GPT-4 vs Gemini

The 2023 title fight has moved on: this is where OpenAI's GPT-5.6 family and Google's Gemini 3 line stand in 2026.

At a glance

Pricing

On the API side, OpenAI's pricing page lists the GPT-5.6 family in three tiers: gpt-5.6-sol at $4 per million input tokens and $20 per million output tokens, gpt-5.6-terra at $2 and $12, and gpt-5.6-luna at $0.20 and $1.20, with long-context requests billed at higher rates. Google's Gemini API pricing lists Gemini 3.1 Pro (preview) at $2 in and $12 out per million tokens for prompts up to 200K tokens, rising to $4 and $18 beyond that, and Gemini 3.7 Flash at a promotional $0.75 in and $3.75 out that is scheduled to double in 2027. Cached input and batch processing discounts exist on both platforms and matter a lot at volume.

On the subscription side, ChatGPT Plus remains $20 a month, with Pro reported at $100 and $200 tiers as of August 2026. Google sells AI Pro at $19.99 a month and cut AI Ultra to $99.99 and $199.99 tiers at I/O 2026, down from $250 for the top plan. Both vendors adjusted prices more than once during 2026, so I would treat any third-party number, including the ones on this page, as a snapshot and confirm on the official pricing pages before committing to volume.

When to pick GPT (OpenAI)

Pick OpenAI when ecosystem gravity matters more than list price. The majority of third-party AI tools, agent frameworks, and integrations target the OpenAI API shape first, hiring for it is easier, and the Azure path gives enterprises a familiar procurement and compliance wrapper. If your product needs to plug into the broadest set of existing tooling with the least friction, that is still OpenAI's advantage.

The luna tier also changed the economics at the bottom. At $0.20 per million input tokens, high-volume classification, extraction, and routing workloads that used to need careful budgeting are now close to free, which lets you keep the whole stack on one vendor instead of mixing providers to save money.

When to pick Gemini

Pick Gemini when you live in Google's world. Workspace integration means the models show up inside Gmail, Docs, and Sheets your team already uses, and Vertex AI is the natural path for anything running on Google Cloud. The roughly 1M-token context on Pro models remains the practical differentiator for whole-codebase and long-document work, even with the price step past 200K tokens.

The Flash line is the other reason. At promotional pricing, Gemini 3.7 Flash undercuts comparable tiers for many workloads while staying capable enough for production use, and the AI Pro subscription at $19.99 bundles model access with storage and Workspace features that a ChatGPT subscription does not match. For cost-sensitive teams already paying Google, the bundle math often decides it.

What most comparisons miss

Model names are moving targets. This page started life as GPT-4 vs Gemini, and both of those names now refer to retired products. Any comparison hard-coded to versions goes stale in months, which is why I compare families, ecosystems, and pricing structure instead of benchmark tables.

Output tokens dominate real bills. Headline input prices get the attention, but output costs 5 to 6 times more per token on both flagships, and reasoning modes multiply output further. A workload estimate built on input pricing alone will miss by a wide margin.

Finally, the subscription and the API are different products that people constantly conflate when budgeting. A $20 ChatGPT or Gemini plan tells you almost nothing about what the same usage costs through the API, and the switching cost that actually locks you in lives in your prompts, evals, and integrations, not in the model itself.