Picking Models for Multilingual Apps

If your users span more than one language, model choice gets a new dimension. A model that is excellent in English can be noticeably weaker in, say, Arabic, Hindi, or Vietnamese. Picking the right model for multilingual apps means looking beyond English benchmarks and testing on the languages your users actually speak.

This guide covers what to watch for and how to evaluate models for multilingual workloads on Model Database.

Language coverage is uneven

Models are trained on different data mixes, so their strengths vary by language. Widely represented languages, such as English, Spanish, French, German, and Chinese, tend to be well-supported across most models. Lower-resource languages can show weaker grammar, awkward phrasing, or factual slips. The only reliable way to know how a model performs in a given language is to test it in that language.

Models worth testing

• openai/gpt-4o and anthropic/claude-sonnet-4-6 — strong, broad multilingual coverage across many widely spoken languages.
• qwen/qwen-2.5-72b-instruct — particularly strong on Chinese and several Asian languages, an open-weight option worth benchmarking for those markets.
• mistralai/mistral-large — solid coverage of major European languages.
• google/gemini-2.0-flash — a fast, economical option for high-volume multilingual tasks where the language is well-supported.

These are starting points. Your own evaluation is what counts.

What to evaluate beyond translation

Multilingual quality is more than literal translation. Check that the model:

• Follows instructions in-language — does it obey a system prompt written in the target language, or only in English?
• Preserves tone and formality — many languages encode politeness levels that matter to users.
• Handles script and encoding correctly — non-Latin scripts, right-to-left text, and diacritics should round-trip cleanly.
• Stays factual in-language — capability can drop in lower-resource languages even when fluency looks fine.

A multilingual evaluation loop

Run the same prompts across candidate models in each target language and compare:

from openai import OpenAI

client = OpenAI(base_url="https://modeldatabase.com/v1", api_key="mdb_live_...")

prompts = {
    "es": "Resume este texto en una frase.",
    "ja": "このテキストを一文で要約してください。",
    "ar": "لخص هذا النص في جملة واحدة.",
}
models = ["openai/gpt-4o", "anthropic/claude-sonnet-4-6", "qwen/qwen-2.5-72b-instruct"]

for lang, instruction in prompts.items():
    for m in models:
        resp = client.chat.completions.create(
            model=m,
            messages=[{"role": "user", "content": instruction + " " + sample_text[lang]}],
        )
        print(lang, m, "->", resp.choices[0].message.content)

Have native speakers or a trusted in-language reviewer rate the outputs. Fluency is easy to fake; correctness and natural phrasing are what users notice.

Route by language in production

You may find that no single model is best across all your languages. A clean solution is to route by detected language, since every model sits behind the same endpoint:

BEST_MODEL = {
    "zh": "qwen/qwen-2.5-72b-instruct",
    "de": "mistralai/mistral-large",
    "default": "anthropic/claude-sonnet-4-6",
}

def reply(text, lang):
    model = BEST_MODEL.get(lang, BEST_MODEL["default"])
    return client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": text}],
    )

This gives each user the strongest model for their language without changing your integration.

Track cost across languages

Output length varies by language, some languages are more token-dense than others, which affects cost. Every billable response returns X-MDB-Charged-USD and X-MDB-Balance-USD, so log cost per language to spot where your spend concentrates and whether a cheaper model would serve a given market just as well.

Building for a global audience? Create a key and add credit at your dashboard, list available models with GET /v1/models, and read the docs to set up language-aware routing.