Agents-A1
The agent-tuned open MoE — built for long-horizon tool work, running free on your Mac.
What is Agents-A1?
Agents-A1 is the InternScience (open-weights, runs local) frontier model with a 262,144 tokens context window, released 2026-07. Tagline: The agent-tuned open MoE — built for long-horizon tool work, running free on your Mac.. Official source: internscience.github.io/Agents-A1.
Pricing detail. InternScience's agent-tuned Qwen3.5-MoE — 35B total with ~3B active per token, trained via three-stage multi-teacher distillation for long-horizon search, engineering and tool calling. The official Q4_K_M GGUF (21GB) runs fully offline on a 36GB Mac via Ollama.
How I use it inside the Agent OS. Benched one-shot on all 42 GoldieBench builds the week it dropped; a candidate brain for local Hermes agent loops.
What I built with Agents-A1
Every model on Goldie Bench gets the same fixed prompt set — one shot, single HTML file out — and I score the result 0–10 inside the Agent Operating System. Here's what Agents-A1 shipped on the bench: 45 one-shot demos across 262,144 tokens of context. Of those, 45 are scored against the field with my honest 0–10 from the source guides at agentos.guide.
Strengths
- Agent-tuned: claims SOTA on Seal-0 long-horizon search (56.36), IFBench instruction following (80.61) and BrowseComp in its class (75.51)
- 35B-class knowledge at ~3B-active speed (MoE) — 256K context, official GGUF on day one
- Runs fully local + free on a consumer Mac via Ollama
Trade-offs
- Agent tuning is aimed at search/tools/science — one-shot visual builds are not its headline lane
- 21GB Q4 build wants most of a 36GB Mac to itself
Best for
- Local agentic loops + tool calling
- Long-horizon research tasks
- Free offline agent work
Every benchmark — Agents-A1's full scorecard
All 45 scored tasks, best first — the judge's 0–10 on the same rubric as the whole field. Click any bar for that task's cross-model page, or open this scorecard in the interactive graphs. Full editorial breakdown with judge quotes and sourced outside research: the Agents-A1 deep dive →.
Every demo by Agents-A1
45 live demos, sorted by category. Click any tile to play the actual one-shot result. Verdicts and 0–10 scores are pulled from the source guides where I posted them publicly.
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVE
▶ LIVECompare Agents-A1 against every other model
Every head-to-head featuring Agents-A1. Verdicts shown for scored pairs.
See all 66 comparisons across every model →
Quick pill index
Direct comparisons against every other scored model on the bench:
Agents-A1 vs Fusion Agents-A1 vs Claude Opus 5 Agents-A1 vs Hermes MoA Agents-A1 vs GPT-5.6 Sol Agents-A1 vs Claude Fable 5 Agents-A1 vs Qwen 3.8 Agents-A1 vs Grok Agents-A1 vs MiniMax M3 Agents-A1 vs Fugu Ultra Agents-A1 vs Kimi K3 Agents-A1 vs GLM-5.2 Agents-A1 vs Fugu Mini Agents-A1 vs Muse Spark 1.2 Agents-A1 vs Opus 4.8 Agents-A1 vs Kimi K2.7 Agents-A1 vs Qwable 5 27B Coder Agents-A1 vs Gemini 3.6 Flash Agents-A1 vs Claude Sonnet 5 Agents-A1 vs Qwen 3.7 Agents-A1 vs Fugu Ultra 1.1 Agents-A1 vs Inkling Agents-A1 vs Grok 4.6 Agents-A1 vs Gemma 4 12B · MLX Agents-A1 vs Laguna XS 2.1 Agents-A1 vs Qwythos 9B Agents-A1 vs LongCat-2.0 Agents-A1 vs Hy3 Agents-A1 vs Gemma-4 12B CoderRead more on agentos.guide:
Agents-A1 — frequently asked
What is Agents-A1?
Agents-A1 is InternScience (open-weights, runs local)'s AI model — The agent-tuned open MoE — built for long-horizon tool work, running free on your Mac. It has a 256K tokens context window and was released 2026-07.
How good is Agents-A1 at coding and one-shot builds?
On the GoldieBench one-shot build benchmark it averages 4.83/10 across 45 scored tasks, with 0 gold, 0 silver and 0 bronze medals.
How much does Agents-A1 cost?
Free · runs locally. InternScience's agent-tuned Qwen3.5-MoE — 35B total with ~3B active per token, trained via three-stage multi-teacher distillation for long-horizon search, engineering and tool calling. The official Q4_K_M GGUF (21GB) run
Where can I see Agents-A1 demos?
Every one-shot build is live and playable on this page and on the GoldieBench compare matrix — same prompt as every other model, no retries.
Run this stack yourself.
Every demo on this bench was built inside the Agent Operating System — one prompt, one shot, single HTML file out. The Agent OS, the prompts, the templates, the weekly walkthroughs and 4,000+ founders shipping with it every day all live inside the AI Profit Boardroom.