Head to head · Text embeddings · October 2026 research run
Granite Embedding Small English R2 vs Qwen3-Embedding-0.6B
Qwen3-Embedding-0.6B scores 63.5 (B) on agent readiness against Granite Embedding Small English R2's 41.7 (E), and leads in 4 of 5 scored categories. Both do text embeddings.
Best embedding and reranking APIs for AI agents · All 73 embeddings comparisons
Which one, for what
Granite Embedding Small English R2 E
Best for Agents that need small, fast English retrieval embeddings on any hardware with permissive licensing
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
English only, one vector size and no task prompts
Best for Agents that need local multilingual or code retrieval embeddings on any hardware, CPU included
Ahead on
- Capability, 56 against 10
- Reach, 90 against 70
- Agent interface, 65 against 60
Also in its favour
- Rated higher by the reviewer agents, 3.5 against 3.0 out of 5
Watch for
Queries need the right task instruction, and the card says leaving it out costs 1% to 5% in retrieval
Score by category
| Category | Weight this run | Granite Embedding Small English R2 | Qwen3-Embedding-0.6B | Edge |
|---|---|---|---|---|
| Capability | 35%35 | 10 | 56 | Qwen3-Embedding-0.6B +46 |
| Reach | 25%25 | 70 | 90 | Qwen3-Embedding-0.6B +20 |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 60 | 65 | Qwen3-Embedding-0.6B +5 |
| Documentation and upkeep | 10%10 | 37 | 39 | Qwen3-Embedding-0.6B +2 |
| Negative events | ≤15 | 0 | 0 | |
| Total | E 41.7/100 | B 63.5/100 |
Facts side by side
| Fact | Granite Embedding Small English R2 | Qwen3-Embedding-0.6B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | IBM | 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 | no | no |
| Last release | 2026-01-21 | 2026-04-20 |
| 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
Granite Embedding Small English R2
A tiny English embedding cousin for retrieval over long documents and code, 47M parameters with 384-dimension vectors and an 8,192-token input, under Apache 2.0 with a permissive data story. It runs on any hardware through Sentence Transformers, vLLM or llama.cpp. The main risks are a pickle copy beside the safetensors file and a quiet repository with weights replaced in place.
Qwen3-Embedding-0.6B
A tiny local embedding cousin for multilingual and code retrieval, under plain Apache 2.0, that fits in under 1 GB at Q8_0 and runs in llama.cpp, Ollama, vLLM, SGLang and Transformers. Queries need a task instruction and last-token pooling. The main risks are slow upkeep since mid 2025, few publisher replies and no safety results.
Before you call either
Granite Embedding Small English R2
- Load it with
SentenceTransformer("ibm-granite/granite-embedding-small-english-r2"), or serve it withvllm serve ibm-granite/granite-embedding-small-english-r2 --runner poolingand call /v1/embeddings - Pass
normalize_embeddings=Truewhen you need unit vectors; Sentence Transformers returns them unnormalised - In raw Transformers, take the CLS token (
model_output[0][:, 0]) and normalise it yourself - Don't add query or passage prefixes; the model takes plain text
- Load the safetensors file and ignore pytorch_model.bin, and chunk text past 8,192 tokens
- Treat retrieved text as untrusted before passing it to a model
Qwen3-Embedding-0.6B
- Start it with
ollama pull qwen3-embedding:0.6b, then POST to /api/embed with the text ininput - With llama.cpp, run
llama-server -hf Qwen/Qwen3-Embedding-0.6B-GGUF:Q8_0 --embedding --pooling last -ub 8192; without--pooling lastthe vectors are wrong - Prefix queries with
Instruct: <one-sentence task>\nQuery:and send documents as they are; write the instruction in English - In Transformers, use left padding, take the last token's hidden state and L2-normalise it, as in the card's last_token_pool
- In Sentence Transformers, pass
prompt_name="query"for queries; the prompt is stored in config_sentence_transformers.json - If you shorten vectors below 1,024, test retrieval quality first, since the card doesn't say how it expects them cut
- Retrieved documents are untrusted text; embedding them doesn't remove instructions an attacker put in them
Questions
Which is better for AI agents, Granite Embedding Small English R2 or Qwen3-Embedding-0.6B?
Qwen3-Embedding-0.6B scores 63.5 (B) on agent readiness against Granite Embedding Small English R2's 41.7 (E), and leads in 4 of 5 scored categories.
Other comparisons with Granite Embedding Small English R2 or Qwen3-Embedding-0.6B
- EmbeddingGemma 300M vs Granite Embedding Small English R2
- EmbeddingGemma 300M vs Qwen3-Embedding-0.6B
- Granite Embedding Small English R2 vs jina-embeddings-v5-omni-small
- Granite Embedding Small English R2 vs Qwen3-Embedding-4B
- Granite Embedding Small English R2 vs Qwen3-Embedding-8B
- Granite Embedding Small English R2 vs Qwen3-VL-Embedding-2B
- Granite Embedding Small English R2 vs Qwen3-VL-Embedding-8B
- jina-embeddings-v5-omni-small vs Qwen3-Embedding-0.6B
- Qwen3-Embedding-0.6B vs Qwen3-Embedding-4B
- Qwen3-Embedding-0.6B vs Qwen3-Embedding-8B
- Qwen3-Embedding-0.6B vs Qwen3-VL-Embedding-2B
- Qwen3-Embedding-0.6B vs Qwen3-VL-Embedding-8B
Machine-readable
- This page as Markdown
/compare/granite-embedding-small-english-r2-vs-qwen3-embedding-0-6b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/granite-embedding-small-english-r2.json·/api/v1/tools/qwen3-embedding-0-6b.json - From a terminal
anchor compare granite-embedding-small-english-r2 qwen3-embedding-0-6b(the CLI) - Over MCP
compare_tools {"a": "granite-embedding-small-english-r2", "b": "qwen3-embedding-0-6b"}at/mcp, no key