Multilingual AI models: all 239 handle more than English

Multilingual
Services found: 239
#AI modelCapabilitiesAPI $/1M (in / out)Context windowPrice / moOutput $/1MOverviewVisit site
221

GPT-5.4 Pro

OpenAI
Text & chatVisionReasoning
$30 / $1801.05M tokensAPI only180View
222

Llama 4 Maverick

Meta
Text & chatVisionLong context 1M+
$0.2 / $0.6961.05M tokensAPI only0.696View
223

Llama 4 Scout

Meta
Text & chatVisionLong context 1M+
$0.1 / $0.310M tokensAPI only0.3View
224

Mistral Large 3

Mistral AI
Text & chatVisionReasoning
$0.5 / $1.5256K tokensAPI only1.5View
225

Qwen3.8 Omni Flash

Qwen
Text & chatVisionAudio
1M tokensAPI onlyView
226

MiMo-V2.6-Pro

Xiaomi
Text & chatVisionAudio
$0.435 / $0.871M tokensAPI only0.87View
227

MiMo-V2.6-Flash

Xiaomi
Text & chatVisionAudio
$0.14 / $0.281M tokensAPI only0.28View
228

MiMo-V2.6-Pro-UltraSpeed

Xiaomi
Text & chatVisionAudio
$4.35 / $8.71M tokensAPI only8.7View
229

Hy4 preview

Tencent
Text & chatReasoningAdjustable thinking
$0.84 / $2.521M tokensAPI only2.52View
230

Fugu Max

Sakana
Text & chatReasoningAdjustable thinking
$2 / $6API only6View
231

Aion 3.5

Aion Labs
Text & chatReasoningPrompt caching
$3 / $6256K tokensAPI only6View
232

Aion 3.5 Mini

Aion Labs
Text & chatReasoningPrompt caching
$0.7 / $1.4256K tokensAPI only1.4View
233

Solar Mini 4

Upstage
Text & chatReasoningAdjustable thinking
$0.1 / $0.4512K tokensAPI only0.4View
234

Perceptron Mk1.5

Perceptron
Text & chatVisionAudio
$0.15 / $1.536.9K tokensAPI only1.5View
235

Ling 3.0 Flash VL

InclusionAI
Text & chatVisionVideo
$0.02 / $0.059256K tokensAPI only0.059View
236

Pareto

Unbiased
Text & chatVisionPrompt caching
$0.8 / $3.2API only3.2View
237

Ternary Bonsai 2 27B

PrismML
Text & chatVisionReasoning
262.1K tokensAPI onlyView
238

Schematron V2 Turbo

Inference.net
Text & chatStructured JSON outputPrompt caching
$0.03 / $0.15125K tokensAPI only0.15View
239

Schematron V2 Small

Inference.net
Text & chatStructured JSON outputPrompt caching
$0.05 / $0.23125K tokensAPI only0.23View

Browse by need

Multilingual capability is now table stakes: all 239 models in this rating handle languages beyond English at production quality, Arabic included. The interesting question is no longer whether a model speaks your language but how efficiently it tokenises it.

Non-Latin scripts consume more tokens per word than English, so the same Arabic text costs more to process than its English equivalent. That makes the cheap end of this list - output from $0.25 per million - disproportionately attractive for high-volume multilingual work, and it makes prompt caching worth checking if your system prompt is long.

Tokenisation, not fluencyEvery model speaks the language; the difference is how many tokens it burns doing it.
Arabic costs moreNon-Latin scripts inflate token counts, so the same text bills higher than its English version.
Caching helps most hereA long multilingual system prompt is exactly what prompt caching was designed for.

Models with this capability

How to choose

  • Measure cost on real text in your language, not on English benchmarks.
  • Check output quality on dialect and register, not just on literal accuracy.
  • Prefer models with a free tier so you can test on your own corpus before paying.
  • For regulated translation, compare against a dedicated translation model.

Common mistakes

Assuming English pricing appliesA budget that works in English can overrun by half again in Arabic.
Skipping native reviewFluent output hides subtle register errors that only a native reader catches.

Who this suits

Gulf-facing productsArabic and English in one interface, with pricing that survives both.
Global supportOne model covering the whole ticket queue instead of per-language routing.
Content teamsDrafting in one language and adapting rather than translating.

Frequently asked questions

Does Arabic cost more than English?

Yes. Non-Latin scripts use more tokens per word, so the same text bills higher. Budget models and prompt caching offset it.

Which models are strongest in Arabic?

The frontier tier handles it best, and regional families — Qwen, GLM, Falcon — are trained with heavier non-English weighting.

Is a translation model better than a multilingual one?

For contracts and regulated text, yes. For everyday work a general mid-tier model is cheaper and good enough.

Which languages are weakest?

Low-resource languages remain uneven across all vendors. Test rather than assume.

Does a bigger model speak better?

Usually yes on nuance, but the gap narrows for straightforward everyday text.

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