Multilingual AI models: all 239 handle more than English

Multilingual
Services found: 239
#AI modelCapabilitiesAPI $/1M (in / out)Context windowPrice / moOutput $/1MOverviewVisit site
121

Tencent HY Vision 1.5 Instruct

Tencent
Text & chatVisionMultilingual
API onlyView
122

Doubao Seed Evolving

ByteDance
Text & chatReasoningTool calling
API onlyView
123

Doubao Seed 2.1 Turbo

ByteDance
Text & chatReasoningTool calling
API onlyView
124

Doubao Seed 2.0 Code

ByteDance
Text & chatVisionCoding
API onlyView
125

Doubao Seed 2.0 Lite

ByteDance
Text & chatVisionReasoning
API onlyView
126

Doubao Seed 2.0 Mini

ByteDance
Text & chatVisionReasoning
API onlyView
127

Doubao Seed Vision

ByteDance
Text & chatVisionReasoning
API onlyView
128

Hy3

Tencent
Text & chatMultilingualProprietary API
$0.132 / $0.528262.1K tokensAPI only0.528View
129

GLM 5.2

Z.ai
Text & chatReasoningTool calling
$0.966 / $3.0361.05M tokensAPI only3.036View
130

Solar Pro 4

Upstage
Text & chatReasoningTool calling
$0.30 / $1.20524.3K tokensAPI only1.2View
131

Laguna S 2.1

Poolside
Text & chatCodingLong context 1M+
$0.09 / $0.181.05M tokensAPI only0.18View
132

Ling 3.0 Flash Fin

InclusionAI
Text & chatVisionLow latency
$0.06 / $0.18262.1K tokensAPI only0.18View
133

DeepSeek V3.2

DeepSeek
Text & chatStructured JSON outputPrompt caching
$0.269 / $0.4163.8K tokensAPI only0.4View
134

Step 3.7 Flash

Stepfun
Text & chatVisionVideo
$0.2 / $1.15262.1K tokensAPI only1.15View
135

GPT-5.6 Luna Pro

OpenAI
Text & chatVisionReasoning
$0.2 / $1.21.05M tokensAPI only1.2View
136

Gemma 4 26B A4B

Google
Text & chatVisionVideo
$0.07 / $0.34262.1K tokensAPI only0.34View
137

Muse Spark 1.3

Meta
Text & chatVisionAudio
$1.25 / $4.251.05M tokensAPI only4.25View
138

Qwen3.7 Flash

Qwen
Text & chatVisionVideo
$0.03 / $0.131M tokensAPI only0.13View
139

Ling 3.0 Flash Sante

InclusionAI
Text & chatVisionLow latency
$0 / $0262.1K tokensAPI only0View
140

Qwen3.8 Flash

Qwen
Text & chatVisionVideo
$0.15 / $0.471M tokensAPI only0.47View

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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