasks accepted without manual repair;
• wall-clock time including tool calls and retries;
• input, cache-read, cache-write and output tokens;
• cost per accepted task, not cost per request;
• review time and the number of false claims that work is complete.

Test at least one multi-file refactor, one bug with an incomplete reproduction, one tool-heavy feature, one long-context investigation and one task where the agent must report what it could not verify. This is more useful than treating a benchmark gap as a universal ranking.

FAQ

Is GPT-6 Sol cheaper than Claude Opus 5.5?

At official list price, yes: GPT-6 Sol is $2 / $10 per million input/output tokens, while Opus 5.5 is $4 / $20. APIMaster route prices can change independently. On the September 23 check, GPT-6 Sol had one active route at $2 / $10, while Opus 5.5 had two active routes and its lowest route was $1.7901 / $8.9503.

Which is better for coding agents?

GPT-6 Sol is the better cost-conscious default for repeated coding loops. Opus 5.5 is the better escalation choice when a difficult repository task benefits from a higher capability ceiling and fewer retries. Your own accepted-task rate matters more than a universal winner.

Is GPT-6 Sol the GPT-6 flagship?

No. GPT-6 Sol sits below GPT-6 Astra and is positioned for complex coding and agent workflows. Use Astra when the task needs the highest GPT-6 capability tier.

Do both models support long context?

Yes. GPT-6 Sol has about a 1.05M-token context window and Opus 5.5 has a 1M-token context window. Large context alone does not guarantee lower cost: cache hit rate, output length and retries determine the actual bill.

Can I call both models through APIMaster?

Yes. Use gpt-6-sol or claude-opus-5-5 as the model ID on the APIMaster endpoint. Check the live GPT-6 Sol card and live Opus 5.5 card for current route status and pricing.

Sources and verification date

• OpenAI GPT-6 Sol model and pricing materials — model positioning, context, reasoning levels and official price information, checked September 23, 2026.
• Anthropic Claude models overview and Claude API pricing — Opus 5.5 capabilities, context and pricing, checked September 23, 2026.
• Artificial Analysis — cross-model intelligence, cost and output observations; benchmark results vary by harness and reasoning setting.
• APIMaster live marketplace cards for gpt-6-sol and claude-opus-5-5, checked September 23, 2026. Route prices and availability can change after publication.

Start with the model that fits your workload

Use GPT-6 Sol when you need a capable coding and agent tier that can run repeatedly at a controlled cost. Use Claude Opus 5.5 when the task is difficult enough that a stronger first attempt can be cheaper than several retries. APIMaster lets you test both with one account and one API endpoint.

Create an APIMaster account, create a key in the console, point your client at https://apimaster.ai/v1, and set the model to gpt-6-sol or claude-opus-5-5.