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

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
NextSearch-1-S (this repo)	Qwen3.6-35B-A3B	35B-A3B MoE	weights
NextSearch-1-XS	Qwen3.5-9B	9B dense	weights

Technical report: nexttoken.co/research/nextsearch-1. Harness, evaluation suite, and audited benchmark golds: 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; full analysis in the technical report.

Quick start

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:

pip install nextsearch && nextsearch-eval run --benches seal0 --models nextsearch-1-s --n 10

Or plain transformers:

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.

License

Released under the Apache License 2.0, as is the base model Qwen/Qwen3.6-35B-A3B.

Citation

@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}
}

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Safetensors

Model size

36B params

Tensor type

BF16

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