BigBang-V1

Introduction

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.

Main Results

BigBang-V1 is evaluated on six representative benchmarks spanning long-horizon search, software engineering, and scientific research. It obtains the highest reported score among the selected 35B models on all six benchmarks. BigBang-V1 exceeds DeepSeek V4 Pro Preview on Frontier Science Research, Humanity's Last Exam, BioMysteryBench-HD, and PaperBench. The model has 35B total parameters with 3B activated during inference and is trained on approximately 10,000 post-training examples.

BigBang-V1 on six representative benchmarks. Click the figure to view the PDF.

Benchmark Results

Comparison with representative closed- and open-source frontier models, together with models at the 35B scale, across long-horizon search, coding, scientific research, and AI research benchmarks. - indicates that the score is not publicly available or was not tested.

Benchmark	Claude Opus 4.8	Gemini 3.1 Pro	GPT 5.5	GLM 5.2	DeepSeek V4 Flash Preview	DeepSeek V4 Pro Preview	Step-3.7 Flash	Qwen3.6 35B-A3B	Nex-N2 mini	Agents A1	Apodex 1.0-mini	BigBang V1
Long-horizon Search
BrowseComp	84.3	85.9	84.4	68.7	73.2	83.4	75.8	67.9	74.1	48.5	73.9	76.5
XBench	61.4	-	72.4	65.8	66.0	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	20.6	23.5	13.7	11.8	2.0	5.9	2.0	5.9	15.7
AI Research
MLE-Bench	50.0	-	54.5	72.7	40.9	59.1	40.9	31.8	4.5	27.3	27.3	36.4
PaperBench	-	-	64.0	64.0	40.0	55.0	37.0	31.0	15.0	17.0	21.0	54.0

Quickstart

For streamlined integration, we recommend using BigBang-V1 via APIs. Below is a guide to use BigBang-V1 via OpenAI-compatible API.

Serving BigBang-V1

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.

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.

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

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:

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.

  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.

  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:

  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

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:

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.

  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.

  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:

  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:

  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

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

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:

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:

transformers serve endless-frontier/BigBang-v1 --port 8000 --continuous-batching