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https://github.com/cocoindex-io/cocoindex-code.git
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Jim McKeeth
e709902e12
* feature: updated alternative embedding options after testing * More detailed stats, removed incompatible models, and some other minor issues. There are a number of really good options like `lightonai/LateOn-Code-edge` * cleaning up tiers
7.1 KiB
7.1 KiB
MTEB Model Discovery Report
Data Freshness: MTEB results dataset last updated on
2026-06-23.
Top Embedding Models for Code Search
Speed note: CPU speed is estimated from parameter count and architecture. Encoder models (BERT/ModernBERT-based) process tokens in parallel and are significantly faster on CPU than Decoder models (LLM-based), which process tokens sequentially. A decoder model of the same parameter count can be 3–10× slower on CPU.
Tier: Micro (< 50M)
| Model | Code Score | General Score | Params (M) | Architecture | CPU Speed |
|---|---|---|---|---|---|
| lightonai/LateOn-Code-edge | 0.816549 | nan | 17 | Encoder | Very Fast |
| lightonai/LateOn-Code-edge-pretrain | 0.791693 | nan | 16.798 | Encoder | Very Fast |
| thenlper/gte-small | 0.781565 | 0.479423 | 33 | Encoder | Very Fast |
| avsolatorio/GIST-small-Embedding-v0 | 0.772521 | 0.480646 | 33.36 | Encoder | Very Fast |
| avsolatorio/NoInstruct-small-Embedding-v0 | 0.770071 | 0.488884 | 33.36 | Encoder | Very Fast |
| abhinand/MedEmbed-small-v0.1 | 0.766076 | 0.52863 | 33.36 | Encoder | Very Fast |
| BAAI/bge-small-en-v1.5 | 0.75267 | 0.514409 | 33 | Encoder | Very Fast |
| Snowflake/snowflake-arctic-embed-s | 0.672949 | 0.493245 | 33 | Encoder | Very Fast |
Tier: Small (< 150M)
| Model | Code Score | General Score | Params (M) | Architecture | CPU Speed |
|---|---|---|---|---|---|
| lightonai/LateOn-Code | 0.851318 | nan | 149 | Encoder | Fast |
| lightonai/LateOn-Code-pretrain | 0.832574 | nan | 149.016 | Encoder | Fast |
| ibm-granite/granite-embedding-97m-multilingual-r2 | 0.799971 | 0.446515 | 97 | Encoder | Fast |
| avsolatorio/GIST-Embedding-v0 | 0.78981 | 0.503411 | 109.482 | Encoder | Fast |
| thenlper/gte-base | 0.789403 | 0.496155 | 109 | Encoder | Fast |
| ibm-granite/granite-embedding-english-r2 | 0.773404 | 0.501664 | 149.014 | Unknown | Unknown |
| BAAI/bge-base-en-v1.5 | 0.767726 | 0.531966 | 109 | Encoder | Fast |
| nomic-ai/nomic-embed-text-v1.5 | 0.716379 | 0.49881 | 137 | Encoder | Fast |
Tier: Medium (< 500M)
| Model | Code Score | General Score | Params (M) | Architecture | CPU Speed |
|---|---|---|---|---|---|
| geevec-ai/geevec-embeddings-1.0-lite | 0.92365 | 0.53474 | 366 | Encoder | Moderate |
| jinaai/jina-embeddings-v5-text-nano | 0.90384 | 0.535934 | 239 | Encoder | Moderate |
| microsoft/harrier-oss-v1-270m | 0.89605 | 0.425505 | 270 | Decoder | Slow (Decoder) |
| Shuu12121/CodeSearch-ModernBERT-Crow-Plus | 0.892957 | nan | 151.668 | Encoder | Fast |
| codefuse-ai/F2LLM-v2-330M | 0.842182 | 0.475202 | 334 | Decoder | Slow (Decoder) |
| google/embeddinggemma-300m | 0.838689 | 0.459 | 302.863 | Unknown | Unknown |
| Shuu12121/NightOwl-CodeEmbedding | 0.831063 | nan | 150.779 | Unknown | Unknown |
| codefuse-ai/C2LLM-0.5B | 0.828636 | nan | 497.252 | Unknown | Unknown |
Tier: Large (> 500M)
| Model | Code Score | General Score | Params (M) | Architecture | CPU Speed |
|---|---|---|---|---|---|
| microsoft/harrier-oss-v1-27b | 0.96994 | 0.483455 | 27009.3 | Decoder | Slow (Decoder) |
| Octen/Octen-Embedding-8B-INT8 | 0.967965 | nan | 7567.3 | Decoder | Slow (Decoder) |
| nvidia/llama-embed-nemotron-8b | 0.96586 | 0.51917 | 7504.92 | Unknown | Unknown |
| Octen/Octen-Embedding-4B-INT8 | 0.96369 | nan | 4022.88 | Unknown | Unknown |
| bflhc/MoD-Embedding | 0.96368 | nan | 4021.77 | Unknown | Unknown |
| Octen/Octen-Embedding-4B | 0.96236 | nan | 4021.77 | Unknown | Unknown |
| Octen/Octen-Embedding-8B | 0.9597 | 0.505307 | 7567.3 | Unknown | Unknown |
| Mira190/Euler-Legal-Embedding-V1 | 0.95635 | 0.51144 | 8188.52 | Decoder | Slow (Decoder) |
Snowflake Arctic Embed Family — Baseline Reference
These encoder-based models are included as baseline references and span the full Snowflake Arctic size range. The xs variant is the default model in cocoindex-code. All variants use an encoder architecture and are fast on CPU. Scores below come from the live MTEB dataset where available.
| Model | Code Score | General Score | Params (M) | Architecture | CPU Speed |
|---|---|---|---|---|---|
| Snowflake/snowflake-arctic-embed-xs | 0.6661 | 0.4721 | 22 | Encoder | Very Fast |
| Snowflake/snowflake-arctic-embed-s | 0.6729 | 0.4932 | 33 | Encoder | Very Fast |
| Snowflake/snowflake-arctic-embed-m | 0.7003 | 0.5197 | 109 | Encoder | Fast |
| Snowflake/snowflake-arctic-embed-l | 0.6976 | 0.5314 | 334 | Encoder | Moderate |
| Snowflake/snowflake-arctic-embed-2-m | N/A | N/A | 305 | Encoder | Moderate |
| Snowflake/snowflake-arctic-embed-2-large | N/A | N/A | 568 | Encoder | Moderate |
How to Regenerate this Report
This report was generated using the find_best_models.py script. To update it with the latest live data from MTEB, run:
uv run scripts/find_best_models.py --clear-cache --output MTEB-RANKINGS.md