F2LLM 0.6B measured on 19 of 261 Embeddings benchmarks19
BenchmarkByScoreRank
No benchmark matches
self-host onlyprice
55.7MTEB English v2
Sep 2025released
1,024Dimensions
41K tokensMax input
enLanguages
Rankingconfiguration unstated?
Full ranking →···
55UAE Large v155.956F2LLM 0.6B55.757EmbeddingGemma 300M55.7Strong at
Alternatives?
ModelScoreCostPriceWhat changes
BetterQwen 3 Embedding 8BunstatedOpen+25%—$0.01Max input: 41K tokens → 33K tokens+2IOpenYuan Embedding 2.0 Enunstated+27%——Max input: 41K tokens → 33K tokensBetterQwen 3 Embedding 0.6BunstatedOpen+11%—$0.01Max input: 41K tokens → 33K tokens+1BetterQwen 3 Embedding 4BunstatedOpen+23%—$0.02Max input: 41K tokens → 33K tokens+2
BetterJina Embeddings v5 Text NanounstatedOpen+6%—$0.02Max input: 41K tokens → 8K tokens+1ModelScoreCostPriceWhat changes
Nothing here yet.
ModelScoreCostPriceWhat changes
Qwen 3 Embedding 8BunstatedOpen+25%—$0.01Max input: 41K tokens → 33K tokens+2Qwen 3 Embedding 0.6BunstatedOpen+11%—$0.01Max input: 41K tokens → 33K tokens+1Qwen 3 Embedding 4BunstatedOpen+23%—$0.02Max input: 41K tokens → 33K tokens+2Jina Embeddings v5 Text NanounstatedOpen+6%—$0.02Max input: 41K tokens → 8K tokens+1
Text Embedding 005unstated+6%—$0.025Max input: 41K tokens → 2K tokens+1ModelScoreCostPriceWhat changes
IYuan Embedding 2.0 EnunstatedOpen+27%——Max input: 41K tokens → 33K tokensQwen 3 Embedding 8BunstatedOpen+25%—$0.01Max input: 41K tokens → 33K tokens+2Qwen 3 Embedding 4BunstatedOpen+23%—$0.02Max input: 41K tokens → 33K tokens+2KQZhou EmbeddingunstatedOpen+21%——Max input: 41K tokens → 33K tokens+1iJasper Token Compression 600MunstatedOpen+19%——Max input: 41K tokens → 33K tokensModelScoreCostPriceWhat changes
cF2LLM v2 14BunstatedOpen+9%——Memory (4-bit): 0.4 GB → 8.4 GBcF2LLM v2 8BunstatedOpen+7%——Memory (4-bit): 0.4 GB → 4.5 GBcF2LLM 4BunstatedOpen+7%——Memory (4-bit): 0.4 GB → 2.4 GBcF2LLM v2 4BunstatedOpen+6%——Memory (4-bit): 0.4 GB → 2.4 GBcF2LLM 1.7BunstatedOpen+4%——Memory (4-bit): 0.4 GB → 1 GBcF2LLM v2 1.7BunstatedOpensame——Memory (4-bit): 0.4 GB → 1 GBcF2LLM v2 0.6BunstatedOpen-3%——cF2LLM v2 330MunstatedOpen-4%——All codefuse-ai models →Run it yourself
estimateFits on✓ 8 GB (phone, laptop) · full quality✓ 16 GB · full quality✓ 24 GB GPU · full quality✓ 48 GB GPU · full quality✓ 80 GB GPU · full quality
LicenceApache-2.0free for commercial use
History
2 versionsBenchmarks
in Embeddings & RetrievalFAQ
How much does F2LLM 0.6B cost?›
There is no per-unit API price: self-host only.
Is F2LLM 0.6B free to use?›
There is no free API. The weights are open, so you can run it yourself.
How good is F2LLM 0.6B, and what is it best at?›
#52 of 200 in Embeddings.
Is F2LLM 0.6B better than UAE Large v1?›
On MTEB English v2 · Retrieval · English, F2LLM 0.6B scores 55.7 vs 55.9 — about the same.
When was F2LLM 0.6B released? Is there a newer version?›
Released 2025-09-18. It is the current version in its line.
Is F2LLM 0.6B open source?›
Yes, the weights are open (Apache-2.0 licence).
Can I run F2LLM 0.6B locally?›
The weights are open and can be self-hosted; hardware needs are not confirmed yet.
What is F2LLM 0.6B?›
F2LLM 0.6B is an open-weights embeddings & retrieval model from codefuse-ai, released Sep 2025.
What are the best alternatives to F2LLM 0.6B?›
Qwen 3 Embedding 8B (better), Yuan Embedding 2.0 En (open weights).