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NextTokenAI/NextSearch-1-S

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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](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; full analysis in the technical report.

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:

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.

License

Released under the Apache License 2.0, as is the base model `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}
}