GPT-6.1 Sol and Claude Sonnet 5.5 share the same Standard API input and output prices, but that does not make them interchangeable or equally expensive per completed coding task. Start with the route that supports your tools, then compare accepted results within a fixed budget.
Checked September 30, 2026. The prices and compatibility details below come from official documentation. Agent.Space has not run a controlled head-to-head benchmark; the trial design is our recommendation, not a performance ranking.
Which should you try first?
This is a way to reduce migration variables, not a claim that either model is better inside a particular brand of client. Codex versus Claude Code compares two complete agent setups; changing only the model in a compatible harness is a different experiment. The coding-model selection guide helps define which decision you are making.
Equal headline prices have important limits
First-party Standard API rates, in US dollars per million tokens:
Sources: OpenAI model pricing and Anthropic pricing. The Sol rates shown apply up to 272K input tokens; beyond that, the full request has doubled input/cache rates and 1.5× output rates. The table is not a quote for subscriptions, third-party routing, or Agent.Space. Other processing modes and paid tools require separate accounting.
Consider a deliberately simple, illustrative request: 20,000 uncached input tokens, 100,000 cache-read tokens, and 5,000 billable output tokens, with no cache creation or other charges. The token bill is $0.10 for Sol and $0.11 for Sonnet. That one-cent difference comes entirely from cache reads—not from evidence that either model solves the task with fewer calls.
For a real task, add every retry and repair call. Tokenization, cache behavior, and output usage can differ. A longer context window is not a reason to send an entire repository on every turn. Use the Sol pricing and upgrade guide for the model-specific conditions.
Tool compatibility comes before price optimization
For GPT-6.1 Sol tool calling, OpenAI requires Responses rather than Chat Completions. Its reasoning settings exclude none and minimal. GPT-6 guidance.
Sonnet 5.5 also changes API behavior: older requests that disable thinking or force any/tool tool selection need review. Follow the Sonnet migration guide instead of replacing only the model string. For a Claude Code client rather than your own API application, use the Sonnet setup guide.
Before a trial, verify that the model can request a tool, receive the result, and continue to a final answer. Include a recoverable tool error. A successful plain-text request does not test the loop that coding work depends on.
Compare independent runs against the same acceptance rule
Choose three tasks from your actual backlog: one localized bug, one small cross-file feature, and one investigation with a known answer. Supply the same starting revision and brief in independent copies. Keep allowed tools, network access, and permission boundaries comparable.
For each run, record:
- Whether the predefined acceptance checks passed.
- Total provider usage and charges, including retries.
- Time to a reviewable result, not just the first output token.
- Human corrections and any out-of-scope changes.
- Effective model, effort, client, and provider configuration.
Do not assume high means an equal compute budget across vendors. State the chosen configuration and spend ceiling explicitly. Review patches without the model label where practical, and repeat enough tasks to see whether a failure is recurring. The regression-evaluation guide provides a longer procedure.
One useful summary is total trial spend divided by accepted tasks. If neither setup produces an accepted result, that ratio is undefined: fix the task or workflow before declaring a cheap winner. Keep review time separate unless you have an agreed way to value it.
When keeping both routes makes sense
Keep a second route only when it addresses a repeatable failure or a distinct constraint. For example, one setup may fail your required tool loop while another completes it. That is observable evidence for this integration, not a universal intelligence ranking.
Avoid rerunning the same vague prompt until one answer looks attractive. Improve missing requirements first; use a different model to investigate a documented weakness, and retain the failed result for comparison.
Apply the decision in Agent.Space
Check live pricing and the compatible model selector before assuming either release is available. Official API rates do not establish Agent.Space charges or included usage.
If a supported combination fits, start an Agent.Space Workspace with the brief, budget, and acceptance checks. Use independent project copies for controlled comparisons; two Sessions editing shared files are not isolated experiments. Choose the setup that delivers acceptable work under your constraints, not the one with the newest name.