Test gpt-image-2 first when the work is OpenAI-native, uses the Image API, or needs structured edits. Test gemini-3.1-flash-image first when you want the faster, lower-cost Google lane for broad image iteration. Test gemini-3-pro-image first when dense text, complex layouts, 4K output, grounding, or high review cost makes the premium Google lane worth proving.
The useful comparison is not a universal model ranking. It is a route decision: which official owner you are integrating with, which workload you are testing, and what same-prompt evidence is enough before you move production traffic.
| Route | Test first when | Hold back when |
|---|---|---|
| GPT Image 2 | You want OpenAI-native generation, edits, reference work, or a single OpenAI billing/support owner. | Your stack is already Google-side and the Google fast lane passes the same prompt set. |
| Gemini 3.1 Flash Image | You need a fast Google API default for broad generation, variants, and cost-sensitive testing. | The job depends on Pro-level text rendering, complex layout, 4K, grounding, or low failure cost. |
| Gemini 3 Pro Image | The asset is expensive to reject: dense typography, complex composition, 4K delivery, grounded context, or final marketing work. | Flash passes the same prompts with acceptable retries and total cost. |
Current API names matter. Nano Banana 2 is the reader-facing alias for Google's gemini-3.1-flash-image, and Nano Banana Pro is the alias for gemini-3-pro-image. Do not start new work from the older preview-style Gemini image IDs; treat them as migration cleanup, then prove the remaining routes with the same six prompts: dense text, product shot, reference edit, diagram or UI board, 4K hero, and multilingual copy.
Use current model IDs before comparing quality
The first practical mistake is comparing nicknames instead of callable routes. OpenAI's current image generation docs use gpt-image-2 for the GPT Image 2 model in the Image API. OpenAI also exposes image generation as a built-in image_generation tool in Responses API workflows, but that is a different orchestration surface from a direct Image API call. If you are building a backend image endpoint, start by deciding whether you want the direct Image API route or a broader Responses workflow.
Google's side needs the same discipline. gemini-3.1-flash-image is the current Gemini 3.1 Flash Image route, often discussed as Nano Banana 2. gemini-3-pro-image is the current Gemini 3 Pro Image route, often discussed as Nano Banana Pro. Google release notes say both stable IDs reached GA on May 28, 2026, while the older gemini-3.1-flash-image-preview and gemini-3-pro-image-preview IDs are deprecated and scheduled to shut down on June 25, 2026.
That does not mean every old article, code sample, provider page, or benchmark row became useless overnight. It means old preview strings should be treated as migration evidence, not as the ID you choose for a new production integration. If a test harness still calls a preview model, update the ID first; otherwise you are comparing stale access behavior against current model behavior.
| Reader-facing name | Current API ID | Owner | Use it for |
|---|---|---|---|
| GPT Image 2 | gpt-image-2 | OpenAI | OpenAI-native generation, edits, structured image work, output controls, and API ownership. |
| Gemini 3.1 Flash Image / Nano Banana 2 | gemini-3.1-flash-image | Fast image iteration, high-volume tests, cost-sensitive Google-side workloads. | |
| Gemini 3 Pro Image / Nano Banana Pro | gemini-3-pro-image | Complex graphic design, dense text, product mockups, factual visualizations, grounding, and final-asset work. |
Route owner decides more than model taste
Use GPT Image 2 first when the image layer is already part of an OpenAI product or developer stack. That can mean direct Image API generation, edits, reference-guided image work, output format control, compression control, or a multi-step workflow where the same OpenAI environment also handles text, tool calls, and product logic. OpenAI's docs also note that organization verification may be required for GPT Image models, so access is part of the route decision rather than a hidden surprise.
Use Gemini 3.1 Flash Image first when the job is broad generation at scale: many creative variants, internal product mockups, campaign drafts, routine product scenes, or experiments where a lower-cost Google route can reach the acceptance bar. Flash is not merely the "weaker" choice. It is the route Google positions around performance, cost, and latency balance. If Flash passes the same prompt set, Pro does not automatically earn the next call.
Use Gemini 3 Pro Image first when the job is expensive to reject. Dense text, diagram-like layouts, grounded or fact-sensitive visuals, complex graphic design, product mockups, and high-value marketing assets can justify starting with the premium lane. The right reason to use Pro is not that the name sounds stronger; it is that the workload carries enough approval risk that a better first pass can reduce total rework.
