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

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
NextSearch-1-S	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), 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; full analysis in the technical report.

Quick start

The weights are ~530 GB bf16 — plan for a multi-GPU node (e.g. 8×H200). See the vLLM recipe for Inkling for current serving flags; our serving notes (sampling, context caps, tool-call parsing pitfalls) are in docs/serving.md.

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:

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.

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

266B params

Tensor type

F32

BF16

Model tree for NextTokenAI/NextSearch-1-M

Base model

thinkingmachines/Inkling-Small

Finetuned

(6)

this model