Multilingual AI models: all 239 handle more than English

Multilingual
Services found: 239
#AI modelCapabilitiesAPI $/1M (in / out)Context windowPrice / moOutput $/1MOverviewVisit site
21

GLM 5.3 Flash

Z.ai
Text & chatVisionVideo
$0.075 / $0.251.31M tokensAPI only0.25View
22

Gemini 3.8 Flash

Google
Text & chatVisionAudio
$0.75 / $3.751.05M tokensAPI only3.75View
23

Qwen3.8 Max (0902)

Qwen
Text & chatVisionVideo
$2 / $61M tokensAPI only6View
24

Qwen3.8 2.4T A95B

Qwen
Text & chatReasoningAdjustable thinking
$2 / $61.05M tokensAPI only6View
25

Muse Spark 1.2

Meta
Text & chatVisionAudio
$1.25 / $4.251.05M tokensAPI only4.25View
26

Gemini 3.7 Flash

Google
Text & chatVisionAudio
$0.75 / $3.751.05M tokensAPI only3.75View
27

Grok 4.5

xAI
Text & chatVisionReasoning
$2 / $6500K tokensAPI only6View
28

GPT-5.5

OpenAI
Text & chatVisionReasoning
$5 / $301.05M tokensAPI only30View
29

Claude Sonnet 5

Anthropic
Text & chatVisionReasoning
$2 / $101M tokensAPI only10View
30

GPT-5.6 Luna

OpenAI
Text & chatVisionReasoning
$0.2 / $1.21.05M tokensAPI only1.2View
31

DeepSeek V4 Pro 0813

DeepSeek
Text & chatReasoningAdjustable thinking
$1.0494 / $3.14821.05M tokensAPI only3.148View
32

DeepSeek V4 Flash 0731

DeepSeek
Text & chatVisionReasoning
$0.065 / $0.181.31M tokensAPI only0.18View
33

Gemini 3.6 Flash

Google
Text & chatVisionAudio
$0.75 / $3.751.05M tokensAPI only3.75View
34

Muse Spark 1.1

Meta
Text & chatVisionAudio
$1.25 / $4.251.05M tokensAPI only4.25View
35

Qwen3.8 27B

Qwen
Text & chatVisionVideo
$0.42 / $31M tokensAPI only3View
36

DeepSeek V4 Pro 0423

DeepSeek
Text & chatReasoningAdjustable thinking
$0.95526 / $1.910521.05M tokensAPI only1.911View
37

Claude Sonnet 4.6

Anthropic
Text & chatVisionReasoning
$3 / $151M tokensAPI only15View
38

Qwen3.7 Max

Qwen
Text & chatReasoningAdjustable thinking
$2.5 / $7.51M tokensAPI only7.5View
39

MiniMax M3

MiniMax
Text & chatVisionVideo
$0.3 / $1.21.05M tokensAPI only1.2View
40

GLM-5.1

Z.ai
Text & chatReasoningTool calling
$1.4 / $4.4200K tokensAPI only4.4View

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.

Frequently searched

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