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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
As Large Language Models (LLMs) approach human expert performance, their continued development is increasingly constrained by training tasks conceived within the limits of human knowledge. We argue that open-ended capability growth requires verifiable frontier tasks: problems at the boundary of current knowledge whose solutions can be objectively evaluated through formal methods, computation, simulation, or domain-specific tools. To this end, we introduce BigBang, a general-purpose LLM evolved from Qwen 3.6 35B-A3B through efficient post-training with an adversarial, self-evolving synthetic data framework. The framework contains two core components: (1) generator agents that continually propose and solve increasingly challenging scientific and technical problems, and (2) critic agents that evaluate correctness, difficulty, scalability, and diversity, while using held-out real research tasks to calibrate the evolving synthetic-data distribution. Through iterative generator–critic interaction, the framework constructs approximately 10,000 high-difficulty post-training examples across multiple domains. Despite the modest data scale, BigBang substantially outperforms its base model across scientific research, reasoning, coding, and tool-use benchmarks, achieving aggregate performance between DeepSeek V4 Flash (284B) and DeepSeek V4 Pro (1.6T). These results demonstrate that self-evolving synthesis of verifiable frontier tasks provides a promising path toward scalable and open-ended intelligence.
BigBang-V1 on eight representative benchmarks spanning long-horizon search, software engineering, scientific research, and AI research. BigBang-V1 obtains the highest reported score among the selected 35B models on all eight benchmarks. It even exceeds DeepSeek V4 Pro Preview (1.6T) on FrontierScience Research, Humanity's Last Exam, PaperBench(Code-Dev) and BioMysteryBench-HD.
Comparison of BigBang-V1 with representative closed- and open-source frontier models, together with models at the 35B scale, across benchmarks for long-horizon search, coding, scientific research, and AI research. The "-" indicates the score is not publicly available or not tested.
| Benchmark | ClaudeOpus 4.8 | Gemini3.1 Pro | GPT5.5 | GLM5.2 | DeepSeek V4Flash Preview | DeepSeek V4Pro Preview | Step-3.7Flash | Qwen3.635B-A3B | Nex-N2mini | AgentsA1 | Apodex1.0-mini | BigBangV1 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Long-horizon Search | ||||||||||||
| BrowseComp | 84.3 | 85.9 | 84.4 | 68.7 | 73.2 | 83.4 | 75.8 | 67.9 | 74.1 |
For streamlined integration, we recommend using BigBang-V1 via APIs. Below is a guide to use BigBang-V1 via OpenAI-compatible API.
BigBang-V1 can be served via APIs with popular inference frameworks. In the following, we show example commands to launch OpenAI-Compatible API servers for BigBang-V1 models.
[!Important] Inference efficiency and throughput vary significantly across frameworks. We recommend using the latest framework versions to ensure optimal performance and compatibility. For production workloads or high-throughput scenarios, dedicated serving engines such as SGLang, KTransformers or vLLM are strongly recommended.
[!Important] The model has a default context length of 262,144 tokens. If you encounter out-of-memory (OOM) errors, consider reducing the context window. However, because BigBang-V1 leverages extended context for complex tasks, we advise maintaining a context length of at least 128K tokens to preserve thinking capabilities.
SGLang is a fast serving framework for large language models and vision language models.
sglang>=0.5.10 is recommended for BigBang-V1, which can be installed using the following command in a fresh environment:
shell
uv pip install sglang[all]
See its documentation for more details.
The following will create API endpoints at http://localhost:8000/v1:
Standard Version: The following command can be used to create an API endpoint with maximum context length 262,144 tokens using tensor parallel on 8 GPUs.
shell
python -m sglang.launch_server --model-path endless-frontier/BigBang-v1 --port 8000 --tp-size 8 --mem-fraction-static 0.8 --context-length 262144 --reasoning-parser qwen3
Tool Use: To support tool use, you can use the following command.
shell
python -m sglang.launch_server --model-path endless-frontier/BigBang-v1 --port 8000 --tp-size 8 --mem-fraction-static 0.8 --context-length 262144 --reasoning-parser qwen3 --tool-call-parser qwen3_coder
Multi-Token Prediction (MTP): The following command is recommended for MTP:
shell
python -m sglang.launch_server --model-path endless-frontier/BigBang-v1 --port 8000 --tp-size 8 --mem-fraction-static 0.8 --context-length 262144 --reasoning-parser qwen3 --speculative-algo NEXTN --speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4
For detailed deployment guide, see the SGLang Qwen3.5 Cookbook.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
vllm>=0.19.0 is recommended for BigBang-V1, which can be installed using the following command in a fresh environment:
shell
uv pip install vllm --torch-backend=auto
See its documentation for more details.
