GPT-6 Sol is the lower-cost choice for most API workloads; Claude Opus 5.5 is the stronger candidate when your evaluation rewards Anthropic’s always-on adaptive reasoning, cross-cloud availability or migration from an existing Claude agent. Neither is universally better.

Start with Sol for price-sensitive coding and agent loops. Evaluate Opus 5.5 for difficult long-running agents, Claude-native integrations and workloads where fewer steps may offset its higher token price. Anthropic reports better token efficiency than Opus 5, but that is a vendor claim and not a direct benchmark against Sol.

This comparison uses official OpenAI, Anthropic and GitHub documentation checked on September 23, 2026. We have not independently benchmarked either model.

GPT-6 Sol vs Claude Opus 5.5 at a glance

Decision	GPT-6 Sol	Claude Opus 5.5
Model ID	gpt-6-sol	claude-opus-5-5
Standard input/output	$2 / $10 per MTok	$4 / $20 per MTok
Cache read	$0.20 per MTok	$0.20 per MTok
Cache write	$2.50 per MTok	$5 for 5m; $8 for 1h
Batch input/output	$1 / $5	$2 / $10
Context	1.05M	1M
Maximum output	128K	128K; 300K Batch beta with header
Reasoning	none through max; medium default	Adaptive, always on; medium default
Low-latency option	Fast at $4 / $20	Fast at $8 / $40, research preview
Direct platforms	OpenAI API	Claude API, AWS, Google Cloud, Microsoft Foundry
Copilot plans	Pro+, Max, Business, Enterprise	Pro+, Max, Business, Enterprise

The immediate price answer

At standard rates, Sol costs half as much for uncached input and output. For a job with one million input tokens and 100,000 output tokens:

• GPT-6 Sol: $2 input + $1 output = $3
• Claude Opus 5.5: $4 input + $2 output = $6

That is a pricing example, not a performance comparison. If Opus completes a difficult task with fewer retries, tool calls or review cycles, the successful-task gap may be smaller. If both models complete it equally well, Sol has the clear token-cost advantage.

Batch preserves the same 2:1 relationship for ordinary context: $1/$5 for Sol and $2/$10 for Opus 5.5. Sol also offers Flex at Batch-level token prices, while Anthropic’s Batch API is an asynchronous request path rather than an equivalent Flex service.

Prompt caching changes the comparison

Both models charge $0.20 per million cache-read tokens. That produces an unusual result: once a long reusable prompt is cached, neither has a read-price advantage.

For one million cached input tokens plus 100,000 output tokens:

• Sol: $0.20 cache read + $1 output = $1.20
• Opus 5.5: $0.20 cache read + $2 output = $2.20

Sol remains cheaper because of output pricing. The initial cache write is also cheaper: $2.50/M for Sol versus $5/M for an Anthropic five-minute cache or $8/M for a one-hour cache. Opus’s one-hour cache can still be useful when an agent reuses a large repository or document set across a longer session.

Do not compare cache-read rates without modelling writes, expiry and actual hit rate. A workflow that continually changes the prompt prefix may pay write prices without receiving the expected read savings.

Long context: similar capacity, different economics

Both accept roughly one million tokens and normally return up to 128K. Sol’s window is 1.05M; Opus 5.5’s is 1M. That small capacity difference is less important than pricing behavior.

OpenAI applies long-context pricing to the entire Sol request once input exceeds 272,000 tokens: $4/M input, $0.40/M cached input, $5/M cache writes and $15/M output. Anthropic’s Opus 5.5 specification does not document an equivalent context threshold surcharge.

For 500,000 uncached input tokens and 50,000 output tokens:

• Sol above its threshold: $2 input + $0.75 output = $2.75
• Opus 5.5: $2 input + $1 output = $3.00

Sol is still slightly cheaper in that example, but its ordinary 2:1 advantage nearly disappears. Long-context teams should price their real prompt sizes rather than extrapolating the headline rates.

Opus 5.5 additionally documents a 300K maximum output in the Message Batches API beta when the required beta header is used. That is a specialized asynchronous path, not its ordinary response limit.

Reasoning and agent behavior

Sol exposes reasoning effort from none to max, with medium as default. The option to use none matters for deterministic or latency-sensitive work. On Chat Completions, function calling is supported only with reasoning set to none; use the Responses API for the fuller agent tool path.

Opus 5.5 uses adaptive thinking that cannot be disabled. Effort controls depth, latency and token use, but integrations cannot force a pure non-thinking mode. This makes it a natural candidate for difficult agent work, while Sol offers more control when the same application mixes simple routing and complex reasoning.

Anthropic says Opus 5.5 completes typical work with fewer tokens and steps than Opus 5. That does not establish superiority over Sol. Test both on the same repositories, tool permissions and acceptance criteria through the AI Testing & Evaluation hub.

Integration and migration differences

Choose Sol when your application already uses OpenAI’s Responses API, hosted tools or OpenAI-compatible operational controls. Its direct model path supports text and image input, text output, function calling and structured outputs. Standard processing is the documented route for EU data residency; regional processing adds 10%, and Fast does not support EU data residency.

Choose Opus 5.5 when your stack is Claude-native or requires the same model through AWS, Google Cloud or Microsoft Foundry. Migration from Opus 5 is not only a model-name change: thinking cannot be disabled, forced tool use can return an error, thinking blocks are tied to the model and conversation, and older computer-use tool versions may require changes. Preserved-thinking rules also affect edited conversation history.

Opus 5.5 supports zero data retention. Procurement and security teams should still verify the exact platform, region and retention agreement rather than transferring Claude API terms to a cloud reseller automatically.

GitHub Copilot users

GitHub is gradually rolling out both models to Copilot Pro+, Max, Business and Enterprise across VS Code, Visual Studio, Copilot CLI, the coding agent, Copilot App, github.com, GitHub Mobile, JetBrains, Xcode and Eclipse. Managed organizations can control access through model policies.

Because both have the same documented plan coverage, plan eligibility does not decide this comparison. Copilot bills the selected model through usage-based billing, so check GitHub’s current multiplier and availability rather than applying raw API prices directly. See the Copilot App guide for governance and OpenTelemetry controls.

Choose GPT-6 Sol when

• standard input and output cost must stay low;
• one workflow needs both non-reasoning and high-effort modes;
• the Responses API and OpenAI hosted-tool ecosystem are already central;
• Batch or Flex processing fits asynchronous workloads;
• ordinary prompts stay below the 272K long-context threshold;
• evaluations show no successful-task advantage for Opus.

Choose Claude Opus 5.5 when

• an existing Claude agent can migrate with bounded integration work;
• AWS, Google Cloud or Microsoft Foundry distribution is required;
• a one-hour prompt cache suits long agent sessions;
• always-on adaptive reasoning matches the workload;
• very high Batch output is needed and the 300K beta is acceptable;
• production evaluations show fewer steps, retries or reviewer interventions.

Verdict

GPT-6 Sol is the economic default: half the standard input/output price, cheaper cache writes and optional non-reasoning execution. Claude Opus 5.5 is the integration and agent-behavior alternative, especially for Claude-native or multi-cloud teams. Above 272K input, Sol’s pricing advantage narrows sharply, so long-context buyers should run exact cost and task-success evaluations before standardizing.

Read the GPT-6 Sol guide and Claude Opus 5.5 guide for their full specifications.