The short answer
For a specific Gemini 3 API comparison, this article pairs Google’s Gemini 3.1 Pro Preview (gemini-3.1-pro-preview) with OpenAI’s GPT-6.1 Sol (gpt-6.1-sol). This is a named comparison—not a claim that the models are equivalent or that this is the only fair pairing. OpenAI describes GPT-6.1 Sol as a balance of capability and cost; it is one explicitly identified API model to compare with Gemini 3.1 Pro Preview. (Google model documentation; OpenAI model documentation)
The clearest documented input difference is direct audio and video input: Google lists text, images, video, audio, and PDF for Gemini 3.1 Pro Preview; OpenAI lists text and images for GPT-6.1 Sol, with audio and video unsupported. PDFs are a separate case: OpenAI’s Responses API file-input documentation supports PDF inputs and describes processing their text and page images for vision-capable models, such as GPT-4o and later. GPT-6.1 Sol lists image input, so its text/image modality listing alone does not rule out PDF workflows; check the applicable file-input requirements for your deployment. Both model pages list function calling and structured outputs. These specifications do not establish which model produces better results on your workload, so treat quality, speed, and reliability as questions for a task-specific evaluation. (Google model documentation; OpenAI model documentation; OpenAI file-input documentation)
Lifecycle matters: Gemini 3.1 Pro is labeled a Preview model. Google’s deprecation schedule lists no shutdown date for gemini-3.1-pro-preview; that is not a guarantee of future availability. Google also identifies a newer Gemini model as its latest, most capable option, so teams starting a new project should consider evaluating the current alternative too. (Google deprecation schedule; Google latest-model documentation)
What models are actually being compared?
“OpenAI” names a provider, not one model. This comparison is specifically about these two API model IDs—not ChatGPT versus the Gemini consumer app, subscription plans, or every model from either company.
- Google Gemini 3.1 Pro Preview: API model ID
gemini-3.1-pro-preview. The model page labels it Preview and lists text, image, video, audio, and PDF inputs. (Google model documentation) - OpenAI GPT-6.1 Sol: API model ID
gpt-6.1-sol. OpenAI positions it for complex coding, computer use, and professional work. (OpenAI model documentation)
This pairing answers a Gemini 3-versus-OpenAI API question while making the model versions explicit. It does not compare Gemini 3.1 against OpenAI as a whole, and it is not a controlled quality benchmark.
Gemini 3.1 Pro Preview vs GPT-6.1 Sol: API comparison
| Decision point | Gemini 3.1 Pro Preview | GPT-6.1 Sol |
|---|---|---|
| API model ID | gemini-3.1-pro-preview (Google) |
gpt-6.1-sol (OpenAI) |
| Lifecycle | Preview; Google’s deprecation schedule lists no shutdown date (Google) | Listed in OpenAI’s API model documentation (OpenAI) |
| Documented inputs | Text, image, video, audio, PDF (Google) | Text and image; audio and video unsupported. PDF handling is documented separately for the Responses API and vision-capable models. (OpenAI model documentation; file-input documentation) |
| Input/context limits | 1,048,576 input tokens (Google) | 1,050,000-token context window; maximum input 922,000 tokens (OpenAI) |
| Maximum output | 65,536 tokens (Google) | 128,000 tokens (OpenAI) |
| Function calling | Supported (Google) | Supported; tool calling requires the Responses API (OpenAI) |
| Structured outputs | Supported (Google) | Supported (OpenAI) |
| Other documented tools | Code execution and search grounding; file search is marked “AI Studio only” (Google) | The model page lists Responses API support for web search, file search, image generation, code interpreter, hosted shell, apply patch, skills, computer use, MCP, and tool search. (OpenAI) |
| Standard text-token rates | A secondary pricing reference reports $2 input / $12 output per 1M tokens for prompts up to 200K tokens; it reports higher rates for larger prompts. Verify current rates with Google before deployment. (pricing reference) | $2 input / $10 output per 1M tokens; API prompts above 272K input tokens have a higher-price condition. (OpenAI pricing documentation) |
The model-specific pages distinguish input or context limits from maximum output; don’t assume those maxima can simply be added together for a single request. For Gemini, check the limits and availability of the specific API feature you plan to use. For OpenAI, its listed built-in tools are documented for the Responses API; don’t assume they are interchangeable across endpoints. (Google; OpenAI)
Prices are USD per million tokens and refer to API usage, not consumer subscriptions. The Gemini rates above come from a secondary pricing reference, not the model page; verify the current official rate and applicable billing conditions before deployment. OpenAI documents its standard rates and says API prompts above 272K input tokens are charged at higher input/cache and output rates for the full request. Caching, batch or other processing options, tool charges, and billing rules can change the final amount. (Gemini pricing reference; OpenAI model and pricing details)
Example API cost
For a workload that uses 1 million input tokens and 100,000 output tokens in total, spread across requests that stay below each provider’s long-prompt threshold, and excluding caching, batch discounts, and separate tool charges:
- Gemini 3.1 Pro Preview: 1M × $2 input + 0.1M × $12 output = $3.20.
