IBM Granite 4.1 and Meta Llama 4 Scout represent two fundamentally different approaches to building open-weight models. Granite uses a dense transformer where every parameter fires on every token. Llama 4 Scout uses Mixture-of-Experts (MoE) to activate only a fraction of its parameters per token. Both are open-weight, both target coding, and both push context windows to extreme lengths. Here’s how they compare.

At a glance

The most striking difference is the architecture. Granite 4.1 30B activates all 30 billion parameters on every token — predictable, consistent, no routing overhead. Llama 4 Scout has 109B total parameters but only activates ~17B per token through its MoE routing, giving it more total knowledge while keeping per-token compute manageable.

Dense vs MoE: what it means in practice

Why IBM chose dense

IBM deliberately avoided MoE for Granite 4.1. Dense models have several practical advantages:

• Predictable latency — every token takes the same compute. No variance from expert routing decisions.
• Simpler deployment — no expert parallelism needed. Standard tensor parallelism works.
• Stable memory usage — VRAM consumption is constant regardless of input content.
• Easier fine-tuning — all parameters participate in every forward pass, so gradient updates are straightforward.

IBM’s previous Granite 4.0-H-Small was a 32B MoE with 9B active parameters. Granite 4.1 8B (dense) matches or beats it on most benchmarks — IBM proved they could get MoE-level performance from a smaller dense model through better training.

Why Meta chose MoE

Llama 4 Scout uses MoE to pack more knowledge into a model that’s still affordable to run:

• More total parameters — 109B of learned knowledge, but only ~17B active per token.
• Better knowledge coverage — different experts specialize in different domains.
• Extreme context — the 10M token context window is enabled partly by the efficiency of sparse activation.
• Scaling path — MoE scales more efficiently than dense models at the frontier.

The tradeoff is complexity. MoE models need more total VRAM (you load all 109B parameters even though only 17B fire), and expert routing can introduce latency variance.

Benchmark comparison

Granite 4.1 30B outperforms Llama 4 Scout on coding benchmarks by a significant margin. HumanEval (89.63 vs ~81), EvalPlus (82.7 vs ~75), and tool calling (73.68 vs ~65) all favor Granite. The gap is especially wide on BFCL V3 tool calling — Granite leads by nearly 9 points.

On general knowledge (MMLU, MMLU-Pro), the models are closer. Llama 4 Scout’s 109B total parameters give it broad knowledge coverage, but Granite’s focused training pipeline extracts more coding performance from fewer parameters.

Coding performance

For code generation, Granite 4.1 30B is the clear winner. The numbers tell the story:

• HumanEval pass@1: 89.63 (Granite) vs ~81 (Scout)
• EvalPlus: 82.7 vs ~75
• BFCL V3 tool calling: 73.68 vs ~65

IBM’s training approach — LLM-as-Judge data filtering, 4-stage RL pipeline, and progressive data annealing — produces a model that’s specifically strong at structured code generation and function calling. Granite 4.1 30B leads the BFCL V3 benchmark among all open-weight models in its class.

Llama 4 Scout’s strength is in large codebase reasoning. With its 10M token context window, Scout can ingest entire repositories and reason across files in ways that Granite’s 512K window can’t match. If your workflow involves analyzing massive codebases or understanding cross-file dependencies at scale, Scout’s context advantage is real.

Winner: Granite 4.1 30B for code generation and tool calling. Llama 4 Scout for large-codebase reasoning. 🏆

Context window: 512K vs 10M

This is the most dramatic difference between the two models. Granite 4.1 30B supports 512K tokens. Llama 4 Scout supports 10 million tokens — roughly 20x more.

In practice, 512K tokens is already enormous. It covers:

• ~400K words of text
• Most entire codebases (excluding node_modules)
• Full books, legal documents, or research paper collections

Llama 4 Scout’s 10M context is in a different league entirely. It can theoretically process:

• Entire monorepos with hundreds of files
• Complete documentation sets
• Multi-year conversation histories

However, there are practical caveats. Processing 10M tokens requires massive memory and compute. Few real-world tasks actually need that much context. And long-context performance degrades — models struggle to attend to information buried deep in multi-million token inputs.

