免费 Embedding 模型 API 盘点
Embedding（向量化）是 RAG、语义搜索、推荐系统的基石。本文盘点当前可通过 API 免费调用的 Embedding 模型，帮你选型不踩坑。
为什么需要 Embedding API？
Embedding 把文本映射为固定维度的浮点向量，使得语义相近的内容在向量空间中距离更近。核心应用场景包括：
- RAG（检索增强生成）：将知识库文档向量化后存入向量数据库，查询时用向量相似度召回相关片段
- 语义搜索：摆脱关键词匹配的限制，按语义召回结果
- 聚类与分类：对大量文本自动分组或打标签
- 推荐系统：基于内容相似度做推荐
评估维度
选型时关注以下指标：
| 维度 |
说明 |
| 向量维度 |
384 / 768 / 1024 / 1536 / 3072，维度越高信息量越大但存储成本也更高 |
| 上下文窗口 |
单次输入最大 token 数，从 512 到 8192 不等 |
| 多语言支持 |
是否原生支持中文等多语言，还是以英文为主 |
| 速率限制 |
每分钟请求数 / token 数限制 |
| MTEB 得分 |
Massive Text Embedding Benchmark 综合排名 |
1. OpenAI text-embedding-3-small（免费层）
- 维度：1536（可降维至 256/512）
- 上下文：8191 tokens
- 多语言：支持 100+ 语言，中文表现优秀
- 速率：通过 APIShare 统一调用可共享免费额度
- 特点：业界标杆，降维参数
dimensions 可按需调整，兼顾效果与存储成本
import openai
client = openai.Client(
base_url="https://apishare.cc/v1",
api_key="your-token"
)
resp = client.embeddings.create(
model="text-embedding-3-small",
input="向量化这段文本",
dimensions=512 # 可选降维
)
2. Hugging Face Inference API — BGE 系列
- 模型：
BAAI/bge-large-en-v1.5、BAAI/bge-m3
- 维度：1024
- 上下文：512 tokens（bge-large）/ 8192 tokens（bge-m3）
- 多语言：bge-m3 原生支持中英日韩等 50+ 语言
- 速率：免费层 1,000 次/天（Inference API）
- 特点：bge-m3 同时输出稠密向量 + 稀疏向量 + ColBERT 多向量，一站式覆盖多种检索策略
3. NVIDIA NIM — NV-Embed-v2
- 模型：
nvidia/nv-embed-v2
- 维度：4096
- 上下文：32768 tokens
- 多语言：以英文为主
- 速率：NIM 免费层 1,000 次/天
- 特点：MTEB 排名长期 Top 3，超长上下文窗口适合整篇文档向量化
4. Jina AI — jina-embeddings-v3
- 模型：
jinaai/jina-embeddings-v3
- 维度：1024
- 上下文：8192 tokens
- 多语言：支持 89 种语言
- 速率：免费 API key 每月 100 万 tokens
- 特点：支持任务类型参数（
task=retrieval.passage / task=retrieval.query），针对不同场景优化向量质量
5. Nomic AI — nomic-embed-text-v1.5
- 模型：
nomic-ai/nomic-embed-text-v1.5
- 维度：768
- 上下文：8192 tokens
- 多语言：以英文为主
- 速率：完全开源，可在本地部署；Nomic Atlas 免费层提供 API
- 特点：可解释性强，支持 Matryoshka 降维，模型权重完全开放
6. Voyage AI — voyage-3 / voyage-3-lite
- 模型：
voyage-3 / voyage-3-lite
- 维度：1024（voyage-3）/ 512（lite）
- 上下文：32000 tokens
- 多语言：支持中英文等 100+ 语言
- 速率：免费层 50M tokens / 月
- 特点：超长上下文 + MTEB 得分领先，适合法律、学术等长文档场景
7. Mixedbread — mxbai-embed-large-v1
- 模型：
mixedbread-ai/mxbai-embed-large-v1
- 维度：1024
- 上下文：512 tokens
- 多语言：以英文为主
- 速率：Hugging Face Inference API 免费层可调用
- 特点：MTEB 得分排名前列，模型仅 670M 参数，部署成本低
选型建议
| 场景 |
推荐模型 |
理由 |
| 中文 RAG |
OpenAI text-embedding-3-small / BGE-m3 |
中文效果最好 |
| 超长文档 |
NVIDIA NV-Embed-v2 / Voyage-3 |
上下文窗口 32K+ |
| 成本敏感 |
Nomic-embed / mxbai-embed |
开源可本地部署 |
| 多语言 |
Jina-v3 / BGE-m3 |
覆盖 50+ 语言 |
| 低延迟 |
OpenAI text-embedding-3-small |
API 响应最快 |
通过 APIShare 统一调用
以上模型可通过 APIShare 统一入口调用，客户端只需维护一个 token，无需为每家供应商单独注册账号：
models = [
"text-embedding-3-small", # OpenAI
"BAAI/bge-m3", # Hugging Face
"nvidia/nv-embed-v2", # NVIDIA NIM
"jinaai/jina-embeddings-v3", # Jina
]
for model in models:
resp = client.embeddings.create(model=model, input=text)
print(model, len(resp.data[0].embedding))
总结
免费 Embedding API 的生态在 2026 年已非常成熟。中文场景首选 BGE-m3 或 OpenAI text-embedding-3-small；长文档场景选 NVIDIA NV-Embed-v2 或 Voyage-3；追求零成本可在本地部署 Nomic-embed 或 mxbai-embed。通过 APIShare 统一入口，你可以在一个 SDK 内自由切换所有模型，按场景选择最优向量质量。
Free Embedding Model APIs Roundup
Embeddings are the foundation of RAG, semantic search, and recommendation systems. This article rounds up embedding models currently available via free API calls, helping you choose without trial and error.
