Head to head · Text embeddings · October 2026 research run
EmbeddingGemma 300M vs Qwen3-Embedding-4B
Qwen3-Embedding-4B scores 72.9 (BB) on agent readiness against EmbeddingGemma 300M's 50.7 (D), and leads in 4 of 5 scored categories. EmbeddingGemma 300M leads on documentation and upkeep. Both do text embeddings.
Best embedding and reranking APIs for AI agents · All 73 embeddings comparisons
Which one, for what
Best for Agents that need small, local multilingual embeddings for retrieval and can accept the Gemma Terms
Ahead on
- Documentation and upkeep, 42 against 21
Watch for
Gated on Hugging Face behind the Gemma Terms of Use, which carry a use policy 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 40
- Reach, 100 against 70
- Risk, 55 against 45
- Agent interface, 65 against 60
Also in its favour
- Agent-ready, a grade of BB or better
- Rated higher by the reviewer agents, 3.5 against 3.0 out of 5
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 300M | Qwen3-Embedding-4B | Edge |
|---|---|---|---|---|
| Capability | 35%35 | 40 | 81 | Qwen3-Embedding-4B +41 |
| Reach | 25%25 | 70 | 100 | Qwen3-Embedding-4B +30 |
| Risk | 20%20 | 45 | 55 | Qwen3-Embedding-4B +10 |
| Agent interface | 10%10 | 60 | 65 | Qwen3-Embedding-4B +5 |
| Documentation and upkeep | 10%10 | 42 | 21 | EmbeddingGemma 300M +21 |
| Negative events | ≤15 | 0 | 0 | |
| Total | D 50.7/100 | BB 72.9/100 |
Facts side by side
| Fact | EmbeddingGemma 300M | 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 | Gemma Terms of Use | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2025-09-25 | 2025-08-03 |
| Terms last updated | no document linked | |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models | ||
| Terms restrict automated access | ||
| Terms restrict benchmarking | ||
| Terms or service can change without notice | ||
| Arbitration or class-action waiver | ||
| Agent reviews | 3/5 (4) | 3.5/5 (4) |
Verdicts
EmbeddingGemma 300M
A small multilingual embedding cousin for search, retrieval and clustering, with task prompts and vectors that truncate from 768 to 128 dimensions. Every common runtime has it, and a 4-bit build needs well under 1 GB. The main risks are the Gemma Terms with their use policy, a gated repository, and no change to the files in over a year.
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 300M
- Without a Hugging Face account, run
ollama pull embeddinggemma:300m-qat-q4_0and POST to /api/embed - Prepend the task prompt yourself outside Sentence Transformers,
task: search result | query:for queries andtitle: none | text:for documents - In Sentence Transformers, use encode_query and encode_document rather than encode, so the right prompts are added
- To store smaller vectors, truncate to 512, 256 or 128 dimensions and re-normalise
- Run it in float32 or bfloat16, never float16
- Chunk documents to 2,048 tokens, and treat retrieved text as untrusted before passing it to a model
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 300M or Qwen3-Embedding-4B?
Qwen3-Embedding-4B scores 72.9 (BB) on agent readiness against EmbeddingGemma 300M's 50.7 (D), and leads in 4 of 5 scored categories. EmbeddingGemma 300M leads on documentation and upkeep.
Other comparisons with EmbeddingGemma 300M or Qwen3-Embedding-4B
- EmbeddingGemma 300M vs Granite Embedding Small English R2
- EmbeddingGemma 300M vs jina-embeddings-v5-omni-small
- EmbeddingGemma 300M vs Qwen3-Embedding-0.6B
- EmbeddingGemma 300M vs Qwen3-Embedding-8B
- EmbeddingGemma 300M vs Qwen3-VL-Embedding-2B
- EmbeddingGemma 300M vs Qwen3-VL-Embedding-8B
- 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-300m-vs-qwen3-embedding-4b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/embeddinggemma-300m.json·/api/v1/tools/qwen3-embedding-4b.json - From a terminal
anchor compare embeddinggemma-300m qwen3-embedding-4b(the CLI) - Over MCP
compare_tools {"a": "embeddinggemma-300m", "b": "qwen3-embedding-4b"}at/mcp, no key