All Google Gemini API Models Compared: Is Gemini 3.1 Flash Image the Cheap Choice?

All Google Gemini API Models Compared: Is Gemini 3.1 Flash Image the Cheap Choice?

If you are choosing a cheap AI API, do not compare input prices alone. Gemini 3.1 Flash Image is the cheapest Google model in the OneMux catalogue for listed input cost, at $0.007098 per 1M input tokens, but its listed output rate is $10.92. That makes it potentially compelling for input-heavy image-oriented workflows, while a lower-priced Gemini Flash model may be easier to operate for applications that generate many output tokens.

This guide compares the Google models available through OneMux, with the decision centered on cost structure rather than model-name prestige. For broader Google model context, Requesty describes Google's catalogue as offering multimodal capabilities and lists Google LLC (Gemini API) models starting at $0.05 per 1M input tokens. See the Requesty Google model catalogue for that external comparison point.

The short answer: which Google model should you choose?

• Choose Gemini 3.1 Flash Image when very low input cost is important and you can control or limit generated output.
• Choose Gemini 3.5 Flash Lite when your application needs a lower-cost general Gemini model across both input and output.
• Choose Gemini 3.5 Flash when you want a middle-ground Flash price rather than the lowest-cost option.
• Choose Gemini 3.1 Pro Preview when your workload justifies a higher listed rate for a Pro-tier model.
• Choose Veo 3.1 Fast Generate Preview or Veo 3.1 Generate Preview for video generation, editing, and motion workflows—not ordinary text or multimodal application requests.

The right choice depends on the ratio between input tokens, output tokens, and non-token media usage. Treat the prices below as the rates listed in the supplied OneMux catalogue and confirm current pricing before production deployment.

Google models available through OneMux

The catalogue labels all of these models as available through OneMux unified model routing. The two Veo entries use different pricing information from the token-priced Gemini entries, and their output price is not listed in the supplied data. Do not estimate a Veo project budget from the Gemini token rates.

Gemini 3.1 Flash Image: why the low input rate is not the whole story

The focus model's listed input price is dramatically below the other Gemini entries in this catalogue. That matters for applications that repeatedly send large prompts or multimodal context and produce limited output. Examples could include content intake, structured extraction, classification, or a workflow where the model's response is deliberately constrained.

The tradeoff is the listed output rate: $10.92 per 1M output tokens. That is substantially higher than the listed output rates for Gemini 3.5 Flash Lite, Gemini 3.6 Flash, Gemini 3.7 Flash, and Gemini 3.8 Flash. A team that evaluates only input cost could select the wrong model for a chat assistant, long-form copy workflow, or agent that produces verbose responses.

Before choosing it, measure three quantities:

1. Average input tokens per request.
2. Average output tokens per request.
3. The percentage of requests that require image or other multimodal handling.

A simple budget model is:

estimated cost = (input tokens / 1,000,000 × input rate)
               + (output tokens / 1,000,000 × output rate)

Use your actual traffic distribution rather than a single average request. Support teams and international products often have uneven usage: short requests may dominate one market, while long user-provided documents dominate another.

Gemini Flash versus Gemini Pro for developers and operators

The supplied catalogue gives Gemini 3.5 Flash Lite the lowest listed input and output combination among the general Gemini models. That makes it the practical first candidate for a cost-sensitive application that does not specifically need the input economics or image orientation of Gemini 3.1 Flash Image.

Gemini 3.6, 3.7, and 3.8 Flash share the same listed rates: $0.1365 per 1M input tokens and $0.6825 per 1M output tokens. With identical listed prices, the decision between them should come from your own testing, application requirements, and current model documentation—not from assuming that the version number alone guarantees a particular result.

Gemini 3.5 Flash is listed at $0.273 input and $1.638 output per 1M tokens. Gemini 3.1 Pro Preview is listed at $0.364 input and $2.184 output per 1M tokens. These rates are higher, so route only the tasks that justify them. A sensible production design can keep a lower-cost model as the default and reserve a higher-priced model for selected requests, subject to application testing.

Where Veo fits—and where it does not

Veo 3.1 Fast Generate Preview and Veo 3.1 Generate Preview are described in the catalogue as video models for prompt-guided generation, editing, and motion workflows. They should be evaluated as a separate media category, not as cheaper substitutes for Gemini text or multimodal models.

The Fast Generate Preview entry has a listed input price of $0.00351, while Generate Preview has a listed input price of $0.00585. Because the supplied data does not list output prices for either model, buyers should confirm how the relevant video operation is billed before estimating unit economics.

For marketing teams, the practical distinction is straightforward: use Gemini models for application intelligence and Veo for video-oriented production workflows. A single API layer can still simplify access and spend tracking across both categories.

A practical OneMux setup for a cheap AI API

OneMux gives users access to leading AI models through one OpenAI-compatible API. For a team comparing Google models with models from other providers, that reduces the need to build a separate integration and credential workflow for every model. OneMux also focuses on routing, keys, spend visibility, credit top-ups, and lower-cost pay-as-you-go usage.

A useful rollout plan is:

1. Start with one default model, such as Gemini 3.5 Flash Lite for cost-sensitive general requests.
2. Test Gemini 3.1 Flash Image separately if low input cost or image-oriented handling is central to the product.
3. Record input and output tokens by route, user segment, and geography.
4. Add explicit output limits where long responses are not necessary.
5. Route selected higher-value tasks to Gemini 3.1 Pro Preview only after measuring the quality and cost difference.

Review the OneMux documentation, follow the quickstart guide, and check OneMux pricing before configuring credits or production usage.

Conclusion

Gemini 3.1 Flash Image is not simply the cheapest Google model. It is the cheapest listed Google model for input, paired with the highest listed Gemini output rate in this comparison. That makes it a specialized economic choice: potentially strong for input-heavy, controlled-output workflows, but not automatically the best value for conversational or verbose applications.

For a broadly cost-conscious Gemini deployment, start by comparing Gemini 3.5 Flash Lite against your actual request mix. Add Gemini 3.1 Flash Image when its input profile and image-oriented use case match your workload. Use Pro selectively, and treat Veo as a separate video-generation category. With OneMux, you can keep those choices behind one OpenAI-compatible API while monitoring spend and changing routes as your traffic develops.

Sources

• OneMux model catalogue
• Gemini 3.1 Flash Image on OneMux
• Requesty Google LLC (Gemini API) model catalogue
• OneMux documentation