One subtle point matters for 4K. Google image-generation docs describe 1K, 2K, and 4K support across Gemini 3 image models, with Flash also supporting 512 output. Do not reduce the decision to "Pro means 4K, Flash means not 4K." The better split is whether your 4K output also needs harder text, composition, grounding, or final-asset control.
Compare official cost boundaries, not flat winner prices
Cost comparison only works when every price row keeps its owner label. OpenAI's GPT Image 2 examples in the image generation guide show output cost changing sharply by size and quality. On June 13, 2026, the guide listed example gpt-image-2 output costs for a 1024x1024 image at about $0.006 on low quality, $0.053 on medium quality, and $0.211 on high quality. For 1024x1536 or 1536x1024, the same examples show about $0.005, $0.041, and $0.165 for low, medium, and high quality.
Those examples are not a universal per-image promise. Inputs, edits, output quality, dimensions, generated tokens, retries, cache behavior, and workflow orchestration can change the bill. The practical use of the OpenAI price row is to show that GPT Image 2 can be cheap for low-quality tests and much more expensive for high-quality final outputs.
Google's pricing table has a different shape. On June 13, 2026, Google's Gemini API pricing page listed Gemini 3.1 Flash Image standard image output examples at $0.045 for 0.5K, $0.067 for 1K, $0.101 for 2K, and $0.151 for 4K. It listed Gemini 3 Pro Image standard image output at $0.134 for 1K or 2K and $0.24 for 4K.
That makes the cheapest route depend on the actual job. A low-quality GPT Image 2 test can be cheaper than a Google image output row. A high-quality OpenAI output can be more expensive than Flash or Pro at some sizes. Gemini 3 Pro Image can look cheaper than one high-quality OpenAI example and still be the wrong first test if your product needs OpenAI-native edits, response orchestration, or one OpenAI-owned support path.
For deeper pricing work, keep the route decision separate from cost cataloging. OpenAI paid-route questions belong in the GPT Image 2 API cheap route guide. Google price, quota, and free-tier questions belong in the Nano Banana Pro pricing and quota guide.
Test the same prompt set before switching production
A public benchmark can tell you which routes deserve a trial. It cannot tell you which route should take your production traffic. The production decision needs the same prompts, same references, same target size, same language requirements, and the same acceptance bar across all remaining candidates.
Start with six prompts:
| Proof prompt | What it reveals | Routes to include |
|---|---|---|
| Dense text poster | Spelling, hierarchy, typography, and layout discipline. | GPT Image 2 and Gemini 3 Pro Image, with Flash as a cost baseline if relevant. |
| Product shot | Object consistency, lighting, realism, and controllability. | Gemini 3.1 Flash Image and Gemini 3 Pro Image, plus GPT Image 2 when OpenAI edits matter. |
| Reference edit | Whether the output preserves the source object and follows the edit instruction. | GPT Image 2 and the Google route you expect to deploy. |
| Diagram or UI board | Structured composition, labels, visual hierarchy, and text handling. | GPT Image 2 and Gemini 3 Pro Image. |
| 4K hero image | Detail stability, scaling behavior, and final-asset polish. | Gemini 3 Pro Image, Gemini 3.1 Flash Image, and one current baseline. |
| Multilingual copy | Non-English text, line breaks, and layout behavior. | Every route still under consideration. |
Store more than the final image. Keep prompt text, reference assets, size, quality, aspect ratio, retry count, accepted output, rejected output reason, latency, and estimated cost. A model that creates one beautiful example but fails four routine prompts is not the right production default. A route that looks less dramatic but keeps the brief, cost, and retry budget stable may be better for a product team.
Set acceptance criteria before running the proof. For a poster, the text must be correct and the hierarchy must survive review. For a product shot, the object must stay recognizable. For a diagram, labels must not become decorative noise. For a reference edit, the source object and edit instruction both matter. If the acceptance bar moves after you see the output, you are choosing from taste rather than evidence.
Stop rules keep the comparison honest
Stop treating GPT Image 2 as the default when the workflow is already Google-side, the prompts are mostly broad generation, and Gemini 3.1 Flash Image meets the acceptance bar with lower total cost or fewer integration steps. Keep GPT Image 2 in the final comparison when the image route needs OpenAI-native edits, OpenAI output controls, Responses orchestration, or direct OpenAI account ownership.
