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0G-AI
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
0G-AI
Model Tree
Qwen/Qwen3.6-35B-A3B
Input Modalities
Output Modalities
Supported Functionality
| Benchmark | Covenant-72B | Qwen 3.5 35B | Qwen 3.6 35B | 0GM-1.0-35B |
|---|---|---|---|---|
| MMLU-Pro (4k context) | 41.84% | 61.43% | 75.75% | 77.62% |
| AIME 2026 (Pass@1) | 0.00% | 76.67% | 70.00% | 83.33% |
| GSM-8K | 70.81% | 80.06% | 96.74% | 96.82% |
| MATH-500 | 51.80% | 91.20% | 95.60% | 95.80% |
| Token Length (MATH-500, rel.) | 6.22% | 100.00% (6,804) | 96.60% | 94.91% |
0GM-1.0-35B-A3B leads on every accuracy benchmark, with the largest absolute gain on AIME 2026 (+13.33 points over Qwen 3.6 35B, +6.66 over Qwen 3.5 35B). Token usage on MATH-500 drops slightly versus the 3.6 dense baseline while accuracy goes up — i.e., shorter chains, higher correctness. Covenant-72B's near-zero output length and floor-level AIME score reflect its non-reasoning-tuned baseline rather than a capability ceiling at that parameter count.
0GM-1.0-35B-A3B outperforms Qwen 3.6 35B-A3B on 12 out of 14 MMLU-Pro subjects, with the largest gains in physics (+5.1), philosophy (+4.8), engineering (+4.4), and computer science (+2.5). Under a constrained 4k context budget, these improvements indicate stronger reasoning capability while using fewer tokens.
| Subject | Covenant-72B | Qwen 3.5 35B | Qwen 3.6 35B | 0GM-1.0-35B |
|---|---|---|---|---|
| Math | 41.6% | 71.4% | 82.4% | 83.9% |
| Physics | 36.8% | 61.9% | 74.2% | 79.3% |
| Biology | 69.5% | 79.1% | 91.1% |
For streamlined integration, we recommend using APIs. Below is a guide to use OpenAI-compatible API.
0GM can be served via APIs with popular inference frameworks. In the following, we show example commands to launch OpenAI-Compatible API servers.
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 this model 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, which can be installed using the following command in a fresh environment:
bash
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.
bash
python -m sglang.launch_server --model-path 0G-AI/0GM-1.0-35B-A3B-0427 --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.
bash
python -m sglang.launch_server --model-path 0G-AI/0GM-1.0-35B-A3B-0427 --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:
bash
python -m sglang.launch_server --model-path 0G-AI/0GM-1.0-35B-A3B-0427 --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
KTransformers is a flexible framework for experiencing cutting-edge LLM inference optimizations with CPU-GPU heterogeneous computing. For running 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:
bash
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:
bash
transformers serve 0G-AI/0GM-1.0-35B-A3B-0427 --port 8000 --continuous-batching
The chat completions API is accessible via standard HTTP requests or OpenAI SDKs. Here, we show examples using the OpenAI Python SDK.
Before starting, make sure it is installed and the API key and the API base URL is configured, e.g.:
bash
pip install -U openai# Set the following accordinglyexport OPENAI_BASE_URL="http://localhost:8000/v1"export OPENAI_API_KEY="EMPTY"
We recommend using the following set of sampling parameters for generation:
- Thinking mode for general tasks:
temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0- Thinking mode for precise coding tasks (e.g. WebDev):
temperature=0.6, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0- Instruct (or non-thinking) mode:
temperature=0.7, top_p=0.80, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0Please note that the support for sampling parameters varies according to inference frameworks.
0GM-1.0-35B-A3B-0427 models operate in thinking mode by default, generating thinking content signified by
<think>\n...</think>\n\nbefore producing the final responses. To disable thinking content and obtain direct response, refer to the examples here.
| Economics | 53.7% | 75.5% | 87.2% | 87.8% |
| Chemistry | 31.1% | 55.3% | 72.1% | 74.6% |
| Business | 42.3% | 72.0% | 78.8% | 81.9% |
| Psychology | 57.4% | 74.8% | 84.5% | 85.0% |
| Computer Science | 43.2% | 69.5% | 80.7% | 83.2% |
| Health | 48.3% | 62.8% | 79.5% | 79.0% |
| Other | 44.5% | 68.2% | 77.7% | 79.0% |
| Philosophy | 42.7% | 62.5% | 80.2% | 85.0% |
| History | 40.9% | 58.0% | 74.8% | 77.2% |
| Engineering | 25.0% | 33.3% | 42.0% | 46.4% |
| Law | 27.6% | 31.1% | 67.5% | 66.0% |
| Avg | 43.19% | 62.53% | 75.7% | 77.4% |