GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
endless-frontier
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
endless-frontier
Model Tree
Qwen/Qwen3.6-35B-A3B
Input Modalities
Output Modalities
Supported Functionality
Fx-Phys achieves the strongest results among the evaluated 35B-class models on all six reported benchmarks. It also surpasses DeepSeek-V4-Pro-Preview on all six benchmarks in this comparison.
Compared with Qwen3.6-35B-A3B, Fx-Phys improves the original PRL-Bench score from 26.61 to 37.93 and the PRL-Bench Verified score from 35.45 to 58.00. On Frontier Science, it raises the Research (Physics) score from 8.5 to 53.5 and the Olympiad (Physics) score from 64.8 to 74.0.
Scores are reproduced from the accompanying results figure; higher is better within each benchmark. The physics scores retain the figure's 0–100 scale, while the finance scores retain its 0–1 scale. -- indicates that no result is shown for that model and benchmark in the figure. Bold marks the best value among the evaluated 35B-class models, not the best value across all model sizes.
PRL-Bench denotes the original benchmark. PRL-Bench Verified is the version revised and corrected by ByteDance. Results for these two benchmark variants are reported separately.
| Model | PRL-Bench (Original) | PRL-Bench Verified | Frontier Science — Research (Physics) | Frontier Science — Olympiad (Physics) |
|---|---|---|---|---|
| Larger than 35B or closed-source models | ||||
| DeepSeek-V4-Pro-Preview | 30.90 | 48.31 | 34.5 | 72.8 |
| DeepSeek-V4.1-Flash | 35.53 | 67.55 | 61.5 | 80.0 |
| GLM-5.3 | 44.56 | 68.99 | 54.0 | 79.6 |
| GPT-5.5 | 45.74 | 67.65 | 56.8 | 72.0 |
| GLM-5.2 | 46.43 | -- | 47.5 | 77.6 |
| Qwen3.8-Flash | 52.60 | 64.74 | 61.0 | 75.6 |
| GPT-6-Astra | -- | 70.05 | -- | -- |
| 35B-class models | ||||
| Apodex-mini-1.0 | 13.44 | 19.96 | 19.0 | 70.0 |
| Nex-N2-mini | 24.78 | 33.84 | 30.0 | 64.0 |
| Qwen3.6-35B-A3B | 26.61 | 35.45 | 8.5 | 64.8 |
| Fx-Phys | 37.93 | 58.00 | 53.5 | 74.0 |
| Model | Finance Agent v2 | Frontier Finance |
|---|---|---|
| Larger than 35B or closed-source models | ||
| DeepSeek-V4-Pro-Preview | 0.842 | 0.510 |
| DeepSeek-V4.1-Flash | 0.924 | 0.610 |
| GLM-5.2 | 0.928 | 0.498 |
| Qwen3.8-Flash | 0.933 | 0.603 |
| 35B-class models | ||
| Apodex-mini-1.0 | 0.827 | 0.430 |
| Nex-N2-mini | 0.758 | 0.474 |
| Qwen3.6-35B-A3B | 0.794 | 0.376 |
| Fx-Phys | 0.868 | 0.518 |
These are the reported comparison results, not a claim of a controlled re-evaluation under identical settings. Exact reproduction requires the corresponding benchmark versions, prompts, decoding settings, and, where applicable, tool environments; the quickstart below is an inference example rather than an evaluation recipe.
| Property | Value |
|---|---|
| Base model | Qwen/Qwen3.6-35B-A3B |
| Architecture | Qwen3_5MoeForConditionalGeneration |
| Total parameters | 35B |
| Activated parameters | Approximately 3B per token |
| Transformer layers | 40 |
| Routed experts | 256, with 8 selected per token |
| Shared experts | 1 per MoE layer |
| Native context length | 262,144 tokens |
| Weight precision | BF16 |
| Model format | Hugging Face Transformers / Safetensors |
The examples below assume you have downloaded this repository and are running commands from its root directory. Use a Transformers, SGLang, or vLLM build with support for Qwen3_5MoeForConditionalGeneration.
For the serving examples, set the model path to the current directory. After publication, you can use the model's Hugging Face repository ID instead.
bash
export MODEL_PATH="$PWD"[!Important] The model configuration supports a native context length of 262,144 tokens. The serving examples use tensor parallelism across 8 GPUs; adjust the parallelism and context length for your hardware and workload. If GPU memory is limited, reduce the context length.
bash
python -m sglang.launch_server \ --model-path "$MODEL_PATH" \ --port 8000 \ --tp-size 8 \ --mem-fraction-static 0.8 \ --context-length 262144 \ --reasoning-parser qwen3bash
vllm serve "$MODEL_PATH" \ --port 8000 \ --tensor-parallel-size 8 \ --max-model-len 262144 \ --reasoning-parser qwen3Both commands expose a compatible API endpoint at http://localhost:8000/v1. To enable automatic tool-call parsing in vLLM, also pass --enable-auto-tool-choice --tool-call-parser qwen3_coder. Actual tool execution requires an external agent harness; the model server does not execute tools by itself.
python
import torchfrom transformers import AutoProcessor, Qwen3_5MoeForConditionalGeneration model_id = "."processor = AutoProcessor.from_pretrained(model_id)model = Qwen3_5MoeForConditionalGeneration.from_pretrained( model_id, torch_dtype="auto", device_map="auto",) messages = [ { "role": "user", "content": ( "Derive the equations of motion for two identical masses connected " "by three identical springs between fixed walls. Identify and " "interpret the normal modes." ), }]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=2048, do_sample=True, temperature=1.0, top_p=0.95, top_k=20, ) new_tokens = output_ids[:, inputs["input_ids"].shape[1]:]print(processor.batch_decode(new_tokens, skip_special_tokens=True)[0])The sampling parameters above match the released generation_config.json; they are not a specification of the benchmark evaluation settings. The model retains the base architecture's vision encoder and multimodal processor, but the results reported here do not constitute a separate image or video capability evaluation.
Benchmark performance does not guarantee correct solutions on unseen problems. Physics derivations, numerical calculations, and finance-related outputs can contain errors and should be independently verified. Tool-assisted workflows additionally depend on the surrounding tools, data, and execution environment.
Fx-Phys is released under the Apache 2.0 license. See LICENSE for the included license and the Qwen3.6-35B-A3B license for the upstream terms.
python -m sglang.launch_server \ --model-path "$MODEL_PATH" \ --port 8000 \ --tp-size 8 \ --mem-fraction-static 0.8 \ --context-length 262144 \ --reasoning-parser qwen3vllm serve "$MODEL_PATH" \ --port 8000 \ --tensor-parallel-size 8 \ --max-model-len 262144 \ --reasoning-parser qwen3import torchfrom transformers import AutoProcessor, Qwen3_5MoeForConditionalGeneration model_id = "."processor = AutoProcessor.from_pretrained(model_id)model = Qwen3_5MoeForConditionalGeneration.from_pretrained( model_id, torch_dtype="auto", device_map="auto",) messages = [ { "role": "user", "content": ( "Derive the equations of motion for two identical masses connected " "by three identical springs between fixed walls. Identify and " "interpret the normal modes." ), }]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=2048, do_sample=True, temperature=1.0, top_p=0.95, top_k=20, ) new_tokens = output_ids[:, inputs["input_ids"].shape[1]:]print(processor.batch_decode(new_tokens, skip_special_tokens=True)[0])