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}
}