Short answer: For a new production API project, neither model is a safe default as of November 12, 2026. OpenAI marks o3 as deprecated and names GPT-5 as its successor. Google’s notices do not clearly reconcile: its Gemini API model page says access is limited to users who have actively used the 2.5 models, which will continue to be served through the API until further notice; its AI Studio model page says Gemini 2.5 Pro access is limited to existing users until December 1, 2026, after which requests will be routed to Gemini 3.8 Flash and billed at that model’s rates. The supplied documentation does not establish how these statements apply to an existing Developer API project. Confirm access and routing for your project before relying on the model. (OpenAI o3 model page; Gemini API model page; Google AI Studio model page)
For a legacy workflow where both remain available, the supplied head-to-head evidence is narrow: a June 2025 report says the models performed similarly through 128,000 tokens on Fiction.Live’s complex-story task. At 192,000 tokens, it reports a sharp decline for o3 while Gemini 2.5 Pro’s preview-06-05 remained above 90% accuracy. That is one reported benchmark—not a general verdict on reasoning, coding, cost, or other production work. (The Decoder’s Fiction.Live report)
At a glance
| Decision factor | Gemini 2.5 Pro | OpenAI o3 |
|---|---|---|
| Availability notices | The Gemini API model page says existing active users can continue using the 2.5 models through the API until further notice. The AI Studio page says access is limited to existing users until Dec. 1, 2026, then requests route to Gemini 3.8 Flash. The relationship between these notices is unclear. | OpenAI’s model page marks o3 deprecated and names GPT-5 as its successor. |
| Documented context and output limits | 1,048,576 input tokens; 65,536 maximum output tokens. | 200,000 context tokens; 100,000 maximum output tokens. |
| Documented inputs and outputs | Audio, images, video, text, and PDF input; text output. | Text and image input; text output. |
| Function calling and structured outputs | Listed as supported on the Gemini API model page. | Listed as supported on the OpenAI o3 model page. |
| Reported head-to-head result | The 2025 report says preview-06-05 stayed above 90% on Fiction.Live through 192K tokens. |
The report describes performance as similar through 128K, then sharply worse at 192K on that task. |
| Standard API token pricing | I could not confirm an applicable Gemini 2.5 Pro rate for existing-user access in the official pricing material retrieved. | OpenAI lists $2 per million input tokens and $8 per million output tokens. |
The model pages list the context, output, and capability details above, but those specifications do not guarantee availability to every account. Google’s Gemini API model page says service will continue until further notice, while its AI Studio model page gives a December 1, 2026 routing notice. The supplied documentation does not explain how those statements reconcile for an existing Developer API project. Google Cloud’s Agent Platform page separately states that Gemini 2.5 Pro retirement took effect October 20, 2026. (Gemini API model page; AI Studio model page; Cloud Agent Platform model page)
What the head-to-head benchmark does—and doesn’t—show
Fiction.Live measures how well models understand and accurately reproduce intricate stories and contexts. The June 8, 2025 report says o3 and Gemini 2.5 Pro performed similarly through 128,000 tokens. At 192,000 tokens, it reports that o3’s performance dropped sharply, while Gemini 2.5 Pro’s preview-06-05 remained above 90% accuracy. The report also describes o3 as flawless through 8K tokens, then fluctuating at 16K and 60K; Gemini had a minor dip at 8K before recovering. These are results reported by the article, not tests conducted here. (The Decoder’s Fiction.Live report)
Practical takeaway: If your existing application handles very long, story-like material near the tested 192K-token length, that report is a reason to test Gemini 2.5 Pro against your own data before choosing o3. It does not establish that Gemini is better for ordinary prompts, coding, image analysis, or all long-document tasks. The reported benchmark reached only 192K tokens; a listed maximum context size is not proof of accuracy across that whole context. (The Decoder’s Fiction.Live report)
For a long-PDF workflow, for example, test the same relevant pages and questions in both models. Remove material unrelated to the question, then check whether the answer preserves important details from different sections. The benchmark report also cautions against filling a large context with irrelevant material. (The Decoder’s Fiction.Live report)
Production API capabilities and limits
Google’s Gemini API model page lists gemini-2.5-pro with audio, image, video, text, and PDF inputs; text output; a 1,048,576-token input limit; and a 65,536-token output limit. It lists function calling and structured outputs as supported. OpenAI’s o3 model page lists o3 with text and image inputs, text output, a 200,000-token context window, a 100,000-token output maximum, function calling, and structured outputs. These are documented capabilities, not a guarantee of continued access. (Gemini API model page; OpenAI o3 model page)
Keep the model names exact. A third-party comparison supplied for this query compares o3-mini-high, not o3; its specifications must not be transferred to o3. Likewise, o3-pro is a different model variant.
