Agents-A1: InternScience 35B MoE Agent Model — Long-Horizon Search, GAIA 96, and vLLM Setup

June 30, 2026 — 4:19 PM: ModelScope announced Agents-A1 on X — a 35B MoE agentic model from InternScience built for long-horizon search, engineering, scientific research, instruction following, and tool calling. Weights landed on Hugging Face the same day under Apache 2.0, with a technical report claiming trillion-parameter-class agent performance without trillion-parameter weights.

The launch sits in a crowded week: LongCat-2.0 from Meituan, ongoing Qwen 3.6 local-dev hype, and Fable 5 still offline. Agents-A1's pitch is different — not raw coding SWE scores alone, but heterogeneous agent horizons: search loops, science tools, instruction evals, and function-calling at 256K context.

TL;DR — what people asked on X

What InternScience claims

Paper title: Scaling the Horizon, Not the Parameters: Reaching Trillion-Parameter Performance with a 35B Agent (arxiv:2606.30616, dated June 29, 2026).

Two scaling axes:

1. Long-horizon trajectories — a domain-grounded knowledge-action infrastructure that jointly constructs actions, observations, and verifier outcomes so multi-step agent runs become trainable targets, not one-off demos.

2. Heterogeneous agent abilities — a three-stage pipeline:

   • Full-domain supervised fine-tuning for broad agent behaviors
   • Domain teacher models for specialized expertise (search vs science vs engineering)
   • Multi-teacher, multi-domain on-policy distillation with heterogeneity-aware optimization

That framing rhymes with Qwen 3.7-Max environment scaling and self-harness agent improvement — the 2026 consensus is that agent quality scales with trajectory diversity, not just parameter count.

InternScience also open-sourced an evaluation framework in the repo (Agents-A1/evaluation) so others can reproduce agent-capability scores under one protocol — a move toward the standardized eval hygiene we cover in the AI benchmarks guide.

Benchmark table — where 35B punches up

Legend from the model card: 🥇 overall SOTA · 🟢 best among ~35B comparables

Long-horizon search

explainx.ai read: GAIA 96 and BrowseComp 75.5 are the headline numbers for teams building search + tool agents — the same benchmark family LongCat-2.0 cites at 79.9 (different harness/protocol — always compare apples-to-apples).

Scientific research

Strongest story: research-agent tasks with tools — relevant for RAG + calculator + literature search stacks, not pure chat.

Instruction following

If your product fails on multi-constraint prompts (format + content + exclusions), this row matters more than MMLU.

Engineering / coding (the skeptical row)

X asked "What about coding?" — fairly. Agents-A1 wins its weight class but Kimi K2.6 and GPT-5.5 lead on SciCode. For repo-scale coding agents, prioritize Kimi K2.7-Code, LongCat-2.0, or dense local Qwen 3.6 27B until independent Terminal-Bench / SWE-bench runs appear.

Architecture and lineage

The qwen3_5_moe tag and ModelScope distribution place Agents-A1 in the Alibaba / Qwen ecosystem orbit — alongside Qwen-AgentWorld (35B-A3B world model, June 23) and the China free-models playbook.

AgentWorld vs Agents-A1:

X commenters suggesting "basically Qwen AgentWorld rebranded" oversimplify — but teams evaluating both should read AgentWorld's paper and Agents-A1's distillation story as complementary, not duplicate.

How to run Agents-A1 (vLLM and SGLang)

Weights are Transformers safetensors — use vLLM or SGLang, not llama.cpp until community GGUF quantizers ship.

SGLang — standard server

uv venv --python 3.12 --seed --managed-python
source .venv/bin/activate
uv pip install sglang

SGLANG_USE_MODELSCOPE=true python -m sglang.launch_server \
  --model-path InternScience/Agents-A1 \
  --port 8000 \
  --tp-size 1 \
  --mem-fraction-static 0.8 \
  --context-length 262144 \
  --reasoning-parser qwen3

OpenAI-compatible API: http://localhost:8000/v1

SGLang — tool calling

SGLANG_USE_MODELSCOPE=true python -m sglang.launch_server \
  --model-path InternScience/Agents-A1 \
  --port 8000 \
  --tp-size 1 \
  --mem-fraction-static 0.8 \
  --context-length 262144 \
  --reasoning-parser qwen3 \
  --tool-call-parser qwen3_coder

vLLM — standard server

uv venv --python 3.12 --seed --managed-python
source .venv/bin/activate
uv pip install vllm --torch-backend=auto

