75Fit score
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Model details →Best AI models for translation
Modern frontier LLMs beat dedicated MT systems on most language pairs. The picks below reflect both quality and breadth of language coverage.
67Fit score
Gemma 3n E4B-it is optimized for efficient execution on mobile and low-resource devices, such as phones, laptops, and tablets. It supports multimodal inputs—including text, visual data, and audio—enabling diverse tasks s…
Model details →67Fit score
No description provided yet.
Model details →Top 25 models for language translation
| # | Model | Provider | Context | Input price / 1M | Tier |
|---|---|---|---|---|---|
| 1 | google-t5/t5-small | Hugging Face | Unknown Unknown context | Custom | Variable |
| 2 | Google: Gemma 3n 4B (free) | 8K Short/standard context | $0.00 | Budget | |
| 3 | google-t5/t5-base | Hugging Face | Unknown Unknown context | Custom | Variable |
| 4 | Mistral: Voxtral Small 24B 2507 | Mistral AI | 32K Short/standard context | $0.10 | Budget |
| 5 | Cohere: Command A | Cohere | 256K Long context (128K+) | $2.50 | Standard |
| 6 | Cohere: Command R (08-2024) | Cohere | 128K Long context (128K+) | $0.15 | Budget |
| 7 | Google: Gemma 3n 2B (free) | 8K Short/standard context | $0.00 | Budget | |
| 8 | BAAI/bge-reranker-v2-m3 | Hugging Face | Unknown Unknown context | Custom | Variable |
| 9 | baidu/Unlimited-OCR | Hugging Face | Unknown Unknown context | Custom | Variable |
| 10 | cross-encoder/mmarco-mMiniLMv2-L12-H384-v1 | Hugging Face | Unknown Unknown context | Custom | Variable |
| 11 | deepseek-ai/DeepSeek-OCR | Hugging Face | Unknown Unknown context | Custom | Variable |
| 12 | FacebookAI/xlm-roberta-base | Hugging Face | Unknown Unknown context | Custom | Variable |
| 13 | FacebookAI/xlm-roberta-large | Hugging Face | Unknown Unknown context | Custom | Variable |
| 14 | google-bert/bert-base-multilingual-cased | Hugging Face | Unknown Unknown context | Custom | Variable |
| 15 | google-bert/bert-base-multilingual-uncased | Hugging Face | Unknown Unknown context | Custom | Variable |
| 16 | intfloat/multilingual-e5-base | Hugging Face | Unknown Unknown context | Custom | Variable |
| 17 | intfloat/multilingual-e5-large | Hugging Face | Unknown Unknown context | Custom | Variable |
| 18 | intfloat/multilingual-e5-small | Hugging Face | Unknown Unknown context | Custom | Variable |
| 19 | jinaai/jina-embeddings-v3 | Hugging Face | Unknown Unknown context | Custom | Variable |
| 20 | meta-llama/Llama-3.2-1B-Instruct | Meta | 60K Short/standard context | $0.03 | Budget |
| 21 | meta-llama/Llama-3.2-3B-Instruct | Meta | 131K Long context (128K+) | $0.05 | Budget |
| 22 | microsoft/mdeberta-v3-base | Hugging Face | Unknown Unknown context | Custom | Variable |
| 23 | Qwen: Qwen2.5-VL 7B Instruct | Qwen | 33K Short/standard context | $0.20 | Budget |
| 24 | Qwen: Qwen3 30B A3B Instruct 2507 | Qwen | 262K Long context (128K+) | $0.05 | Budget |
| 25 | ResembleAI/chatterbox | Hugging Face | Unknown Unknown context | Custom | Variable |
Frequently asked questions
Are LLMs better than dedicated translation systems?
For high-resource languages, yes — frontier LLMs match or beat NMT on quality and beat them on context awareness. For very low-resource languages, dedicated systems still help.
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