Availability notices and what remains unclear
- OpenAI API: The official o3 page marks the model deprecated and names GPT-5 as its successor. Its model details list
o3-2025-04-16as the default snapshot. Do not assume a new project can depend on the model just because its specifications remain documented. (OpenAI o3 model page) - Google Gemini API and AI Studio notices: The Gemini API model page says access to the 2.5 models is limited to users who have actively used them, describes them as not deprecated, and says they will continue to be served through the API until further notice. The AI Studio model page says API access to Gemini 2.5 Pro is limited to existing users until December 1, 2026; it says requests will then route to Gemini 3.8 Flash and be billed at Gemini 3.8 Flash rates. The supplied documentation does not establish how these statements reconcile for an existing Developer API project, so confirm the access and routing that apply to your project. (Gemini API model page; AI Studio model page)
- Google Cloud Agent Platform: Its model page says Gemini 2.5 Pro retirement took effect October 20, 2026. This is a separately documented Cloud lifecycle notice; it does not resolve the relationship between the Gemini API and AI Studio API-access notices. (Cloud Agent Platform model page)
For a new deployment, choose a model that is currently supported for your intended API and account. If retaining a legacy integration, verify the applicable access and routing, and test the replacement path before migration rather than assuming the same model identifier will continue to provide the same behavior. (OpenAI o3 model page; Gemini API model page; AI Studio model page)
API pricing: what can be compared
OpenAI’s o3 model page lists standard API prices of $2 per 1 million input tokens and $8 per 1 million output tokens. OpenAI’s token guidance says reasoning tokens count toward output usage and are billed at the output-token rate; a short visible answer may therefore use more billable output tokens than its displayed text suggests. These are API rates, not ChatGPT subscription prices. (OpenAI o3 model page; OpenAI token guidance)
For Gemini 2.5 Pro, I could not confirm from the official pricing material retrieved a current rate that applies to existing-user Gemini Developer API access. That is a missing verified price, not evidence that Google publishes no price. Because Google’s model pages give differing API-access notices, don’t treat an older or third-party rate as a confirmed price for your account and access path. There is no verified, apples-to-apples current price verdict here. Confirm the rate and billing terms for the endpoint and account you can actually use. (Gemini Developer API pricing; Gemini API model page; AI Studio model page)
How to choose—or validate a legacy workflow
If you still have access to both models and are deciding whether to retain one for an existing system, run a small matched evaluation before changing production:
- Use representative work. Choose real inputs that reflect the application: typical prompts, long-context cases, and images or documents if relevant, including examples that have previously failed.
- Set success criteria first. Define what counts as correct, complete, grounded in the supplied material, and usable by your application. Apply the same criteria to both models.
- Record exactly what ran. Log the API surface, model identifier or snapshot, evaluation date, input size, settings, and tool or structured-output configuration.
- Compare outcomes and failures. For long documents, check whether answers connect details across sections—not just whether the prompt fits within the advertised context limit. For multimodal tasks, evaluate the actual inputs your application sends.
- Measure relevant constraints. Track latency and token usage. Calculate cost only from prices and billing rules confirmed for your account and endpoint.
- Test the replacement before migration. Since both queried models have lifecycle constraints, evaluate the supported replacement you intend to deploy and check for changes in API behavior, output, and billing.
This is a recommended evaluation process, not a test performed for this article. Without your workload and confirmed account access, the evidence does not support a universal winner.
Bottom line
For new production API deployments as of November 12, 2026, neither Gemini 2.5 Pro nor o3 is a sound default: both have lifecycle constraints, and Google’s API-access notices do not clearly reconcile. For a legacy long-context workflow, the June 2025 Fiction.Live report favors Gemini 2.5 Pro preview-06-05 at 192K tokens on that specific complex-story task, while describing performance as similar through 128K. Treat that result as a reason to run a version-matched test—not proof that Gemini wins overall. For other workloads, compare currently supported models using representative inputs, and confirm availability and pricing for the exact API you plan to deploy. (OpenAI o3 model page; Gemini API model page; AI Studio model page; The Decoder’s Fiction.Live report)
Sources
Understanding and counting tokens | OpenAI Help Center
Gemini 2.5 Pro | Google AI Studio
Gemini 2.5 Pro | Gemini Enterprise Agent Platform
Google's Gemini 2.5 Pro beats OpenAI's o3 model in processing complex, lengthy texts