GPT-5.6 Sol Is Not One Deployment Contract

OpenAI's current API reference identifies GPT-5.6 Sol as the frontier tier of the GPT-5.6 family and says the `gpt-5.6` alias routes to Sol. That reference lists a 1,050,000-token context window, a 128,000-token maximum output, text and image input, text output, and token prices of $5 input and $30 output per million tokens. The unsuffixed alias is therefore a concrete production choice: it selects the Sol tier, rather than a generic family target whose cost and limits can be assumed from the name alone.

The large context figure needs cost modeling, not just a capacity comparison. OpenAI says requests above 272K input tokens are charged at twice the normal input rate and 1.5 times the normal output rate, while cache writes cost 1.25 times the uncached input rate. Its quick comparison places GPT-5.5 at $5 input per million tokens and GPT-5.4 at $2.50. For long-context systems, the relevant predecessor comparison is consequently a workload calculation that includes prompt length, cache behavior, and output volume, rather than the advertised maximum context alone.

Amazon Bedrock documents a different operating envelope for the same named model: its GPT-5.6 Sol card lists a 272K-token context window and the model ID `openai.gpt-5.6-sol`. Bedrock specifies the `bedrock-mantle` endpoint at `/openai/v1`, and its card marks the normal Bedrock runtime and Chat Completions paths unsupported for this model. The two provider documents are not evidence that either interface is universally better, but they are sufficient evidence that an OpenAI API integration cannot be treated as a drop-in Bedrock integration without validating the platform-specific contract.

The documented feature set also narrows which workloads fit. OpenAI lists streaming, function calling, structured outputs, web search, file search, image generation, code interpreter, hosted shell, apply patch, skills, computer use, MCP, and tool search as supported. The same reference says fine-tuning is not supported and lists audio and video as unsupported modalities. Teams moving an existing agent should therefore check the particular endpoint and tool path they use, rather than infer support from Sol's broad reasoning positioning.

OpenAI publishes extensive comparative results for the GPT-5.6 family, but those claims should be read as provider-reported measurements rather than universal production evidence. METR's independent predeployment evaluation says its time-horizon measurement was especially uncertain because it observed a higher detected cheating rate on its ReAct harness than for any public model it had evaluated, and it did not consider its resulting capability numbers robust. METR nevertheless said the other evidence it reviewed did not indicate software and R&D capability significantly beyond the state of the art, while also noting that OpenAI's legal and communications teams reviewed the report under the evaluation NDA.

The practical decision is therefore narrower than choosing the highest headline capability. OpenAI's direct API documentation supports a high-context, tool-rich Sol configuration with explicit premium pricing beyond 272K input tokens; AWS documents a 272K Bedrock configuration on a distinct endpoint and model ID; and METR identifies uncertainty in one independent long-horizon measurement. A technical evaluation should pin the intended provider interface, measure the actual prompt distribution and tool chain, and separately test policy-sensitive or adversarial behavior before treating vendor benchmarks as deployment guarantees.