If a coding agent stopped behaving as expected after a switch to GPT-6.1 Sol, first confirm which product and model are handling the task. Then check API tool compatibility, reasoning settings and request parameters, migrated cache configuration, and whether the agent can access the files and permissions it needs. These checks isolate common mismatches; none alone proves the model caused the failure or guarantees a fix.
1. Confirm the product surface and active model
Start by separating a Codex or ChatGPT Work issue from an API integration issue. GPT-6.1 Sol is listed for ChatGPT Work and Codex, but availability depends on paid-plan rollout, workspace settings, and account access. If the model is unavailable or a different one is running, check those conditions before changing your agent configuration. See OpenAI’s model availability guidance.
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In Codex, inspect the model picker for the task itself. A manually selected model can remain active even after you have switched models elsewhere. For API requests, verify that the request uses the exact model identifier gpt-6.1-sol. OpenAI announced GPT-6.1 Sol on September 29, 2026, for complex coding and professional work in its API changelog.
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If the agent needs to run shell commands, read or edit files, or use other tools, confirm that the integration uses the Responses API. OpenAI’s Using GPT-6 API migration guide says: “Use the Responses API for tool calling.” Chat Completions is supported for GPT-6.1 Sol without tool calling, so an integration that relies on tool execution but sends Chat Completions requests has a specific compatibility issue to investigate.
3. Check reasoning effort and request parameters
GPT-6.1 Sol supports reasoning effort values low, medium, high, xhigh, and max. The model page lists medium as the default. The values none and minimal are not supported. If a previous model configuration used minimal, OpenAI advises starting at low when migrating; retain an effective prior effort where the new model supports it. The supported options are documented on the GPT-6.1 Sol model page.
For non-none reasoning effort, the migration guide directs developers to remove parameters that may not be accepted:
- Remove
temperature,top_p, andtop_logprobs. - With Chat Completions, also remove
logprobs. - With Responses, remove
message.output_text.logprobsfrom theincludearray.
Check the actual request payload, including defaults added by your SDK or wrapper, rather than only the settings visible in an agent UI.
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4. Review migrated prompt-cache settings and approval behavior
Prompt-cache configuration
If you migrated from GPT-5.5 or earlier, update the cache setting as the migration guide directs: replace prompt_cache_retention with prompt_cache_options.ttl set to "30m". Also review cache boundaries and cache-write billing. This is a conditional check for migrated integrations, not a general explanation for every failure.
Repeated approval pauses
If the agent repeatedly stops to ask for approval instead of following through, consult the GPT-6 guide’s prompting guidance on initiative and follow-through. Treat this as relevant when the symptom matches; it is distinct from a rejected API request or a tool the integration cannot call.
5. Restore missing context, files, and permissions
An agent may respond but fail to act on the repository because the task or environment does not provide what it needs. Check that the prompt states the desired outcome, the relevant repository files are available, connected apps are accessible, and the environment has the required permissions. These checks matter whether the agent is ignoring files, cannot use an action, or produces work detached from the codebase.
OpenAI’s usage guidance for GPT-6 Astra in Work and Codex notes that increasing reasoning effort cannot supply missing information or access. Higher effort can also consume more usage and does not always improve results, so first restore context and permissions rather than treating effort as a universal fix.
6. Compare the same tasks before drawing a conclusion
Once configuration and access are checked, rerun a small set of representative coding tasks. Keep the repository context, instructions, permissions, and available tools the same across runs. Record what actually differs:
- Does the request fail or get rejected?
- Does the agent omit tool activity it should perform?
- Can it see the files and connected resources the task requires?
- Does it complete the same task differently, and at what reasoning effort?
OpenAI’s GPT-6.1 Sol model page recommends comparing with Astra on your own tasks to assess quality and cost tradeoffs. The documentation does not establish a universal pass threshold for an individual codebase; judge the results against the tasks that matter to your workflow.
If the problem is visual or image-related, keep model-specific fixes in scope. A September 25, 2026 changelog entry reports an image-encoding fix for GPT-6 Sol and GPT-6 Luna and recommends rerunning evaluations and retrying affected visual workflows. That entry does not identify GPT-6.1 Sol as a model covered by the fix, so it should not be treated as evidence that the same issue or remedy applies here.
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