VLLM_USE_MODELSCOPE=true vllm serve InternScience/Agents-A1 \
  --port 8000 \
  --tensor-parallel-size 1 \
  --max-model-len 262144 \
  --reasoning-parser qwen3

vLLM — tool calling

VLLM_USE_MODELSCOPE=true vllm serve InternScience/Agents-A1 \
  --port 8000 \
  --tensor-parallel-size 1 \
  --max-model-len 262144 \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder

Text-only (save KV cache)

VLLM_USE_MODELSCOPE=true vllm serve InternScience/Agents-A1 \
  --port 8000 \
  --tensor-parallel-size 1 \
  --max-model-len 262144 \
  --reasoning-parser qwen3 \
  --language-model-only

Recommended sampling (from model card)

Wire to agent harnesses

Point any OpenAI-compatible harness at http://localhost:8000/v1:

• OpenCode — opencode.jsonc baseURL
• Codex OSS — local provider
• Claude Code / OpenClaw — if your stack supports custom OpenAI endpoints

For MCP-heavy loops, run the tool-call parser variant and validate with your real server set — benchmark tool rows don't guarantee clean JSON on your schema.

What X got right (and what to verify)

Reasonable hype

• IFEval / IFBench SOTA — instruction-following is a real product surface; numbers are strong.
• FrontierScience-Research 40.0 — large jump vs 35B peers (2.9–6.7 range in table).
• Open eval code — rare and useful; run it before procurement.

Skepticism to keep

• "256K is not enough for agent" — long-horizon agents blow context with tool payloads, retries, and state snapshots. 256K helps; state externalization and compaction still required.
• "7 steps before state drift" (Gregor's reply) — real-world agent runs fail on memory coherence, not just benchmark max scores.
• Coding — wait for Terminal-Bench 2.0 / SWE-bench reproductions on your harness before replacing Kimi/LongCat/Qwen dense locals.
• Vendor tables — when a model doesn't report a benchmark, InternScience says they evaluated under their protocol; cross-check against original vendor papers.

Where Agents-A1 fits in the 2026 open-agent ladder

Coding-first open MoE     →  LongCat-2.0, Kimi K2.7-Code
Local daily driver (dense)  →  Qwen 3.6 27B + llama.cpp
World-model / sim RL        →  Qwen-AgentWorld
Heterogeneous long-horizon  →  Agents-A1  ← this launch
Closed frontier (if allowed)→  GPT-5.5, Fable/Mythos (offline)

During the US export ban on Fable/Mythos, Apache 2.0 agent models that excel at search + science + instructions fill a different niche than GLM-5.2 coding plans — tier workloads like explainx.ai recommends in closed vs open alternatives.

Update — June 29, 2026: Apodex-1.0-mini — another Qwen3.5-35B-A3B agent fine-tune — topped FutureX at 59.17, beating Sonnet 4.6 and GPT-5.5 on future prediction. Agents-A1 targets heterogeneous benchmarks; Apodex targets verification-centric deep research.

Reproduction checklist

1. Pull weights — huggingface-cli download InternScience/Agents-A1 or ModelScope mirror
2. Serve with tools — vLLM/SGLang commands above on a GPU box with headroom for 262K KV
3. Clone eval — Agents-A1/evaluation from the GitHub repo linked on Hugging Face
4. Run your agent loop — same MCP/search tools you use in production
5. Compare MoE vs dense — if instruction-following regresses on repo tasks, try Qwen 3.6 27B dense locally
6. Log horizon length — steps until drift; benchmarks won't show this

Add verification loops from explainx.ai loops when agents drive CI.

Related on explainx.ai

• Qwen-AgentWorld — language world model — environment simulation vs agent policy
• Qwen 3.6 27B local dev — dense vs MoE — hands-on MoE skepticism
• LongCat-2.0 — 1.6T MoE coding agent — same-day launch, coding focus
• Kimi K2.7-Code — open coder alternative
• Qwen 3.7-Max — long-horizon autonomy — Alibaba agent scaling thesis
• Run open-source models in OpenCode — wire local APIs to harnesses
• AI benchmarks complete guide — GAIA, Terminal-Bench, how to read vendor tables
• Top Chinese AI companies 2026 — ModelScope / Qwen ecosystem map
• What are AI agents? — agent loop fundamentals

Official: Hugging Face — InternScience/Agents-A1 · ModelScope model page · Technical report (arxiv:2606.30616)

Benchmark figures and sampling defaults reflect the InternScience model card as of June 30, 2026. MoE serving requirements and independent coding evals may differ — verify before production. Last updated: June 30, 2026.