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DeepSeek V3 vs embed-english-v3.0

Bottom line

DeepSeek V3 is open source and embed-english-v3.0 is not. You can run DeepSeek V3 on your own machine or server; embed-english-v3.0 is hosted by the vendor.

Which one should you try?

DeepSeek V3

Freemium

Best for Cost-sensitive organizations requiring frontier-class performance

Visit DeepSeek V3 See full review

embed-english-v3.0

Freemium

Best for English-only semantic search applications

Visit embed-english-v3.0 See full review

Pricing, capabilities and platforms side by side. Prices are as of the "Pricing checked" date in the table.

DeepSeek V3

DeepSeek V3

A fast, chat-based, Mixture-of-Experts (MoE) model from DeepSeek.

embed-english-v3.0

embed-english-v3.0

embed-english-v3.0 generates semantic embeddings from English text, producing 1,024-dimensional vectors suitable for retrieval-augmented generation, classification, clustering, and semantic search tasks. It achieves state-of-the-art performance on MTEB and BEIR benchmarks and was trained on approximately 1 billion English training pairs. The model supports batches of up to 96 inputs with 512 tokens maximum per input, and supports both text and image embedding. Pricing is $0.10 per million tokens. A notable limitation is that it requires explicit input_type specification to differentiate between search documents, queries, classification, and clustering tasks.

AttributeDeepSeek V3embed-english-v3.0
PricingFreemiumFreemium
Price$0.14 per million input tokens and $0.28 per million output tokens$0.10 per million tokens
Pricing checkedNot checked yetNot checked yet
Free trialNot statedNot stated
Open sourceYesNo
Has APIYesYes
Self-hosted optionYesNo
PlatformsHugging Face, GitHub, DeepSeek API, multiple cloud providers (Cerebras, DeepInfra, Together, OpenRouter, Fireworks, Hyperbolic, SambaNova)Cohere API, AWS SageMaker, Azure AI Foundry, OCI Generative AI
LanguagesSupports multiple languages, allowing input and output in several languagesEnglish (primary); text-image multimodal support
Released2024-12-262024
Pros
  • Cost-effective at $0.27 per million input tokens and $1.10 per million output tokens
  • Fast throughput at approximately 60 tokens per second, 3x faster than DeepSeek-V2
  • Fully open-source weights available under MIT License for local deployment
  • Performance comparable to GPT-4 and Claude 3.5 Sonnet
  • Outperforms other open-source models across multiple benchmarks
  • State-of-the-art performance on MTEB and BEIR benchmarks
  • Highly cost-efficient at $0.10 per million tokens
  • Supports multimodal input (text and images) with unified embeddings
  • Batch processing up to 96 inputs per request
  • Multiple embedding output formats (float, int8, uint8, binary, base64)
Cons
  • Context window significantly smaller than some competitors
  • Does not support tool calling (functions)
  • Does not support vision capabilities
  • English-optimized only; use embed-multilingual-v3.0 for multilingual needs
  • 512-token limit per input may truncate long documents
  • Requires explicit input_type specification for optimal results

Made up your mind?

DeepSeek V3

Freemium

Best for Cost-sensitive organizations requiring frontier-class performance

Visit DeepSeek V3 See full review

embed-english-v3.0

Freemium

Best for English-only semantic search applications

Visit embed-english-v3.0 See full review

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Specs come from each tool's own site and docs (how we test). AIDiveForge is editorially independent.

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