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endless-frontier
Available on FriendliAI
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Model Details
Model Provider
endless-frontier
Model Tree
Qwen/Qwen3.6-35B-A3B
Input Modalities
Output Modalities
Supported Functionality
Fx-Work-1028 scores higher than GLM-5.2 on JobBench Main, AutomationBench, and Claw-Eval, while remaining close on GDPvalAA-v2.1 Elo (1328 vs. 1374) and APEX-Agents-AA (28.2 vs. 29.7):
Higher is better for every metric. GDPvalAA-v2.1 reports Elo; APEX-Agents-AA and AutomationBench report pass@1; JobBench reports its Main score; and Claw-Eval reports pass³. Pass metrics are shown as percentages.
| Model | GDPvalAA-v2.1 Elo | APEX-Agents-AA pass@1 | JobBench Main | AutomationBench pass@1 | Claw-Eval pass³ |
|---|---|---|---|---|---|
| Frontier models | |||||
| GLM-5.3 | 1651 | 35.2 | 48.48 | 48.20 | 72.2 |
| Qwen3.8-max | 1608 | 38.9 | 52.57 | 51.30 | 72.8 |
| DeepSeek V4.1 Flash-0910 | 1600 | 38.4 | 49.41 | 54.80 | 64.3 |
| Kimi K3 | 1533 | 35.5 | 43.81 | 46.70 | 69.3 |
| GLM-5.2 | 1374 | 29.7 | 33.79 | 20.30 | 67.9 |
| Models at or below 35B | |||||
| Sage-A1.5 | 675 | 14.8 | 14.32 | 8.78 | 65.0 |
| Apodex-1.1-mini | 883 | 23.5 | 16.31 | 14.30 | 45.1 |
| Qwen3.6-35B-A3B | 895 | 13.4 | 14.99 | 9.30 | 49.7 |
| Occamy-1.0 | 1088 | 20.5 | 18.88 | 27.60 | 71.1 |
| Nex-N2.5-mini | 1304 | 17.0 | 28.50 | 32.30 | 36.1 |
| Fx-Work | 1248 | 25.2 | 25.19 | 18.00 | 38.9 |
| Fx-Work-1028 | 1328 | 28.2 | 34.34 | 43.33 | 70.1 |
For integration with an agent workflow, we recommend serving Fx-Work-1028 through an OpenAI-compatible API using SGLang or vLLM. The examples use the model repository endless-frontier/Fx-Work-1028. Hugging Face authentication is required if repository access is restricted.
[!Important] Fx-Work-1028 follows Fx-Work's native context length of 262,144 tokens. If GPU memory is limited, reduce the context length; for long-horizon professional work, retaining at least 128K tokens is recommended.
SGLang provides an OpenAI-compatible server for high-throughput inference.
bash
python -m sglang.launch_server \ --model-path endless-frontier/Fx-Work-1028 \ --port 8000 \ --tp-size 8 \ --mem-fraction-static 0.8 \ --context-length 262144 \ --reasoning-parser qwen3vLLM provides a high-throughput OpenAI-compatible server.
bash
vllm serve endless-frontier/Fx-Work-1028 \ --port 8000 \ --tensor-parallel-size 8 \ --max-model-len 262144 \ --reasoning-parser qwen3Both commands expose an endpoint at http://localhost:8000/v1. Tool execution is provided by the surrounding agent harness; the model server exposes generation and, where configured, tool-call responses.
python
import torchfrom transformers import AutoProcessor, Qwen3_5MoeForConditionalGeneration model_id = "endless-frontier/Fx-Work-1028"processor = AutoProcessor.from_pretrained(model_id)model = Qwen3_5MoeForConditionalGeneration.from_pretrained( model_id, torch_dtype="auto", device_map="auto",) messages = [ { "role": "user", "content": "Summarize the key risks and next actions in this work request.", }]inputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True,).to(model.device) with torch.inference_mode(): output_ids = model.generate(**inputs, max_new_tokens=1024) new_tokens = output_ids[:, inputs["input_ids"].shape[1]:]print(processor.batch_decode(new_tokens, skip_special_tokens=True)[0])The processor supports the multimodal image and video message format provided by Qwen3.6.
If you use Fx-Work-1028, please cite:
bibtex
@article{zhu2026workgenesis, title={WorkGenesis: Building the Worlds That Teach Agents to Work}, author={Zhu, Xinyu and Liu, Fenyi and Cai, Yuzhu and Tang, Shuo and Ye, Rui and Zhang, Linfeng and Chen, Siheng}, journal={arXiv preprint arXiv:2609.39325}, year={2026}}Fx-Work-1028 is released under the Apache 2.0 license, subject to the terms of the base model and third-party components. See the Qwen3.6-35B-A3B license for the upstream terms.
python -m sglang.launch_server \ --model-path endless-frontier/Fx-Work-1028 \ --port 8000 \ --tp-size 8 \ --mem-fraction-static 0.8 \ --context-length 262144 \ --reasoning-parser qwen3vllm serve endless-frontier/Fx-Work-1028 \ --port 8000 \ --tensor-parallel-size 8 \ --max-model-len 262144 \ --reasoning-parser qwen3import torchfrom transformers import AutoProcessor, Qwen3_5MoeForConditionalGeneration model_id = "endless-frontier/Fx-Work-1028"processor = AutoProcessor.from_pretrained(model_id)model = Qwen3_5MoeForConditionalGeneration.from_pretrained( model_id, torch_dtype="auto", device_map="auto",) messages = [ { "role": "user", "content": "Summarize the key risks and next actions in this work request.", }]inputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True,).to(model.device) with torch.inference_mode(): output_ids = model.generate(**inputs, max_new_tokens=1024) new_tokens = output_ids[:, inputs["input_ids"].shape[1]:]print(processor.batch_decode(new_tokens, skip_special_tokens=True)[0])@article{zhu2026workgenesis, title={WorkGenesis: Building the Worlds That Teach Agents to Work}, author={Zhu, Xinyu and Liu, Fenyi and Cai, Yuzhu and Tang, Shuo and Ye, Rui and Zhang, Linfeng and Chen, Siheng}, journal={arXiv preprint arXiv:2609.39325}, year={2026}}