{
  "request": {
    "slug": "rag-embedding-reranker-selection",
    "title": "Choose embedding and reranker models for RAG",
    "model": "moonshotai/Kimi-K2.6:novita",
    "instructions": "Keep this as a quick model-selection answer. Do not launch broad research sub-agents. Use at most four direct Hugging Face lookups, then answer.",
    "input": "For a 2026 production RAG system over technical docs, compare these Hugging Face options: Qwen/Qwen3-Embedding-8B, BAAI/bge-m3, jinaai/jina-embeddings-v4, and BAAI/bge-reranker-v2-m3. Recommend an embedding + reranker stack for quality vs latency. Include one short sentence-transformers or transformers usage snippet. Keep under 450 words.",
    "background": true,
    "max_cost_usd": 2.0,
    "metadata": {
      "example_slug": "rag-embedding-reranker-selection"
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    "object": "response",
    "created_at": 1781314650,
    "completed_at": 1781314686,
    "status": "completed",
    "model": "moonshotai/Kimi-K2.6:novita",
    "background": true,
    "previous_response_id": null,
    "session_id": "b6254061-b8b9-4d8c-8768-a4d96606263f",
    "max_cost_usd": 2.0,
    "instructions": "Keep this as a quick model-selection answer. Do not launch broad research sub-agents. Use at most four direct Hugging Face lookups, then answer.",
    "output": [
      {
        "type": "custom_tool_call",
        "id": "hub_repo_details:0",
        "name": "hub_repo_details",
        "input": "{\"repo_ids\": [\"Qwen/Qwen3-Embedding-8B\", \"BAAI/bge-m3\", \"jinaai/jina-embeddings-v4\", \"BAAI/bge-reranker-v2-m3\"]}",
        "output": "**Type: Model**\n\n# Qwen/Qwen3-Embedding-8B\n\n## Overview\n- **Author:** Qwen\n- **Task:** feature-extraction\n- **Library:** sentence-transformers\n- **Downloads:** 13.8M | **Likes:** 705\n- **Updated:** 7 Jul, 2025\n\n## Technical Details\n- **Model Class:** AutoModel\n- **Parameters:** 7567.3M\n\n## Tags\n`sentence-transformers` `safetensors` `transformers` `sentence-similarity` `feature-extraction` `text-embeddings-inference` `arxiv:2506.05176` `base_model:Qwen/Qwen3-8B-Base` `base_model:finetune:Qwen/Qwen3-8B-Base` `license:apache-2.0` `endpoints_compatible` `deploy:azure` `region:us`\n\n## Metadata\n- **License:** apache-2.0\n\n## Demo Spaces\n- [leaderboard](https://hf.co/spaces/mteb/leaderboard)\n- [leaderboard](https://hf.co/spaces/mteb-pt/leaderboard)\n- [PhiRAG](https://hf.co/spaces/alx-d/PhiRAG)\n- [nomic-embeddings](https://hf.co/spaces/ipepe/nomic-embeddings)\n- [miian-Q3](https://hf.co/spaces/miiann/miian-Q3)\n- [rag_korean_manufacturing_docs](https://hf.co/spaces/Ahmed-El-Zainy/rag_korean_manufacturing_docs)\n- [germeval2025](https://hf.co/spaces/cortex359/germeval2025)\n- [Saudi-Law-AI-Assistant](https://hf.co/spaces/AldawsariNLP/Saudi-Law-AI-Assistant)\n- [AIDA](https://hf.co/spaces/destinyebuka/AIDA)\n- [legacy_code_modernizer](https://hf.co/spaces/MCP-1st-Birthday/legacy_code_modernizer)\n- [central-memory-agent](https://hf.co/spaces/MCP-1st-Birthday/central-memory-agent)\n- [leaderboard](https://hf.co/spaces/maxpar1/leaderboard)\n- *... and 48 more spaces*\n\n## Inference Providers\n- **scaleway** (live)\n- **novita** (error)\n- **nebius** (error)\n\nTry this model in the [playground](https://hf.co/playground?modelId=Qwen/Qwen3-Embedding-8B)\n\n**Link:** [https://hf.co/Qwen/Qwen3-Embedding-8B](https://hf.co/Qwen/Qwen3-Embedding-8B)\n\n---\n\n**Type: Model**\n\n# BAAI/bge-m3\n\n## Overview\n- **Author:** BAAI\n- **Task:** sentence-similarity\n- **Library:** sentence-transformers\n- **Downloads:** 182.5M | **Likes:** 3104\n- **Updated:** 3 Jul, 2024\n\n## Technical Details\n- **Architecture:** xlm-roberta\n\n## Tags\n`sentence-transformers` `pytorch` `onnx` `xlm-roberta` `feature-extraction` `sentence-similarity` `arxiv:2402.03216` `arxiv:2004.04906` `arxiv:2106.14807` `arxiv:2107.05720` `arxiv:2004.12832` `license:mit` `eval-results` `text-embeddings-inference` `endpoints_compatible` `deploy:azure` `region:us`\n\n## Metadata\n- **License:** mit\n\n## Demo