Granite 4.1’s 512K context is more practical for most use cases, and IBM’s staged extension approach (32K → 128K → 512K with model merging) ensures quality doesn’t degrade at shorter lengths. The 30B scores 85.2 on RULER at 32K, 84.6 at 64K, and 76.7 at 128K — graceful degradation rather than a cliff.

Winner: Llama 4 Scout on raw context length. Granite 4.1 on practical long-context quality. 🏆

Hardware requirements

This is where the dense vs MoE tradeoff becomes concrete:

Model	Total VRAM (FP16)	Active compute per token	Practical deployment
Granite 4.1 30B	~60 GB	30B params	Single A100 80GB or 2× A6000
Llama 4 Scout	~220 GB	~17B params	Multi-GPU (3× A100 80GB minimum)

Granite 4.1 30B needs ~60 GB of VRAM at FP16 — it fits on a single A100 80GB or two consumer GPUs. With FP8 quantization (officially supported by IBM), it drops to ~30 GB, fitting on a single A6000 or high-end consumer GPU.

Llama 4 Scout needs ~220 GB of VRAM to load all 109B parameters, even though only ~17B are active per token. You need at least 3× A100 80GB GPUs. Quantization helps but still requires multi-GPU setups.

For local development, Granite 4.1 30B is far more accessible. It runs on a Mac Studio with 64 GB unified memory or a single high-end GPU. Scout requires server-grade hardware.

Winner: Granite 4.1 — dramatically lower hardware requirements. 🏆

License comparison

This matters more than most developers realize.

Granite 4.1 uses Apache 2.0 — the most permissive widely-used open-source license. No restrictions on commercial use, modification, redistribution, or derivative works. You can fine-tune it, embed it in proprietary products, and never mention IBM.

Llama 4 Scout uses Meta’s Llama 4 Community License. It’s permissive for most use cases but includes restrictions:

• Companies with 700M+ monthly active users need a separate license from Meta
• There are usage restrictions around certain applications
• The license is not OSI-approved open source

For startups, mid-size companies, and most enterprises, the Llama license is fine. But for organizations that need true open-source licensing for compliance reasons — or that might grow past the MAU threshold — Apache 2.0 is safer.

IBM also adds enterprise trust features on top of Apache 2.0: cryptographic signing, ISO certification, and Guardian safety models. These don’t restrict the license but add verification and compliance tools.

Winner: Granite 4.1 — Apache 2.0 is strictly more permissive. 🏆

Enterprise readiness

Granite 4.1 is built for enterprise deployment:

• Apache 2.0 — no licensing surprises at scale
• Cryptographic signing — verify model integrity
• ISO certified AI Management System
• Guardian models — separate safety/guardrail models
• IBM AI Risk Atlas integration
• watsonx.ai managed deployment with SLAs
• Full model family — language, vision, speech, guardian, embedding from one vendor

Llama 4 Scout has Meta’s backing and broad ecosystem support, but lacks the enterprise compliance tooling. There’s no equivalent to Guardian models, no cryptographic signing, and the license has the MAU restriction.

For regulated industries (finance, healthcare, government), Granite’s enterprise trust stack is a significant advantage.

Winner: Granite 4.1 🏆

Deployment options

Platform	Granite 4.1 30B	Llama 4 Scout
Ollama	✅	✅
HuggingFace	✅	✅
vLLM	✅	✅
LM Studio	✅	✅
OpenRouter	✅	✅
Cloud managed	watsonx.ai	Meta AI, various clouds

Both models have broad platform support. The practical difference is that Granite 4.1 30B is much easier to self-host due to lower hardware requirements.

Which should you pick?

