Compare the listed model data for Llama 4 Scout 17B (16 Experts) by Meta Llama and Gemma 3 27B by Google. Benchmark and pricing records are not a controlled test and may change.
Data last updated: 2026-10-16. Verify current model details with each provider.
What the listed data says
Llama 4 Scout 17B (16 Experts): 10M tokens; Gemma 3 27B: 128K tokens. Llama 4 Scout 17B (16 Experts) has the larger listed context window. Llama 4 Scout 17B (16 Experts) is listed for coding, vision, multimodal input, agentic workflows, long-context tasks; Gemma 3 27B is listed for coding, vision, multimodal input, long-context tasks. Shared catalog benchmark entries: MMLU: Llama 4 Scout 17B (16 Experts) 79.9%; Gemma 3 27B 78.4%; HumanEval: Llama 4 Scout 17B (16 Experts) 80.1%; Gemma 3 27B 74.3%; DocVQA: Llama 4 Scout 17B (16 Experts) 88.4%; Gemma 3 27B 85.6%. These records are reference data, not a controlled head-to-head test; verify current pricing and specifications with each provider.
Catalog source links
Use these model and provider links to verify current specifications, pricing, and availability.
The first listed parameter quantities are 17B active / 16 experts and 27B; the larger quantity is 1.59× the smaller. Total and active MoE parameters are not directly equivalent.
The catalog release dates are April 2025 and March 2025, about 1 month apart at the precision supplied.
Cost example: 1M input and 1M output tokens
A cost example for 1M input plus 1M output tokens cannot be calculated reliably because separate numeric input and output rates are not listed for both models.
Choose Llama 4 Scout 17B (16 Experts) if…
Choose Llama 4 Scout 17B (16 Experts) when your task aligns with catalog-listed uses or strengths such as Ultra-long document processing: entire legal code sets, multi-volume research, Video understanding with multi-hour temporal context, Multi-document enterprise knowledge bases, 10 million token context — largest publicly available context window as of 2025 (roughly 30 full novels).
Choose Gemma 3 27B if…
Choose Gemma 3 27B when your task aligns with catalog-listed uses or strengths such as Multimodal enterprise apps at deployable scale, Visual document analysis: receipts, invoices, charts, Multilingual applications, Google's open multimodal model at accessible 27B scale.
Catalog specifications for Llama 4 Scout 17B (16 Experts) and Gemma 3 27B
Specification
Llama 4 Scout 17B (16 Experts)
Gemma 3 27B
Provider
Meta Llama
Google
Parameters
17B active / 16 experts
27B
Context window
10M tokens
128K tokens
Maximum output
Not listed
Not listed
Catalog pricing
Llama 4 Community License — free. Third-party: $0.10-0.30/MTok
Gemma Terms — free
Listed input/output cost per 1M tokens
Separate numeric input/output rates not listed
Separate numeric input/output rates not listed
License
Llama 4 Community License
Gemma Terms of Use
Coding support
Listed
Listed
Reasoning support
Not listed
Not listed
Vision input
Listed
Listed
Multimodal
Listed
Listed
Open weights
Yes
Yes
Benchmark: MMLU
79.9%
78.4%
Benchmark: HumanEval
80.1%
74.3%
Benchmark: DocVQA
88.4%
85.6%
Figures are transcribed from LITHOVEX model records. Benchmark versions and evaluation setups may differ; pricing and specifications can change. Check each provider's current documentation before making a decision.
Frequently Asked Questions
What is different between Llama 4 Scout 17B (16 Experts) and Gemma 3 27B?
Llama 4 Scout 17B (16 Experts): 10M tokens; Gemma 3 27B: 128K tokens. Llama 4 Scout 17B (16 Experts) has the larger listed context window. Llama 4 Scout 17B (16 Experts) is listed for coding, vision, multimodal input, agentic workflows, long-context tasks; Gemma 3 27B is listed for coding, vision, multimodal input, long-context tasks. Shared catalog benchmark entries: MMLU: Llama 4 Scout 17B (16 Experts) 79.9%; Gemma 3 27B 78.4%; HumanEval: Llama 4 Scout 17B (16 Experts) 80.1%; Gemma 3 27B 74.3%; DocVQA: Llama 4 Scout 17B (16 Experts) 88.4%; Gemma 3 27B 85.6%. These records are reference data, not a controlled head-to-head test; verify current pricing and specifications with each provider.
What benchmark values are listed for Llama 4 Scout 17B (16 Experts) and Gemma 3 27B?
MMLU: Llama 4 Scout 17B (16 Experts) 79.9%; Gemma 3 27B 78.4%; HumanEval: Llama 4 Scout 17B (16 Experts) 80.1%; Gemma 3 27B 74.3%; DocVQA: Llama 4 Scout 17B (16 Experts) 88.4%; Gemma 3 27B 85.6%. The catalog values may use different versions and evaluation setups, so they are reference points rather than a controlled head-to-head result.
What are the listed costs per million tokens for Llama 4 Scout 17B (16 Experts) and Gemma 3 27B?
Llama 4 Scout 17B (16 Experts): Separate numeric input/output rates not listed (Llama 4 Community License — free. Third-party: $0.10-0.30/MTok). Gemma 3 27B: Separate numeric input/output rates not listed (Gemma Terms — free). Where separate numeric input and output rates are available, the worked example uses 1M input plus 1M output tokens. Confirm current terms with each provider.
How far apart are the listed release dates for Llama 4 Scout 17B (16 Experts) and Gemma 3 27B?
The catalog lists Llama 4 Scout 17B (16 Experts) as April 2025 and Gemma 3 27B as March 2025, about 1 month apart at the precision supplied.
When was the comparison data last updated?
The model catalog records 2026-10-16 as its data update date. Provider specifications and prices may have changed since then.
How should I choose between Llama 4 Scout 17B (16 Experts) and Gemma 3 27B?
Compare the listed specifications and capabilities, review the benchmark notes, then test both with the same representative prompt for your task. This page does not claim a universal winner.
Create your free LITHOVEX account and compare Llama 4 Scout 17B (16 Experts) and Gemma 3 27B on your actual tasks. No credit card, no API key, no subscriptions needed.