The following will create API endpoints at http://localhost:8000/v1:
Standard Version: The following command can be used to create an API endpoint with maximum context length 262,144 tokens using tensor parallel on 8 GPUs.
shell
vllm serve endless-frontier/BigBang-v1 --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3
Tool Call: To support tool use, you can use the following command.
shell
vllm serve endless-frontier/BigBang-v1 --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder
Multi-Token Prediction (MTP): The following command is recommended for MTP:
shell
vllm serve endless-frontier/BigBang-v1 --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":2}'
Text-Only: The following command skips the vision encoder and multimodal profiling to free up memory for additional KV cache:
shell
vllm serve endless-frontier/BigBang-v1 --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --language-model-only
For detailed deployment guide, see the vLLM Qwen3.5 Recipe.
KTransformers is a flexible framework for experiencing cutting-edge LLM inference optimizations with CPU-GPU heterogeneous computing. For running BigBang-V1 with KTransformers, see the KTransformers Deployment Guide.
Hugging Face Transformers contains a lightweight server which can be used for quick testing and moderate load deployment.
The latest transformers is required for BigBang-V1:
shell
pip install "transformers[serving]"
See its documentation for more details. Please also make sure torchvision and pillow are installed.
Then, run transformers serve to launch a server with API endpoints at http://localhost:8000/v1; it will place the model on accelerators if available:
shell
transformers serve endless-frontier/BigBang-v1 --port 8000 --continuous-batching
| 48.5 |
| 73.9 |
| 76.5 |
| XBench | 61.4 | - | 72.4 | 65.8 | 62.2 | 64.8 | 50.8 | 32.6 | 57.2 | 52.4 | 61.8 | 58.4 |
| Coding Tasks |
| SWE-Bench Pro | 69.2 | 54.2 | 58.6 | 62.1 | 52.6 | 55.4 | 56.3 | 43.6 | 50.2 | 42.3 | 38.7 | 54.2 |
| SciCode-V-Sub | 92.3 | - | 95.1 | 84.3 | 83.7 | 90.2 | - | 56.5 | 39.0 | 64.1 | - | 68.6 |
| SciCode-V-Main | 78.1 | - | 90.6 | 70.3 | 68.6 | 78.1 | - | 26.6 | 15.6 | 50.0 | - | 50.0 |
| Scientific Research |
| FS-R | 45.2 | 24.8 | 58.3 | 52.4 | 37.7 | 40.7 | 37.2 | 11.9 | 36.8 | 38.4 | 29.6 | 46.2 |
| HLE | 57.9 | 51.4 | 52.2 | 54.7 | 45.1 | 48.2 | 47.2 | 36.2 | 38.4 | 46.3 | 45.3 | 50.3 |
| BioMystery-HS | 88.5 | - | 76.7 | 75.3 | 68.0 | 64.4 | 57.5 | 44.8 | 42.9 | 48.9 | 50.2 | 57.5 |
| BioMystery-HD | 42.4 | - | 23.5 | 21.6 | 23.5 | 13.7 | 11.8 | 2.0 | 5.9 | 2.0 | 5.9 | 15.7 |
| AI Research |
| MLE-Bench(Lite) | 63.6 | - | 59.1 | 72.7 | 40.9 | 59.1 | 40.9 | 31.8 | 18.2 | 27.3 | 27.3 | 59.1 |
| PaperBench(Code-Dev) | - | - | 64.2 | 63.6 | 40.4 | 50.4 | 36.7 | 30.7 | 14.8 | 17.3 | 20.5 | 53.6 |