Why Model Choice Matters
Most chatbot users don't think about the model under the hood — they think about results: "Does the bot answer correctly? Quickly? Does it understand my language?"
Model choice directly affects all of this. Different models deliver different answer quality, operate at different speeds, and cost different amounts. If you're building a chatbot on an API, this decision has a direct impact on UX and unit economics.
GPT-4o (OpenAI)
Strengths:
- Best reasoning quality and instruction-following
- Excellent multilingual performance
- Multimodal (text + images)
- Massive integration ecosystem
Weaknesses:
- More expensive than DeepSeek and Gemini Flash
- Requires an OpenAI API account
Cost: $5 per 1M input tokens, $15 per 1M output tokens (GPT-4o).
When to choose: when you need maximum answer quality and complex scenarios (legal, medical, technical questions).
Gemini 1.5 Pro (Google)
Strengths:
- Massive context window (up to 1M tokens) — ideal for long documents
- Good quality at a moderate price
- Gemini Flash is very fast and cheap
Weaknesses:
- Slightly weaker on instruction-following consistency than GPT-4o
- Less predictable for complex structured outputs
Cost: Gemini 1.5 Flash — $0.075 per 1M input tokens (very cheap).
When to choose: when working with long documents or when you need high speed at low cost.
DeepSeek V3 / R1
Strengths:
- Very low cost: $0.27 per 1M input tokens (V3)
- Excellent quality for the price
- Open weights — can be run self-hosted
Weaknesses:
- Servers in China — data localisation compliance considerations
- Less consistent on complex instructions
- Weaker on low-resource languages
Cost: $0.27–$0.55 per 1M input tokens.
When to choose: when budget is constrained and questions are straightforward (FAQ, standard instructions).
Comparison Table
| GPT-4o | Gemini 1.5 Pro | DeepSeek V3 | |
|---|---|---|---|
| Answer quality | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ |
| Multilingual | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐ |
| Speed | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ (Flash) | ⭐⭐⭐⭐ |
| Price | $$ | $ (Flash) | $ |
| Context length | 128K | 1M | 64K |
| Self-hosted | ❌ | ❌ | ✅ |
What Auralix Uses
Auralix uses GPT-4o as its primary model — for maximum answer quality and reliable multilingual performance. The model choice is made for you.
If you're building your own chatbot on an API and want to choose a model yourself, use the table above as a reference.
FAQ
Can I switch models in Auralix? In the current version, the model is selected automatically. User-selectable models are on the roadmap.
Is it worth paying for GPT-4o when DeepSeek is cheaper? It depends on the task. For simple FAQ-style questions — DeepSeek handles them well. For nuanced, complex conversations — GPT-4o produces noticeably better results.
How does the model affect hallucinations? All models hallucinate without RAG. With a connected knowledge base, hallucinations drop sharply regardless of the model — the agent answers from documents rather than inventing.
Are there open-source alternatives worth considering? Llama 3.1 (Meta) and Mistral are strong open-source options for self-hosted deployments. Quality is close to GPT-4o on many tasks, with zero API cost.
Summary
There's no single "best" model — only the right one for your task. GPT-4o when quality is paramount. Gemini Flash when speed and volume matter. DeepSeek when cost is the constraint. For most business chatbots with a knowledge base, the difference is smaller than it seems: RAG normalises answer quality across models.
