Instructions to use NextTokenAI/NextSearch-1-M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NextTokenAI/NextSearch-1-M with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="NextTokenAI/NextSearch-1-M")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("NextTokenAI/NextSearch-1-M")
model = AutoModelForMultimodalLM.from_pretrained("NextTokenAI/NextSearch-1-M", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use NextTokenAI/NextSearch-1-M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NextTokenAI/NextSearch-1-M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NextTokenAI/NextSearch-1-M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NextTokenAI/NextSearch-1-M

SGLang

How to use NextTokenAI/NextSearch-1-M with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "NextTokenAI/NextSearch-1-M" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NextTokenAI/NextSearch-1-M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "NextTokenAI/NextSearch-1-M" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NextTokenAI/NextSearch-1-M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use NextTokenAI/NextSearch-1-M with Docker Model Runner:
```
docker model run hf.co/NextTokenAI/NextSearch-1-M
```

NextSearch-1-M / README.md

nitkk

NextSearch-1 release

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	---
	license: apache-2.0
	base_model: thinkingmachines/Inkling-Small
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- agent
	- web-research
	- agentic-search
	- tool-use
	---

	# NextSearch-1-M

	NextSearch-1-M is the largest of the three NextSearch-1 web research
	agents: post-trained models that decompose a question, search and fetch from
	the live web, reconcile conflicting evidence, and return a concise answer or
	a structured research artifact. They are built to work as the research
	component inside a larger system — called repeatedly by an orchestrator —
	where per-call accuracy, tail latency, and cost compound.

	\| Model \| Base \| Params \| \|
	\|---\|---\|---\|---\|
	\| NextSearch-1-M (this repo) \| Inkling-Small \| 276B-A12B MoE \| [weights](https://huggingface.co/NextTokenAI/NextSearch-1-M) \|
	\| NextSearch-1-S \| Qwen3.6-35B-A3B \| 35B-A3B MoE \| [weights](https://huggingface.co/NextTokenAI/NextSearch-1-S) \|
	\| NextSearch-1-XS \| Qwen3.5-9B \| 9B dense \| [weights](https://huggingface.co/NextTokenAI/NextSearch-1-XS) \|

	Technical report: [nexttoken.co/research/nextsearch-1](https://nexttoken.co/research/nextsearch-1).
	Harness, evaluation suite, and audited benchmark golds:
	[github.com/NextTokenAI/nextsearch](https://github.com/NextTokenAI/nextsearch).

	## Results

	Live-web evaluation (August 2026), 12B-active M against frontier API
	anchors. Benchmarks: SEAL-0 (fresh/conflicting evidence, n=97), FRAMES
	(multi-constraint retrieval, n=100), DeepSearchQA (comprehensive answer
	sets, n=100), WideSearch-sub (structured table sub-tasks, n=49), and
	WideSearch (full tasks under the orchestrated harness, n=20). Best per
	column in bold.

	\| \| SEAL-0 \| FRAMES \| DeepSearchQA \| WideSearch-sub \| WideSearch \| mean $/ep \| mean turns \|
	\|---\|---\|---\|---\|---\|---\|---\|---\|
	\| NextSearch-1-M \| 0.515 \| 0.850 \| 0.803 \| 0.805 \| 0.708 \| $0.074 \| 5.9 \|
	\| glm-5.2 (355B-A32B) \| 0.505 \| 0.920 \| 0.790 \| 0.856 \| 0.806 \| $0.015 \| 7.5 \|
	\| gemini-3.6-flash \| 0.495 \| 0.880 \| 0.773 \| 0.885 \| 0.712 \| $0.127 \| 8.8 \|
	\| gpt-5.6-luna-med \| 0.484 \| 0.820 \| 0.788 \| 0.763 \| 0.742 \| $0.010 \| 6.9 \|
	\| deepseek-v4-flash \| 0.474 \| 0.820 \| 0.781 \| 0.682 \| 0.758 \| $0.029 \| 10.1 \|
	\| nemotron-3-ultra (550B-A55B) \| 0.423 \| 0.850 \| 0.693 \| 0.760 \| — \| $0.083 \| 9.3 \|

	All rows run under our harness (same tools, prompts, turn budgets,
	pinned task date) against audited golds with one shared judge —
	consistent within this table, not comparable to other papers'
	leaderboards. Protocol, costs, and reproduction:
	[docs/evals.md](https://github.com/NextTokenAI/nextsearch/blob/main/docs/evals.md);
	full analysis in the
	[technical report](https://nexttoken.co/research/nextsearch-1).

	## Quick start

	The weights are ~530 GB bf16 — plan for a multi-GPU node (e.g. 8×H200).
	See the [vLLM recipe for Inkling](https://recipes.vllm.ai/thinkingmachines/Inkling)
	for current serving flags; our serving notes (sampling, context caps,
	tool-call parsing pitfalls) are in
	[docs/serving.md](https://github.com/NextTokenAI/nextsearch/blob/main/docs/serving.md).

	```bash
	vllm serve NextTokenAI/NextSearch-1-M --tensor-parallel-size 8 \
	--enable-auto-tool-choice --max-model-len 65536
	```

	Recommended sampling: temperature 0.7, max 16k tokens per turn, reasoning
	effort 0.7. The model expects a task date in its system prompt and two
	tools (`search`, `fetch`); the exact prompts and tool schemas it was
	tuned for ship in the [harness](https://github.com/NextTokenAI/nextsearch):

	```bash
	pip install nextsearch && nextsearch-eval run --benches seal0 --models nextsearch-1-m --n 10
	```

	## License

	Released under the Apache License 2.0, as is the base model
	[`thinkingmachines/Inkling-Small`](https://huggingface.co/thinkingmachines/Inkling-Small).

	## Citation

	```bibtex
	@techreport{nextsearch1,
	title = {NextSearch-1: Open models for wide and deep web research},
	author = {Nitish Kulkarni and Alankar Jain},
	institution = {NextToken},
	year = {2026},
	url = {https://nexttoken.co/research/nextsearch-1}
	}
	```