Use case	Pick
Code generation	Granite 4.1 30B
Tool calling / function calling	Granite 4.1 30B (leads BFCL V3)
Analyzing massive codebases	Llama 4 Scout (10M context)
Single-GPU deployment	Granite 4.1 30B
Enterprise / regulated industry	Granite 4.1 30B
True open-source license	Granite 4.1 30B (Apache 2.0)
Multimodal (text + images)	Llama 4 Scout (native)
Budget hardware	Granite 4.1 8B or 3B
General knowledge breadth	Llama 4 Scout (109B total params)

Bottom line

Granite 4.1 30B wins on coding benchmarks, tool calling, hardware efficiency, licensing, and enterprise readiness. Llama 4 Scout wins on raw context length and total knowledge breadth from its 109B parameters.

For most developers, Granite 4.1 30B is the more practical choice. It’s easier to deploy, cheaper to run, and better at the coding tasks that matter most. Llama 4 Scout’s 10M context window is impressive but rarely needed in practice, and its hardware requirements put it out of reach for local development.

If you need to process truly massive codebases or documents, Scout’s context advantage is real. For everything else, Granite 4.1 delivers more coding performance per dollar of compute.

For setup details, see our Granite 4.1 complete guide and Llama 4 complete guide. Want to run Llama locally? Check how to run Llama 4 locally.

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FAQ

Is Granite 4.1 better than Llama 4 Scout for coding?

Yes, on benchmarks. Granite 4.1 30B scores 89.63 on HumanEval vs Scout’s ~81, 82.7 on EvalPlus vs ~75, and 73.68 on BFCL V3 tool calling vs ~65. IBM’s dense architecture and focused training pipeline produce stronger code generation despite having fewer total parameters. Scout’s advantage is in large-codebase reasoning thanks to its 10M token context.

Why is Granite dense while Llama 4 is MoE?

Different design philosophies. IBM prioritizes predictable latency, simpler deployment, and easier fine-tuning — all advantages of dense models. Meta uses MoE to pack more knowledge (109B parameters) into a model with manageable per-token compute (~17B active). IBM proved with Granite 4.1 that better training can match MoE performance from a smaller dense model.

Can I run both locally?

Granite 4.1 30B runs on a single high-end GPU (A100 80GB) or a Mac Studio with 64 GB unified memory. With FP8 quantization, it fits on ~30 GB VRAM. Llama 4 Scout needs ~220 GB VRAM for its 109B parameters — that’s 3+ A100 GPUs minimum. For local development, Granite is far more accessible. Consider Granite 4.1 8B or 3B for consumer hardware.

Which license is better for commercial use?

Granite 4.1’s Apache 2.0 is strictly more permissive. No restrictions whatsoever on commercial use, modification, or redistribution. Llama 4’s Community License restricts companies with 700M+ monthly active users and isn’t OSI-approved open source. For most companies the Llama license is fine, but Apache 2.0 eliminates any licensing risk.

Does Llama 4 Scout’s 10M context actually work well?

It works, but with caveats. Processing millions of tokens requires massive compute and memory. Performance degrades on information buried deep in very long contexts — this is a fundamental limitation of current attention mechanisms. For most practical tasks, Granite 4.1’s 512K context is more than sufficient and delivers more reliable long-context quality, scoring 85.2 on RULER at 32K and 76.7 at 128K.

Which is better for tool calling and API integration?

Granite 4.1 30B leads the BFCL V3 tool calling benchmark at 73.68, nearly 9 points ahead of Llama 4 Scout (~65). If your application relies on structured function calling, API integration, or agent tool use, Granite is the clear choice. IBM specifically optimized for this use case in their training pipeline.

How do the smaller Granite models compare to Scout?

Granite 4.1 8B scores 87.2 on HumanEval and 68.27 on BFCL V3 — competitive with Scout on coding despite being a fraction of the size. The 8B runs on a 16 GB GPU. For developers who don’t need Scout’s extreme context window, Granite 4.1 8B offers remarkable coding performance on consumer hardware.

Related: Granite 4.1 complete guide · Llama 4 complete guide · How to run Llama 4 locally