Most comparisons between language models date within months — a "model A versus B" piece is published, then a new release flips the result. So this guide does not recommend a specific model. It gives you evaluation and testing criteria that stay valid however the names change.
The short answer: there is no absolute "best" model. Test the candidates on your own data and decide by results rather than published comparisons.
First: a decision that comes before choosing a model
Before asking which model, ask: do you need a language model at all?
| Your need | The right solution |
|---|---|
| A limited set of repeated questions | An FAQ page or rule-based bot |
| Routing customers to the right department | Simple menus |
| Searching your documents | Text search + a model for phrasing |
| Understanding free-form phrasing and answering from it | A language model |
See our chatbot cost guide — many cases are solved by a rule-based bot at a tenth of the cost.
Six selection criteria
1. Arabic quality — and dialect specifically
This is the most important criterion in our market, and the one global comparisons overlook.
Test specifically:
- Formally written Modern Standard Arabic.
- Your actual audience's dialect — Gulf, Egyptian, Levantine.
- Arabizi (Arabic in Latin script: "3ayez a3rf").
- Mixing Arabic and English within one sentence.
- Common spelling variations (hamza forms, taa marbuta).
Modern models handle this well generally, but with variation. Don't assume — test with examples from your real customer conversations.
2. Context window size
The context window is how much text a model can "read" in a single request. It matters if you pass long documents or extended conversation history.
In practice: most customer service applications don't need enormous windows — if you're passing an entire document on every request, the problem is in the design rather than the model. Use a search step that extracts only the relevant passages.
3. Cost per conversation
Prices are calculated per million "tokens" (text units), and vary between providers and versions while changing continuously.
Calculate like this:
conversation cost ≈ (input tokens + output tokens) × model price
An important point for Arabic: Arabic text consumes more tokens than English for the same meaning. Don't rely on estimates based on English text — measure with real Arabic text from your domain.
A practical test: take 20 real conversations from your logs, run them through the candidate models, and calculate actual cost. That figure is more accurate than any theoretical estimate.
4. Response latency
In a live conversation, the difference between two seconds and six is a genuine experience difference.
Smaller models are faster, cheaper, and less capable. Many applications run on a small model with sufficient quality — don't pay for capability you don't need.
A useful common pattern: a small fast model for common questions, escalating to a stronger one for complexity.
5. Privacy and data residency
This may settle the decision before any technical criterion.
If your assistant processes Saudi customer data, the Personal Data Protection Law applies — including restrictions on transferring data outside the Kingdom.
Ask every provider (and review their published privacy documentation, such as Anthropic's Trust Center):
- Where is data processed geographically?
- Are our inputs used to train models? (The answer should be no, or disableable.)
- What is the data retention period?
- Is a regional data region available?
Do not pass sensitive personal data to any model before verifying these points with your legal advisor.
6. Stability and version changes
Models are updated and retired. Ask about version policy: can you pin a specific version? How much notice before one is discontinued?
A design rule: build your system so the model can be swapped by changing one setting, not by rewriting. This protects you from price and capability changes, and lets you test alternatives easily.
How to test for yourself
This is the most important section — because it stays valid however the models change.
1. Gather 30–50 real cases from your customer conversations, covering easy, hard, and ambiguous.
2. Write the correct answer for each — this is your evaluation reference.
3. Run them through every candidate model with exactly the same instructions (prompt).
4. Score on three criteria: accuracy (is the information correct?), linguistic quality (is the Arabic sound and in the right dialect?), and grounding (did it stick to your sources or invent?).
5. Measure actual cost and latency for each model on the same sample.
The result: a decision built on your own data, not a general comparison that may not resemble your case at all.
Reducing cost without reducing quality
- Shorten the system prompt. It is sent with every request, so each extra word is paid thousands of times.
- Pass only relevant excerpts, not entire documents.
- Cache answers to repeated questions — no need to call the model for the same question a hundred times.
- Use a smaller model for simple tasks and escalate only when needed.
- Cap answer length — long answers cost more and are rarely better.
Related reading
-
AI for business — the decision that comes before choosing a model.
-
Arabic document processing — models applied to Arabic documents.
-
AI chatbot cost — upfront and operational cost in detail.
-
PDPL compliance — before passing any data to an external model.
-
API security — never put model keys in the app.
Frequently asked questions
Which model is best for Arabic?
There is no fixed answer — models evolve quickly and any specific recommendation dates within months. The sounder approach is to test candidates on data from your domain and your audience's dialect using the methodology above. Modern models are generally good at Arabic; differences show up in dialects and specialised terminology.
What does running an AI assistant cost monthly?
It depends on conversation volume, length, and the model used. See our chatbot cost guide for the line items. The rule: calculate with a real sample of your conversations rather than a theoretical estimate — and Arabic text consumes more tokens than English.
Is my data safe with model providers?
It varies by provider and plan. Ask explicitly about processing location, use of data in training, and retention period. Do not pass sensitive personal data before verifying compliance with the Personal Data Protection Law with your legal advisor.
What if the model gives wrong information?
This is the primary risk. Three practices reduce it: restricting the model to answer only from your approved content, setting a confidence threshold below which it hands off to a human, and periodically reviewing conversation logs. Human handoff is a necessity, not an optional extra.
Can I switch models later?
Yes if you design for it — make the model a swappable setting rather than something embedded in code. You will usually need to retune the prompt and retest, but the architecture holds. This is the single most important design decision in the project.
Do I need a custom model trained on my data?
Rarely. Most cases are solved by a general model plus search over your documents (RAG) — cheaper, faster, and easier to update. Custom training earns its cost only with large, highly specialised datasets and a budget that justifies it.
Conclusion
Don't pick a model based on a published comparison — models change faster than articles.
Test on your data: 30–50 real cases, in your audience's dialect, with identical instructions, scored on accuracy, language, and grounding.
Verify privacy and data residency first if you process Saudi customer data.
And design the system so the model is swappable — that protects you from every change to come.
Want help evaluating the options for your case? Get in touch for a free assessment — including a straight opinion if a simple rule-based bot would serve you. See our AI solutions services.