- GPT-6.1 Sol: 1M × $2 input + 0.1M × $10 output = $3.00.
This is a rate-based estimate, not a quality-adjusted cost comparison. It assumes each Gemini prompt stays within the quoted 200K-token tier and each GPT-6.1 Sol API prompt stays at or below 272K input tokens. The Gemini figures are from a secondary pricing reference; confirm applicable rates with Google. Compare cost per successful task in your own evaluation: retries, thinking tokens, different output lengths, and tool use can change the result. (Gemini pricing reference; OpenAI model and pricing details)
Which API fits which workload?
- You need direct audio or video input: Gemini 3.1 Pro Preview’s API page lists both; GPT-6.1 Sol’s page lists audio and video as unsupported. For PDFs, Gemini lists PDF input, while OpenAI documents PDF file inputs separately through the Responses API; its file guide describes processing PDFs for vision-capable models, and GPT-6.1 Sol lists image input. Don’t infer PDF exclusion from the audio/video modality listing; check the documented file-input workflow for your deployment. (Google; OpenAI model documentation; OpenAI file-input documentation)
- You rely on function calling or structured output: Both model pages list these capabilities. Test your actual schemas and tool calls; shared feature names do not prove identical behavior or output quality. For GPT-6.1 Sol, tool calling requires the Responses API. (Google; OpenAI)
- You need a provider-documented built-in tool: GPT-6.1 Sol’s page lists Responses API tools including web search, file search, code interpreter, and computer use. Google’s Gemini page lists code execution and search grounding, while marking file search “AI Studio only.” Check the documented API surface against your deployment needs. (OpenAI; Google)
- You need a high output ceiling: The listed maximum output is 65,536 tokens for Gemini 3.1 Pro Preview and 128,000 for GPT-6.1 Sol. Test whether your real request can use those limits effectively; larger limits alone do not establish better results. (Google; OpenAI)
- You need stable lifecycle commitments: Gemini 3.1 Pro Preview is explicitly a preview model. Google’s published schedule currently lists no shutdown date, but preview status still matters to a long-lived production dependency. Review each provider’s current lifecycle and service terms before committing. (Google deprecation schedule)
- You are deciding on quality, speed, or consistency: The documentation and price table cannot establish a universal winner on those measures. Compare both APIs on your actual inputs, tools, success criteria, and repeated runs rather than treating general promotional descriptions as a controlled head-to-head result.
How to choose for production
Run a bounded comparison before routing important traffic:
- Select representative normal and difficult requests, including the real image, video, audio, or document inputs your application uses.
- Keep the task, source data, tool definitions, output schema, and success criteria consistent across both tests.
- Score task-specific quality and failure modes—for example, missed fields, incorrect answers, invalid tool arguments, or failed steps.
- Measure end-to-end latency and repeatability across multiple runs; don’t substitute tokens per second for full-request time.
- Estimate cost from actual input, output, thinking, retries, and tool use under each provider’s current billing rules.
- Decide whether Gemini’s Preview lifecycle is acceptable for your release and migration requirements. If not, evaluate a stable alternative separately.
Bottom line: For the explicit Gemini 3 API matchup, compare Gemini 3.1 Pro Preview with GPT-6.1 Sol. Gemini’s documentation lists audio and video inputs; GPT-6.1 Sol lists those modalities as unsupported, while OpenAI separately documents PDF file input through the Responses API for vision-capable models. GPT-6.1 Sol has a higher listed maximum output and a lower illustrative total for the stated token mix. Those are specification and arithmetic differences—not proof of a general quality or production-reliability winner. Since Google identifies a newer model as its latest, most capable option, include it in a new-project evaluation if using the newest Gemini model matters. (Google model documentation; OpenAI model documentation; OpenAI file-input documentation; Google latest-model documentation)
Sources
Gemini 3.1 Pro Preview — Gemini API
GPT-6.1 Sol Model — OpenAI API
Gemini deprecations — Gemini API