Multilingual AI models: all 239 handle more than English

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

Claude Fable 5.1

Anthropic
Text & chatVisionReasoning
$10 / $501M tokensAPI only50View
2

GPT-6 Astra

OpenAI
Text & chatVisionReasoning
$10 / $501.05M tokensAPI only50View
3

GPT-6.1 Sol

OpenAI
Text & chatVisionReasoning
$2 / $101.05M tokensAPI only10View
4

GPT-6 Sol

OpenAI
Text & chatVisionReasoning
$2 / $101.05M tokensAPI only10View
5

GPT-6 Luna

OpenAI
Text & chatVisionReasoning
$0.1 / $0.51.05M tokensAPI only0.5View
6

Claude Opus 5.5

Anthropic
Text & chatVisionReasoning
$4 / $201M tokensAPI only20View
7

Claude Sonnet 5.5

Anthropic
Text & chatVisionReasoning
$2 / $101M tokensAPI only10View
8

Grok 4.7

xAI
Text & chatVisionReasoning
$2 / $6500K tokensAPI only6View
9

DeepSeek V4.1 Flash

DeepSeek
Text & chatVisionReasoning
$0.3 / $1.21M tokensAPI only1.2View
10

Qwen3.8 Max Prime

Qwen
Text & chatReasoningLong context 1M+
$3.36 / $10.081M tokensAPI only10.08View
11

GLM 5.3 FlashX

Z.ai
Text & chatVisionVideo
$0.37 / $1.251M tokensAPI only1.25View
12

Ember-1

Fireworks AI
Text & chatVisionReasoning
$3 / $151.04M tokensAPI only15View
13

Claude Opus 5

Anthropic
Text & chatVisionReasoning
$5 / $251M tokensAPI only25View
14

Claude Fable 5

Anthropic
Text & chatVisionReasoning
$10 / $501M tokensAPI only50View
15

GPT-5.6 Sol

OpenAI
Text & chatVisionReasoning
$4 / $201.05M tokensAPI only20View
16

GLM 5.3

Z.ai
Text & chatReasoningTool calling
$1.4 / $4.41.31M tokensAPI only4.4View
17

Grok 4.6

xAI
Text & chatVisionReasoning
$2 / $6500K tokensAPI only6View
18

Kimi K3

Moonshot AI
Text & chatVisionVideo
$3 / $151.05M tokensAPI only15View
19

GPT-5.6 Terra

OpenAI
Text & chatVisionReasoning
$2 / $121.05M tokensAPI only12View
20

Claude Opus 4.8

Anthropic
Text & chatVisionReasoning
$5 / $251M tokensAPI only25View

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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