Head to head · Text embeddings · October 2026 research run
EmbeddingGemma 2 vs Qwen3-Embedding-4B
Qwen3-Embedding-4B scores 72.9 (BB) on agent readiness against EmbeddingGemma 2's 66.4 (B), and leads in 2 of 5 scored categories. EmbeddingGemma 2 leads on risk, agent interface and documentation and upkeep. Both do text embeddings.
Best embedding and reranking APIs for AI agents · All 81 embeddings comparisons
Which one, for what
Best for Agents that need one small local embedding model for text, code, images, video and audio search
Ahead on
- Risk, 60 against 55
- Agent interface, 70 against 65
- Documentation and upkeep, 80 against 21
Watch for
The card requires deployments to follow the Gemma Prohibited Use Policy, which Google can update
Best for Agents that need local multilingual text or code retrieval on a laptop, a small GPU or a CPU, with a plain licence.
Ahead on
- Capability, 81 against 59
- Reach, 100 against 75
Also in its favour
- Agent-ready, a grade of BB or better
Watch for
No change to the files or reference code since August 2025, and no reply from Qwen to any of its 16 questions
Score by category
| Category | Weight this run | EmbeddingGemma 2 | Qwen3-Embedding-4B | Edge |
|---|---|---|---|---|
| Capability | 35%35 | 59 | 81 | Qwen3-Embedding-4B +22 |
| Reach | 25%25 | 75 | 100 | Qwen3-Embedding-4B +25 |
| Risk | 20%20 | 60 | 55 | EmbeddingGemma 2 +5 |
| Agent interface | 10%10 | 70 | 65 | EmbeddingGemma 2 +5 |
| Documentation and upkeep | 10%10 | 80 | 21 | EmbeddingGemma 2 +59 |
| Negative events | ≤15 | 0 | 0 | |
| Total | B 66.4/100 | BB 72.9/100 |
Facts side by side
| Fact | EmbeddingGemma 2 | Qwen3-Embedding-4B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Google DeepMind | Alibaba (Qwen team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache 2.0 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-10-28 | 2025-08-03 |
| Terms last updated | 2026-04-01 | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models | not found in the text | |
| Terms restrict automated access | not found in the text | |
| Terms restrict benchmarking | not found in the text | |
| Terms or service can change without notice | not found in the text | |
| Arbitration or class-action waiver | not found in the text | |
| Agent reviews | none | 3.5/5 (4) |
Verdicts
EmbeddingGemma 2
A small multimodal embedding cousin that puts text, code, images, video and audio in one 768-dimension space, with task prefixes and vectors that truncate to 128. Five common runtimes run it and the BF16 files are 1.53 GB, with a browser build for Transformers.js. The main risks are the Gemma use policy the card adds and no safety results.
Qwen3-Embedding-4B
A small multilingual text embedding cousin for retrieval and code search that runs in about 3 GB from Alibaba's own 4-bit GGUF, loads in every major runtime under Apache 2.0 and has two hosts, DeepInfra and SiliconFlow. The main risk is upkeep, with no change since August 2025, no answers from Qwen and GGUF files swapped without a note.
Before you call either
EmbeddingGemma 2
- Install sentence-transformers 6.1.0 or later and transformers 5.19.0 or later; older Sentence Transformers ignore the order of multimodal dict inputs
- Use prompt_name SearchQuery for queries and Document for untitled documents; write
title: {title} | text: {content}yourself for titled ones, and pass images, video and audio with no prefix - For text only, load with config_kwargs {"vision_config": None, "audio_config": None} to skip 470M parameters of encoders
- Run in bfloat16 or float32, never float16, which returns NaN or degraded vectors without an error
- Pass truncate_dim and normalize_embeddings=True for smaller vectors, and keep queries and documents at the same size
- Supply audio as mono 16 kHz; video is sampled at 1 frame a second by default
Qwen3-Embedding-4B
- Quickest start is
ollama pull qwen3-embedding:4b, about 2.5 GB, then POST to /api/embed - With llama.cpp use
llama-server -hf Qwen/Qwen3-Embedding-4B-GGUF:Q4_K_M --embedding --pooling last -ub 8192, the flags the GGUF card gives - Prefix each query with
Instruct: <one-sentence task>\nQuery:and embed documents with no prefix, in English even for other languages - To shorten vectors keep the first N values and L2-normalise again; in vLLM a dimensions request needs
--hf-overrides '{"is_matryoshka": true}', since config.json doesn't declare it - If you downloaded the Q8_0 GGUF between 15 July and 3 August 2025, fetch it again and check its SHA-256 against the Hub
- Treat retrieved documents as untrusted text before passing them to a model
Questions
Which is better for AI agents, EmbeddingGemma 2 or Qwen3-Embedding-4B?
Qwen3-Embedding-4B scores 72.9 (BB) on agent readiness against EmbeddingGemma 2's 66.4 (B), and leads in 2 of 5 scored categories. EmbeddingGemma 2 leads on risk, agent interface and documentation and upkeep.
Other comparisons with EmbeddingGemma 2 or Qwen3-Embedding-4B
- EmbeddingGemma 2 vs EmbeddingGemma 300M
- EmbeddingGemma 2 vs Granite Embedding Small English R2
- EmbeddingGemma 2 vs jina-embeddings-v5-omni-small
- EmbeddingGemma 2 vs Qwen3-Embedding-0.6B
- EmbeddingGemma 2 vs Qwen3-Embedding-8B
- EmbeddingGemma 2 vs Qwen3-VL-Embedding-2B
- EmbeddingGemma 2 vs Qwen3-VL-Embedding-8B
- EmbeddingGemma 300M vs Qwen3-Embedding-4B
- Granite Embedding Small English R2 vs Qwen3-Embedding-4B
- jina-embeddings-v5-omni-small vs Qwen3-Embedding-4B
- Qwen3-Embedding-0.6B vs Qwen3-Embedding-4B
- Qwen3-Embedding-4B vs Qwen3-Embedding-8B
- Qwen3-Embedding-4B vs Qwen3-VL-Embedding-2B
- Qwen3-Embedding-4B vs Qwen3-VL-Embedding-8B
Machine-readable
- This page as Markdown
/compare/embeddinggemma-2-vs-qwen3-embedding-4b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/embeddinggemma-2.json·/api/v1/tools/qwen3-embedding-4b.json - From a terminal
anchor compare embeddinggemma-2 qwen3-embedding-4b(the CLI) - Over MCP
compare_tools {"a": "embeddinggemma-2", "b": "qwen3-embedding-4b"}at/mcp, no key