Head to head · Text embeddings · October 2026 research run
Granite Embedding Small English R2 vs Qwen3-VL-Embedding-2B
Qwen3-VL-Embedding-2B scores 55.4 (C) on agent readiness against Granite Embedding Small English R2's 41.7 (E), and leads in 3 of 5 scored categories. Granite Embedding Small English R2 leads on risk. 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
Ahead on
- Risk, 55 against 45
Watch for
English only, one vector size and no task prompts
Best for Agents that need local multimodal search over text, screenshots, images and video on a small GPU or CPU
Ahead on
- Capability, 50 against 10
- Reach, 75 against 70
- Agent interface, 65 against 60
Watch for
No quantised build from Alibaba and nothing in Ollama's or LM Studio's own libraries
Score by category
| Category | Weight this run | Granite Embedding Small English R2 | Qwen3-VL-Embedding-2B | Edge |
|---|---|---|---|---|
| Capability | 35%35 | 10 | 50 | Qwen3-VL-Embedding-2B +40 |
| Reach | 25%25 | 70 | 75 | Qwen3-VL-Embedding-2B +5 |
| Risk | 20%20 | 55 | 45 | Granite Embedding Small English R2 +10 |
| Agent interface | 10%10 | 60 | 65 | Qwen3-VL-Embedding-2B +5 |
| Documentation and upkeep | 10%10 | 37 | 36 | Granite Embedding Small English R2 +1 |
| Negative events | ≤15 | 0 | 0 | |
| Total | E 41.7/100 | C 55.4/100 |
Facts side by side
| Fact | Granite Embedding Small English R2 | Qwen3-VL-Embedding-2B |
|---|---|---|
| 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 | 2026-04-16 |
| 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 (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-VL-Embedding-2B
A small multimodal embedding cousin that puts text, images, screenshots and video into one vector space, in 4.3 GB of BF16 files that fit any 8 GB machine, under plain Apache 2.0. It loads with Sentence Transformers, vLLM or SGLang. The main gaps are no quantised build from Alibaba, few publisher replies and no safety material.
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-VL-Embedding-2B
- Start it with
vllm serve Qwen/Qwen3-VL-Embedding-2B --runner pooling --max-model-len 8192, then POST chat-stylemessageswith the instruction in the system turn to /v1/embeddings - Or
pip install -U sentence-transformers(5.4 or later) andSentenceTransformer('Qwen/Qwen3-VL-Embedding-2B').encode(texts, prompt='<task instruction>'); images go in as URLs, paths or dicts with text and image - Write the task instruction in English, even for other languages, and embed queries and documents with matching instructions
- To shorten vectors, cut the first N of 2,048 dimensions and renormalise yourself; no parameter for it is documented
- Don't mix vectors from vLLM and the reference qwen_vl_utils path in one index, since vLLM's docs say image preprocessing differs
- Treat retrieved documents as untrusted before passing them to another model
Questions
Which is better for AI agents, Granite Embedding Small English R2 or Qwen3-VL-Embedding-2B?
Qwen3-VL-Embedding-2B scores 55.4 (C) on agent readiness against Granite Embedding Small English R2's 41.7 (E), and leads in 3 of 5 scored categories. Granite Embedding Small English R2 leads on risk.
Other comparisons with Granite Embedding Small English R2 or Qwen3-VL-Embedding-2B
- EmbeddingGemma 300M vs Granite Embedding Small English R2
- EmbeddingGemma 300M vs Qwen3-VL-Embedding-2B
- Granite Embedding Small English R2 vs jina-embeddings-v5-omni-small
- Granite Embedding Small English R2 vs Qwen3-Embedding-0.6B
- 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-8B
- jina-embeddings-v5-omni-small vs Qwen3-VL-Embedding-2B
- Qwen3-Embedding-0.6B vs Qwen3-VL-Embedding-2B
- Qwen3-Embedding-4B vs Qwen3-VL-Embedding-2B
- Qwen3-Embedding-8B vs Qwen3-VL-Embedding-2B
- Qwen3-VL-Embedding-2B vs Qwen3-VL-Embedding-8B
Machine-readable
- This page as Markdown
/compare/granite-embedding-small-english-r2-vs-qwen3-vl-embedding-2b.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-vl-embedding-2b.json - From a terminal
anchor compare granite-embedding-small-english-r2 qwen3-vl-embedding-2b(the CLI) - Over MCP
compare_tools {"a": "granite-embedding-small-english-r2", "b": "qwen3-vl-embedding-2b"}at/mcp, no key