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Grok 4 Heavy vs Llama 4 Scout 17B Instruct

Grok 4 Heavy (2025) and Llama 4 Scout 17B Instruct (2026) are general-purpose language models from xAI and AI at Meta. Grok 4 Heavy ships a 256k-token context window, while Llama 4 Scout 17B Instruct ships a not-yet-sourced context window. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.

Llama 4 Scout 17B Instruct is safer overall; choose Grok 4 Heavy when provider fit matters.

Specs

Specification
Released2025-07-092026-01-01
Context window256k
Parameters
Architecture--
LicenseProprietaryProprietary
Knowledge cutoff--

Pricing and availability

Pricing attributeGrok 4 HeavyLlama 4 Scout 17B Instruct
Input price-$0.17/1M tokens
Output price-$0.66/1M tokens
Providers-

Capabilities

CapabilityGrok 4 HeavyLlama 4 Scout 17B Instruct
VisionNoNo
MultimodalYesYes
ReasoningNoNo
Function callingNoNo
Tool useNoNo
Structured outputsNoYes
Code executionNoNo

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint differs most on structured outputs: Llama 4 Scout 17B Instruct. Both models share multimodal input, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.

Pricing coverage is uneven: Grok 4 Heavy has no token price sourced yet and Llama 4 Scout 17B Instruct has $0.17/1M input tokens. Provider availability is 0 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.

Choose Grok 4 Heavy when provider fit are central to the workload. Choose Llama 4 Scout 17B Instruct when provider fit and broader provider choice are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.

FAQ

Is Grok 4 Heavy or Llama 4 Scout 17B Instruct open source?

Grok 4 Heavy is listed under Proprietary. Llama 4 Scout 17B Instruct is listed under Proprietary. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.

Which is better for multimodal input, Grok 4 Heavy or Llama 4 Scout 17B Instruct?

Both Grok 4 Heavy and Llama 4 Scout 17B Instruct expose multimodal input. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.

Which is better for structured outputs, Grok 4 Heavy or Llama 4 Scout 17B Instruct?

Llama 4 Scout 17B Instruct has the clearer documented structured outputs signal in this comparison. If structured outputs is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Where can I run Grok 4 Heavy and Llama 4 Scout 17B Instruct?

Grok 4 Heavy is available on the tracked providers still being sourced. Llama 4 Scout 17B Instruct is available on AWS Bedrock. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

When should I pick Grok 4 Heavy over Llama 4 Scout 17B Instruct?

Llama 4 Scout 17B Instruct is safer overall; choose Grok 4 Heavy when provider fit matters. If your workload also depends on provider fit, start with Grok 4 Heavy; if it depends on provider fit, run the same evaluation with Llama 4 Scout 17B Instruct.

Continue comparing

Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.