Qwen3.8 vs Nemotron 3.5 vs Muse Glimmer: Best 30B Agent (2026)

The week of August 10, 2026, turned the local agent market into a three-way race. Meta shipped Muse Glimmer, a 30B model built for always-on local agents (Ollama library, 2026). NVIDIA shipped Nemotron 3.5 Lightning, a 30B mixture-of-experts model with 3B active parameters (Ollama library, 2026). Then Qwen shipped Qwen3.8-27B, a dense 27B vision-language model that reports a 73.0 on Terminal Bench 2.1 (model card, 2026). All three sit in the 24-32GB machine class, which is why the comparison matters for anyone running agents on a single Mac or GPU.

TL;DR: For multimodal agent work and coding, Qwen3.8-27B wins on the published numbers (Terminal Bench 2.1 73.0 per Qwen's card). Nemotron 3.5 Lightning wins on always-on efficiency with 3B active parameters. Muse Glimmer wins on tool-use reliability with an Apache-2.0 license. All three install with one Ollama command.

What Defines an Agent Model in 2026?

An agent model is one that can carry a multi-step task to completion: plan, call tools, read the result, and recover from failure. The three models here all target that workload, and their cards say so explicitly. Qwen3.8-27B highlights long-horizon agentic tasks (Qwen card, 2026). Nemotron 3.5 Lightning is described as built for always-on agents (Ollama library, 2026). Muse Glimmer is tuned for tool use, long tasks, and failure recovery (Ollama library, 2026).

Sizes measured from HuggingFace file trees in August 2026 (@@MODELFIT_7@@, @@MODELFIT_8@@, @@MODELFIT_9@@). Ollama tags verified against the registry, 200. Licenses per each model card.

Qwen3.8-27B: The Multimodal Agent

Qwen3.8-27B is the only one of the three that reads images and video natively, and the only one with published agentic coding scores. Its card reports Terminal Bench 2.1 at 73.0, up from 63.4 for Qwen3.6-27B, and SWE-bench Pro at 61.7 (Qwen card, 2026). The 262,144-token native context helps long-running agents keep their history, and MTP gives speculative decoding a head start. The trade-off is density: 27B active parameters per token make it the slowest of the three on paper, bandwidth-bound on a Mac.

Nemotron 3.5 Lightning: The Always-On Choice

Nemotron 3.5 Lightning is a sparse model: 30B total parameters with only 3B active per token (Ollama library, 2026). That 3B active budget is what keeps an always-on agent cheap to run, because speed scales with active parameters on bandwidth-bound machines. The cost is a bigger download: the UD-Q4_K_M build from unsloth is 25.27GB (unsloth quants, 2026), so the 24GB class is out and 32GB is the realistic entry. Community tests on r/LocalLLaMA describe it as fast with clean tool calling (thread, 2026), but no independent benchmark table exists yet, so treat those as impressions.

Muse Glimmer: The Reliability Play

Muse Glimmer is Meta's 30B answer for local agents, licensed Apache-2.0 and described by Ollama as running on a single GPU with tuning for tool use and failure recovery (Ollama library, 2026). Its Q4_K_M build is 17.31GB (bartowski quants, 2026), squarely in the 24GB class. Failure recovery is the differentiator: agents that retry and adapt matter more than raw benchmark speed for local automation. The catch is evidence: Muse Glimmer shipped days ago, and there is no third-party leaderboard entry yet.

Which One Should You Run?

Choose by workload, not by brand. If you build agents that read screens or documents, Qwen3.8-27B is the only native vision model here, and its agentic coding scores lead the group (Qwen card, 2026). If you run an always-on server that answers tool calls all day, Nemotron 3.5 Lightning's 3B active budget keeps the token cost low, but plan for 32GB. If you value license freedom and recovery behavior above scoreboards, Muse Glimmer matches Qwen's Apache-2.0 terms and fits the same 24GB boxes.

RAM classes from measured Q4 download sizes; all three fit comfortably at 64GB with room for context.

How to Run All Three

Installation is one command each, and the registry probes for all three tags return 200.

ollama run qwen3.8:27b
ollama run nemotron-3.5-lightning:30b
ollama run muse-glimmer:30b

Qwen3.8 also ships an MLX port (qwen3.8:27b-mlx) for Apple Silicon, detailed in the local install guide. The other two are GGUF-class builds running through Ollama's native runtime.

FAQ

Which 30B agent model is fastest on a Mac?

On paper, Nemotron 3.5 Lightning, because only 3B parameters are active per token. No independent Mac benchmark exists yet, so the claim comes from architecture, not measurement.

Do these models run on a 24GB Mac?

Qwen3.8-27B and Muse Glimmer fit at Q4, both around 17-18GB. Nemotron 3.5 Lightning needs 32GB because its Q4 download is 25.27GB.

Are there independent benchmarks for these models?

No. Qwen published agentic coding scores on its card, and the other two have no public leaderboard entries yet. Treat all scores as vendor claims until third-party results land.

What hardware can run a local agent stack?

The practical stack is one 32GB machine holding the model plus a tool loop. For sizing guidance, the how much RAM guide covers the budgeting rules used on ModelFit.

The Short Version

The 30B local agent tier just became real, with three credible options shipping in one week. Qwen3.8-27B is the pick for multimodal and coding agents, Nemotron 3.5 Lightning for always-on efficiency on 32GB, and Muse Glimmer for tool reliability under Apache-2.0. Start with the one matching your workload, and check back after independent benchmarks land. For the bigger picture, the 30B wave analysis and the Qwen3.5 hub keep the family history in one place.