Why Do You Need Embedding APIs?
Embeddings map text into fixed-dimensional floating-point vectors so that semantically similar content is closer in vector space. Core use cases include:
- RAG (Retrieval-Augmented Generation): Vectorize knowledge base documents, store in a vector DB, and recall relevant chunks via similarity search at query time
- Semantic Search: Move beyond keyword matching to semantic recall
- Clustering & Classification: Automatically group or label large text corpora
- Recommendation Systems: Content-similarity-based recommendations
Evaluation Criteria
| Dimension |
Description |
| Vector Dim |
384 / 768 / 1024 / 1536 / 3072 — higher dims carry more info but cost more storage |
| Context Window |
Max tokens per input, ranging from 512 to 8192+ |
| Multilingual |
Native support for Chinese and other languages vs. English-only |
| Rate Limits |
Requests per minute / tokens per minute |
| MTEB Score |
Massive Text Embedding Benchmark ranking |
1. OpenAI text-embedding-3-small (Free Tier)
- Dimensions: 1536 (reducible to 256/512)
- Context: 8191 tokens
- Multilingual: 100+ languages, excellent Chinese support
- Rate: Shared free quota via APIShare unified access
- Highlights: Industry benchmark; the
dimensions parameter lets you trade off quality vs. storage
2. Hugging Face Inference API — BGE Series
- Models:
BAAI/bge-large-en-v1.5, BAAI/bge-m3
- Dimensions: 1024 (both)
- Context: 512 (bge-large) / 8192 (bge-m3)
- Multilingual: bge-m3 supports 50+ languages including Chinese, Japanese, Korean
- Rate: 1,000 calls/day on Inference API free tier
- Highlights: bge-m3 outputs dense + sparse + ColBERT vectors simultaneously
3. NVIDIA NIM — NV-Embed-v2
- Model:
nvidia/nv-embed-v2
- Dimensions: 4096
- Context: 32,768 tokens
- Multilingual: Primarily English
- Rate: 1,000 calls/day on NIM free tier
- Highlights: Consistently Top 3 on MTEB; ultra-long context for whole-document embedding
4. Jina AI — jina-embeddings-v3
- Model:
jinaai/jina-embeddings-v3
- Dimensions: 1024
- Context: 8192 tokens
- Multilingual: 89 languages
- Rate: 1M tokens/month with free API key
- Highlights: Task-type parameters optimize vectors per use case
5. Nomic AI — nomic-embed-text-v1.5
- Model:
nomic-ai/nomic-embed-text-v1.5
- Dimensions: 768
- Context: 8192 tokens
- Multilingual: Primarily English
- Rate: Fully open-source, self-hostable; Nomic Atlas free tier provides API
- Highlights: Strong interpretability, Matryoshka dimension reduction, fully open weights
6. Voyage AI — voyage-3 / voyage-3-lite
- Models:
voyage-3 / voyage-3-lite
- Dimensions: 1024 (voyage-3) / 512 (lite)
- Context: 32,000 tokens
- Multilingual: 100+ languages including Chinese
- Rate: 50M tokens/month free tier
- Highlights: Ultra-long context + leading MTEB scores; ideal for legal/academic documents
7. Mixedbread — mxbai-embed-large-v1
- Model:
mixedbread-ai/mxbai-embed-large-v1
- Dimensions: 1024
- Context: 512 tokens
- Multilingual: Primarily English
- Rate: Available on Hugging Face Inference API free tier
- Highlights: Top MTEB ranking, only 670M parameters — low deployment cost
Selection Guide
| Scenario |
Recommended Model |
Reason |
| Chinese RAG |
OpenAI text-embedding-3-small / BGE-m3 |
Best Chinese performance |
| Long Documents |
NVIDIA NV-Embed-v2 / Voyage-3 |
32K+ context window |
| Cost-Sensitive |
Nomic-embed / mxbai-embed |
Open-source, self-hostable |
| Multilingual |
Jina-v3 / BGE-m3 |
50+ language coverage |
| Low Latency |
OpenAI text-embedding-3-small |
Fastest API response |
Unified Access via APIShare
All models above are accessible through APIShare's unified endpoint — one token, no need to register with each provider:
models = [
"text-embedding-3-small", # OpenAI
"BAAI/bge-m3", # Hugging Face
"nvidia/nv-embed-v2", # NVIDIA NIM
"jinaai/jina-embeddings-v3", # Jina
]
for model in models:
resp = client.embeddings.create(model=model, input=text)
print(model, len(resp.data[0].embedding))
Conclusion
The free embedding API ecosystem has matured significantly by 2026. For Chinese scenarios, BGE-m3 or OpenAI text-embedding-3-small are top choices; for long documents, NVIDIA NV-Embed-v2 or Voyage-3 excel; for zero-cost setups, self-host Nomic-embed or mxbai-embed. Through APIShare's unified endpoint, you can freely switch between all models within a single SDK and pick the best embedding quality per scenario.