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}
}