All tasks / Comparisons
F2LLM-v2-14B vs Qwen3-Embedding-8B
Compared only on public benchmark results, across the 1 task where both are listed: Qwen3-Embedding-8B is a tier higher in 1 of 1 shared task.
F2LLM-v2-14B
Open-weights Apache-2.0 multilingual text embedding models from codefuse-ai, from 80M to 14B parameters.
- Vendor
- Ant Group (codefuse-ai)
- Weights
- Open: run it yourself
- Released
- Mar 2026
- GPU memory to run locally
- ~28 GB
- License
- Apache-2.0
- Last verified
- 19 Oct 2026
Qwen3-Embedding-8B
Alibaba's Apache-2.0 open-weights multilingual text embedding models in 0.6B, 4B and 8B sizes.
- Vendor
- Alibaba (Qwen)
- Weights
- Open: run it yourself
- Released
- Jun 2025
- GPU memory to run locally
- ~16 GB
- License
- Apache-2.0
- Last verified
- 19 Oct 2026
Task by task
| Task | F2LLM-v2-14B | Qwen3-Embedding-8B | Higher tier |
|---|---|---|---|
| Embeddings & retrieval Models | A68.7% on MTEB(Multilingual, v2) · Local | S70.6% on MTEB(Multilingual, v2) · Local | Qwen3-Embedding-8B |
A tier spans a range of scores, and small gaps on a leaderboard are often within its margin of error, so only a different tier counts as ahead. How we rank.
Same benchmark, both results
| Benchmark | F2LLM-v2-14B | Qwen3-Embedding-8B | Snapshot |
|---|---|---|---|
| MTEB(eng, v2) Mean (Task): average main score over 41 English tasks (classification, clustering, pair classification, reranking, retrieval, STS, summarization), higher is better | 73.1% | 75.2% | 19 Oct 2026 |
| MTEB(Multilingual, v2) Mean (Task): average main score over 131 multilingual tasks (bitext mining, classification, clustering, retrieval, reranking, STS, instruction reranking), higher is better | 68.7% | 70.6% | 19 Oct 2026 |