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

Libraries

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

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

pipe = pipeline("text-generation", model="NextTokenAI/NextSearch-1-S")
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-S")
model = AutoModelForMultimodalLM.from_pretrained("NextTokenAI/NextSearch-1-S", 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-S with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NextTokenAI/NextSearch-1-S"
# 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-S",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use NextTokenAI/NextSearch-1-S 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-S" \
    --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-S",
		"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-S" \
        --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-S",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

NextSearch-1-S / README.md

nitkk

NextSearch-1 release

1bab341 9 days ago

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	---
	license: apache-2.0
	base_model: Qwen/Qwen3.6-35B-A3B
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- agent
	- web-research
	- agentic-search
	- tool-use
	---

	# NextSearch-1-S

	NextSearch-1-S is the mid-size NextSearch-1 web research agent: a
	post-trained model that decomposes a question, searches and fetches from the
	live web, reconciles conflicting evidence, and returns a concise answer or a
	structured research artifact. The family is 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. S is the family's
	serving sweet spot: MoE inference economics with near-M accuracy on breadth
	benchmarks.

	\| Model \| Base \| Params \| \|
	\|---\|---\|---\|---\|
	\| NextSearch-1-M \| Inkling-Small \| 276B-A12B MoE \| [weights](https://huggingface.co/NextTokenAI/NextSearch-1-M) \|
	\| NextSearch-1-S (this repo) \| 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), against open models of its class.
	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-S (avg@2) \| 0.381 \| 0.830 \| 0.620 \| 0.737 \| 0.730 \| $0.072 \| 7.0 \|
	\| inkling-med (API) \| 0.433 \| 0.810 \| 0.689 \| 0.683 \| 0.709 \| $0.052 \| 8.2 \|
	\| qwen3.6-35b-a3b (base) \| 0.402 \| 0.790 \| 0.638 \| 0.732 \| 0.619 \| $0.025 \| 9.2 \|
	\| nemotron-3-super (120B-A12B) \| 0.320 \| 0.740 \| 0.538 \| 0.462 \| — \| $0.028 \| 10.9 \|
	\| gemma-4-31b \| 0.155 \| 0.670 \| 0.565 \| 0.667 \| — \| $0.013 \| 5.0 \|
	\| gpt-oss-120b \| 0.227 \| 0.670 \| 0.450 \| 0.406 \| — \| $0.018 \| 9.3 \|
	\| tongyi-dr-30b (30B-A3B) † \| 0.351 \| 0.780 \| 0.407 \| 0.322 \| 0.388 \| $0.011\* \| 20.4 \|
	\| quest-35b-rl (35B-A3B) † \| 0.371 \| 0.740 \| 0.467 \| 0.157 \| 0.382 \| $0.023\* \| 24.0 \|

	The table above is the conservative arm (parallel search backend). **With
	the recommended exa-auto backend, S's four-bench mean rises ~+10pp to
	0.738** (all four benches up) at ~1.3× episode cost; see the serving
	notes.

	† published deep-research baselines, self-hosted under our harness; their
	rows ran under a more generous turn budget than the rest of the table
	(upper bounds). \* self-hosted: $/ep excludes GPU time. 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 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

	```bash
	vllm serve NextTokenAI/NextSearch-1-S \
	--enable-auto-tool-choice --tool-call-parser hermes --max-model-len 65536
	```

	Recommended sampling: temperature 0.7, max 16k tokens per turn, thinking
	on. 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-s --n 10
	```

	Or plain `transformers`:

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	model = AutoModelForCausalLM.from_pretrained(
	"NextTokenAI/NextSearch-1-S", torch_dtype="auto", device_map="auto")
	tokenizer = AutoTokenizer.from_pretrained("NextTokenAI/NextSearch-1-S")
	```

	Serving pitfalls that fail silently (tool-call parsing, context caps,
	thinking retention):
	[docs/serving.md](https://github.com/NextTokenAI/nextsearch/blob/main/docs/serving.md).

	## License

	Released under the Apache License 2.0, as is the base model
	[`Qwen/Qwen3.6-35B-A3B`](https://huggingface.co/Qwen/Qwen3.6-35B-A3B).

	## 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}
	}
	```