Stop treating Gemini 3.1 Flash Image as enough when the proof set repeatedly fails on dense text, complex layouts, grounded visual work, 4K final review, or expensive product-approval loops. That is where Gemini 3 Pro Image has a concrete job. Pro is not a trophy route; it is an escalation route for specific failure modes.
Stop treating Gemini 3 Pro Image as the production default when Flash passes the same prompt set with acceptable retries, or when total cost and latency do not match the product's volume. Premium output is valuable only when it changes the acceptance result or reduces rework enough to justify the lane.
Stop comparing any route until the IDs are current. If your old test used gemini-3-pro-image-preview or gemini-3.1-flash-image-preview, migrate it before scoring. A preview shutdown issue, access error, or stale model alias should not be interpreted as image quality.
Keep sibling guides narrow
The first job is the three-route choice. Related subquestions should stay narrow instead of folding every price, quota, free-access, and output-size branch into one decision.
Use the Gemini 3 Pro Image vs Gemini 3.1 Flash Image comparison when the only decision is Google Flash versus Google Pro. Use GPT Image 2 4K image generation when your OpenAI question is size, aspect ratio, or resolution mechanics. Use Is GPT Image 2 API free? when the problem is official free-tier status rather than model choice.
Keeping those pages separate prevents a common failure: one comparison tries to answer route ownership, image quality, price, free access, quota, 4K output, provider routes, and troubleshooting at once. The result usually becomes less useful for every reader. Make the first route decision here, then branch only when the next question is genuinely narrower.
FAQ
Is GPT Image 2 better than Gemini 3 Pro Image?
Not universally. GPT Image 2 is the better first route when your workflow is OpenAI-native, depends on the Image API, needs structured edits, or benefits from one OpenAI-owned account and support path. Gemini 3 Pro Image is the better first route when the job is Google-side and carries dense text, complex layout, grounding, 4K, or high rejection cost. Use the same prompt matrix before calling either one "better" for production.
Is Nano Banana 2 the same as Gemini 3.1 Flash Image?
For developer routing, treat Nano Banana 2 as the reader-facing alias for gemini-3.1-flash-image. Use the official model ID in code, logs, tests, and cost notes. The alias is useful for readers, but the model ID is what keeps your integration and migration work clear.
Is Nano Banana Pro the same as Gemini 3 Pro Image?
For developer routing, yes: Nano Banana Pro refers to Google's gemini-3-pro-image route. Use the alias when discussing the market-visible name and the model ID when discussing API calls, pricing, changelogs, and production migration.
Should I still use the Gemini preview image IDs?
Do not start new work from the preview-style IDs. Google release notes say gemini-3.1-flash-image-preview and gemini-3-pro-image-preview are deprecated and scheduled to shut down on June 25, 2026. Treat preview IDs as migration cleanup, not as a current production target.
Which route is cheapest?
There is no single cheapest route without size, quality, retry count, and workload. GPT Image 2 low-quality examples can be very inexpensive, while high-quality examples are much more expensive. Gemini 3.1 Flash Image is the Google cost-sensitive lane. Gemini 3 Pro Image costs more than Flash in Google's table but can reduce rework on hard assets. Compare total accepted-output cost, not just one row.
Which route is fastest?
Gemini 3.1 Flash Image is the first route to test when speed, latency, and high-volume iteration matter on Google's side. That does not prove it is fastest in your product stack after authentication, request size, references, retries, storage, and review time. Measure end-to-end latency in the same harness you plan to deploy.
Which route handles text-heavy images best?
Start with GPT Image 2 when the workflow is OpenAI-native and the text-heavy asset also needs OpenAI edits or output controls. Start with Gemini 3 Pro Image when the Google route owns the stack and the asset has dense typography, layout risk, or final marketing value. Include Gemini 3.1 Flash Image as the cost baseline if the text burden is moderate.
Can I use GPT Image 2 through Responses API?
Use the Image API when you want the direct gpt-image-2 image route. Use Responses API when image generation is one tool inside a conversational or multi-step OpenAI workflow. The route choice matters because billing, orchestration, tool output handling, and application architecture are not identical.
When should I switch production from one route to another?
Switch only when the new route beats the current baseline on the prompts that matter, with acceptable retries, cost, latency, access, and rollback behavior. If the new route wins only on a public example but not on your dense text, reference edit, product shot, diagram, 4K, or multilingual proof prompts, keep the current production route.
