An up-to-date comparison of the main AI APIs: costs, pros and cons of OpenAI, Claude, Gemini, Llama, and DeepSeek to make more informed decisions in 2026.
In short:
- OpenAI → best overall balance
- Claude → best for quality and coding
- Gemini → best for large volumes and long context
- DeepSeek → cheapest plug-and-play option
- Llama (on-prem) → maximum control, but more complexity
A few days ago I was talking to a friend who works in IT at an SME in the Oil & Gas sector. He’s building his own RAG application for document management and, at one point, asked me a very simple question:
“Do you have something concrete — an article, a link — to understand which AI API is really worth using between OpenAI, Anthropic, Gemini, Llama, and low-cost alternatives? Maybe with some cost indications too?”
A fair question. And not a trivial one.
Because there’s no shortage of material online. In fact, there’s too much. The problem is that it often:
- isn’t up to date
- stays too theoretical
- or doesn’t really answer what you need when you have to make a decision
In this specific case: RAG applications, document management, and real-world enterprise environments.
That’s where this comes from: trying to put together a useful synthesis. Not perfect, but based on what I’m actually seeing work (and not work) in practice.
The comparison (numbers included)
Indicative costs per million tokens (input/output). Meant for orientation, not precise accounting.
| Provider | Costs (€) | Pros | Cons | Official pricing |
|---|---|---|---|---|
| OpenAI (GPT-5, o3 mini) | GPT-5.4: ~€2.3 / €13.8 Mini/Nano: ~€0.14–0.70 / €0.55–4.10 | Overall balance, integration, development speed | Not the cheapest, less accurate than Claude on complex tasks | OpenAI API Pricing |
| Anthropic (Claude 4.6) | Sonnet: ~€2.8 / €13.8 Opus: ~€4.6 / €23 | Coding, complex reasoning, clean outputs | Higher cost | Claude Pricing |
| Gemini (3.1 Flash/Pro) | Flash: ~€0.09–0.28 / €0.35–2.30 Pro: ~€1.8 / €11 | Long context, large volumes, document analysis | Less consistent quality | Google AI Pricing |
| DeepSeek | ~€0.05–0.20 / €0.20–1.50 | Very cheap, easy to integrate | Less stable on complex tasks | DeepSeek Pricing |
| Llama (on-prem / open) | Variable (infrastructure + hosting) | Full control, no lock-in, low cost at scale | Complex setup, infrastructure management, tuning | N/A (self-hosted) |
The cost per token is only part of the story. The real cost emerges when you start using these models in practice.
If you look only at price per token, you risk making the wrong decisions.
What really matters is:
- how many times you need to retry a request
- how much you need to fix the output
- how much logic you need to build around it
In other words: how much it costs you to reach a usable result.
The most common mistake is choosing the cheapest model per token without considering how many iterations are needed to get an acceptable output.
Patterns I’m seeing in practice
- Claude costs more, but often gets it right faster
On complex tasks (especially coding or structured reasoning), it tends to produce more accurate outputs on the first attempt. That means fewer iterations, less debugging, and less time spent fixing things. The cost per call is higher, but the total cost often isn’t. - OpenAI is the most predictable, and that matters in production
It’s not always the absolute best, but it’s the most consistent. Responses are more stable, integrations work well, and the ecosystem is mature. When you move to production, that predictability reduces issues and surprises. - Gemini becomes convenient at scale
On small tasks, the difference isn’t huge. But when you start working with large datasets (long documents, RAG, large contexts), cost per token and context handling start to matter. - DeepSeek is the cheapest, but with more trade-offs
Great for prototyping and high-volume use. But on complex tasks, it requires more control and fallback mechanisms. - Llama (on-prem) reduces costs long-term, but increases complexity
Here you’re not just choosing a model, but an architecture. Llama can run locally using runtimes like Ollama, vLLM, or similar stacks. It works well when you have high volumes and in-house expertise; otherwise, costs shift to infrastructure and management.
This isn’t a ranking. It’s more a map of trade-offs. A more expensive model that gets it right on the first attempt can end up being cheaper overall.
Lately, I’ve been relying less and less on a single provider and more on a simple routing approach across different models: cheaper ones for repetitive tasks, classification, and bulk processing, and more advanced models for critical steps, decision-making, and final output generation. There’s no need for a complex architecture — a clear separation of roles between models is often enough to significantly impact both real costs and the quality of the results.
Conclusion
Until recently, it made sense to ask which model was the best.
Today, it makes more sense to ask: where does this model stop being convenient?
Because that’s where the real costs begin.
The cheapest model isn’t the one that costs less — it’s the one that gets you to a usable result faster.
And what are you using these days?
I’m curious to hear what’s actually working — and what’s not — out in the real world.