Spaces\n- [leaderboard](https://hf.co/spaces/mteb/leaderboard)\n- [DanbooruSearch](https://hf.co/spaces/SAkizuki/DanbooruSearch)\n- [WenQiuYue](https://hf.co/spaces/SAkizuki/WenQiuYue)\n- [ManthanAI](https://hf.co/spaces/anirudh594/ManthanAI)\n- [construction-rag](https://hf.co/spaces/Ashanasri/construction-rag)\n- [leaderboard](https://hf.co/spaces/mteb-pt/leaderboard)\n- [FinTech-FinalProject-Group7](https://hf.co/spaces/JasonRott/FinTech-FinalProject-Group7)\n- [rag2](https://hf.co/spaces/shreyasp11111111111111/rag2)\n- [foodeval-leaderboard](https://hf.co/spaces/latimal/foodeval-leaderboard)\n- [leaderboard_legacy](https://hf.co/spaces/mteb/leaderboard_legacy)\n- [thai_sentence_embedding_benchmark](https://hf.co/spaces/panuthept/thai_sentence_embedding_benchmark)\n- [mtem-pruner](https://hf.co/spaces/antoinelouis/mtem-pruner)\n- *... and 88 more spaces*\n\n## Inference Providers\n- **hf-inference** (live)\n\nTry this model in the [playground](https://hf.co/playground?modelId=BAAI/bge-m3)\n\n**Link:** [https://hf.co/BAAI/bge-m3](https://hf.co/BAAI/bge-m3)\n\n---\n\n**Type: Model**\n\n# jinaai/jina-embeddings-v4\n\n## Overview\n- **Author:** jinaai\n- **Task:** visual-document-retrieval\n- **Library:** transformers\n- **Downloads:** 2.4M | **Likes:** 525\n- **Updated:** 8 Apr, 2026\n\n## Technical Details\n- **Model Class:** AutoModel\n- **Parameters:** 3754.9M\n\n## Tags\n`transformers` `safetensors` `image-feature-extraction` `vidore` `colpali` `multimodal-embedding` `multilingual-embedding` `Text-to-Visual Document (T\u2192VD) retrieval` `feature-extraction` `sentence-similarity` `mteb` `sentence-transformers` `vllm` `visual-document-retrieval` `custom_code` `multilingual` `arxiv:2506.18902` `region:eu`\n\n## Metadata\n- **Language:** multilingual\n\n## Demo\u2026 [truncated 2919 chars]",
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            "type": "output_text",
            "text": "| Model | Params | Speed | Best For |\n|---|---|---|---|\n| **Qwen3-Embedding-8B** | 7.6B | Slowest | Maximum retrieval quality |\n| **BAAI/bge-m3** | ~278M | Fastest | Proven production workhorse |\n| **jina-embeddings-v4** | 3.8B | Medium | Visual/multimodal docs, late chunking |\n| **BAAI/bge-reranker-v2-m3** | 568M | Fast | Standard reranker |\n\n**Latency stack:** `BAAI/bge-m3` + `BAAI/bge-reranker-v2-m3`. bge-m3 is the most downloaded embedding on the Hub (182M), runs efficiently on CPU or small GPU, and handles multilingual technical text well. The 568M reranker is lightweight to re-score top-k chunks.\n\n**Quality stack:** `Qwen/Qwen3-Embedding-8B` + `BAAI/bge-reranker-v2-m3`. The 8B Qwen3 model dominates MTEB benchmarks and captures nuanced technical concepts far better than smaller encoders, but budget a GPU for inference.\n\n**Jina v4** (3.8B, April 2026) is compelling if your docs include diagrams or you want ColPali-style visual retrieval; otherwise it sits between the other two on speed/quality.\n\nShort usage snippet:\n```python\nfrom sentence_transformers import SentenceTransformer\nmodel = SentenceTransformer(\"BAAI/bge-m3\")\nembeddings = model.encode(docs, normalize_embeddings=True)\n```"
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      "inference_usd": 0.0,
      "hf_jobs_estimated_usd": 0.0,
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      "llm_calls": 2,
      "hf_jobs_count": 0,
      "sandbox_count": 0,
      "prompt_tokens": 31905,
      "completion_tokens": 1413,
      "cache_read_tokens": 14336,
      "cache_creation_tokens": 0,
      "total_tokens": 33318,
      "hf_jobs_billable_seconds_estimate": 0,
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    "metadata": {
      "example_slug": "rag-embedding-reranker-selection"
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