Choosing an AI Model for Your AI Agent

When you build an AI Agent, you can choose which AI model powers it. Different models balance cost (credit usage), accuracy, and speed differently. This guide helps you pick the right one.

The short version: Start with a balanced model. Move to a lightweight model if you want to save credits on simple tasks, or an advanced model if your agent handles complex reasoning. Then test with your own knowledge base and adjust.

How model choice affects credits usage

AI Credits are used based on the number of tokens processed — both the text sent to the model (input) and the text it generates (output).

What is a token?

A token is a small chunk of text a model reads or writes — roughly 4 characters, or about ¾ of a word.

Two things drive how many credits a model uses:

1. The model's rate. More advanced models charge more credits per 1,000 tokens. Output usually costs several times more than input.

2. How much the model "thinks." Advanced models can reason through a problem before answering. Better reasoning improves accuracy on hard questions but generates more output tokens — which uses more credits.

Rule of thumb: the smarter the model and the harder you make it think, the more credits each reply uses.

Model tiers at a glance

Models fall into three broad tiers. Use this to narrow down quickly:

Tier	Models	Credit usage	Best accuracy for	Good for	Speed
Lightweight	DeepSeek-V4-Flash gpt-4o-mini gpt-5.4-nano gpt-oss-120b gpt-5-mini	Low	Simple, well-defined tasks	FAQs, routing, short answers, high message volumes	⚡ Fastest
Balanced	gemini-2.5-flash Qwen3.6 Plus Kimi K2.5 gpt-5.4-mini Kimi K2.6 Grok 4.3	Medium	Most everyday support & sales	General chatbots, KB Q&A, lead qualification	Fast
Advanced	gpt-5 gemini-2.5-pro gpt-4o gpt-5.4	High	Complex reasoning & nuance	Multi-step reasoning, sensitive/complex queries	Slower

About "thinking" options

Some models — including gpt-5, gpt-5.4, gemini-2.5-pro, and Grok 4.3 — let you set a thinking level (none, low, medium, or high). A higher thinking level makes the model reason more before answering, which can improve accuracy on hard questions but generates more tokens, so it uses more credits. Grok 4.3 appears in the dropdown both on its own and as ready-made low / medium / high thinking presets.

Image recognition needs a vision-capable model

If you want your agent to understand images customers send — for example a photo of a product, a screenshot, or a document — enable Process Images in the agent settings and choose a model that supports vision. Not every model can "see" images; sending an image to a text-only model won't work.

NOTE: processing an image consumes credits, and images generally use more input tokens than text

Models that support image recognition

• gpt-4o, gpt-4o-mini

• gpt-5, gpt-5-mini

• gpt-5.4, gpt-5.4-mini

• gemini-2.5-flash, gemini-2.5-pro

• Grok 4.3

If your agent uses Tools or Actions, for example checking an order status, booking a slot, calling an external API, or extracting structured data, the model has to reliably decide when to call a tool, which one, and how to fill in the details. This is harder than plain chat, and lightweight models are noticeably less reliable at it.

We recommend a Balanced or Advanced model for any agent that uses Tools or Actions.

How much do models actually use? (example)

Credit use depends on your prompts, knowledge base size, and conversation length. To give a rough sense, here's the approximate cost of a single reply that reads ~1,500 tokens of context and produces a short (~250-token) answer:

Model	Tier	Approx. credits per reply*
DeepSeek-V4-Flash	Lightweight	~0.3
gpt-4o-mini	Lightweight	~0.4
gpt-5.4-nano	Lightweight	~0.6
gpt-oss-120b	Lightweight	~0.7
gpt-5-mini	Lightweight	~0.9
gemini-2.5-flash	Balanced	~1.1
Qwen3.6 Plus	Balanced	~1.5
Kimi K2.5	Balanced	~1.7
gpt-5.4-mini	Balanced	~2.3
Kimi K2.6	Balanced	~2.4
Grok 4.3	Balanced	~2.5
gpt-5	Advanced	~4.4
gemini-2.5-pro	Advanced	~4.4
gpt-4o	Advanced	~6.3
gpt-5.4	Advanced	~7.5

*Approximate estimates. Real usage varies with your conversation length, knowledge base, Tool calls, Actions, Analyze, and how much the model reasons.

Which model should I choose?

Pick based on what your agent does:

• Handling FAQs, simple lookups, or routing to the right team?

→ Start with a lightweight model. It's fast, cheap, and accurate enough for straightforward tasks.

• General customer support or sales over your knowledge base?

→ Use a balanced model. This is the best starting point for most agents.

• Complex, multi-step questions, nuanced judgment, or high-stakes replies?

→ Use an advanced model, and only where you need it.

• Very high message volume and tight on credits?

→ Favour lightweight models and keep answers concise.

A note on accuracy

Accuracy isn't decided by the model alone. For most Gallabox agents, the biggest factors are:

• The quality of your knowledge base — clear, well-structured, up-to-date content matters more than the model.

• Your instructions / prompt — specific, unambiguous instructions improve every model.

• Your use case — a lightweight model can be more than accurate enough for simple tasks, while complex reasoning benefits from an advanced model.

Because of this, we recommend testing 2–3 models with your own knowledge base and comparing the answers before committing.

Tips to reduce credit usage

• Match the model to the task — don't use an advanced model for simple FAQs.

• Keep your knowledge base focused — smaller, relevant context means fewer input tokens per reply.

• Ask for concise answers in your agent's instructions — shorter replies use fewer output tokens.

• Use lower reasoning effort where the model supports it, for simpler tasks.

• Monitor your AI Wallet — review your usage history to see how each agent and model consumes credits, and adjust.

Frequently asked questions

What is a token?

A token is a small chunk of text — roughly 4 characters, or about ¾ of a word. Models read and write in tokens, and credits are charged per 1,000 tokens (≈ 750 words). Longer prompts, bigger knowledge bases, and longer answers all use more tokens.

Which model uses the fewest credits?

Lightweight models (like the "nano" and "mini" options) use the fewest. They're ideal for simple, high-volume tasks.

Which model is the most accurate?

It depends on your use case and knowledge base. Advanced models handle complex reasoning best, but for everyday support a balanced model is usually accurate enough — and cheaper.

Can I change my agent's model later?

Yes. You can switch models anytime and test which works best for your needs.

Will a more expensive model always give better answers?

No. For simple tasks, a lightweight model can perform just as well at a fraction of the credit cost. Test before assuming you need the most advanced option.