Gemma 4 E2B vs Llama 4 Scout 17B-16E Instruct
Gemma 4 E2B (2026) and Llama 4 Scout 17B-16E Instruct (2025) are compact production models from Google DeepMind and AI at Meta. Gemma 4 E2B ships a 128k-token context window, while Llama 4 Scout 17B-16E Instruct ships a 328k-token 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.
Gemma 4 E2B is safer overall; choose Llama 4 Scout 17B-16E Instruct when long-context analysis matters.
Decision scorecard
Local evidence first| Signal | Gemma 4 E2B | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| Best for | multimodal apps and tool-calling agents | provider-routed production |
| Decision fit | RAG, Agents, and Long context | RAG, Agents, and Long context |
| Context window | 128k | 328k |
| Cheapest output | - | $0.30/1M tokens |
| Provider routes | 1 tracked | 10 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Gemma 4 E2B uniquely exposes Multimodal and Function calling in local model data.
- Local decision data tags Gemma 4 E2B for RAG, Agents, and Long context.
- Llama 4 Scout 17B-16E Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama 4 Scout 17B-16E Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Llama 4 Scout 17B-16E Instruct uniquely exposes Structured outputs in local model data.
- Local decision data tags Llama 4 Scout 17B-16E Instruct for RAG, Agents, and Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Gemma 4 E2B
Unavailable
No complete token price in local provider data
Llama 4 Scout 17B-16E Instruct
$139
Cheapest tracked route/tier: OpenRouter
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- Provider overlap exists on GCP Vertex AI; start route-level A/B tests there.
- Check replacement coverage for Multimodal and Function calling before moving production traffic.
- Llama 4 Scout 17B-16E Instruct adds Structured outputs in local capability data.
- Provider overlap exists on GCP Vertex AI; start route-level A/B tests there.
- Check replacement coverage for Structured outputs before moving production traffic.
- Gemma 4 E2B adds Multimodal and Function calling in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-03-31 | 2025-04-05 |
| Context window | 128k | 328k |
| Parameters | 2B | 17B |
| Architecture | - | mixture of experts |
| License | Open Source | Open Source |
| Knowledge cutoff | 2025-01 | 2024-08 |
Pricing and availability
| Pricing attribute | Gemma 4 E2B | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| Input price | - | $0.08/1M tokens |
| Output price | - | $0.30/1M tokens |
| Providers |
Capabilities
| Capability | Gemma 4 E2B | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| Vision | No | No |
| Multimodal | Yes | No |
| Reasoning | No | No |
| Function calling | Yes | No |
| Tool use | No | No |
| Structured outputs | No | Yes |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on multimodal input: Gemma 4 E2B, function calling: Gemma 4 E2B, and structured outputs: Llama 4 Scout 17B-16E Instruct. Both models share the core language-model surface, 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: Gemma 4 E2B has no token price sourced yet and Llama 4 Scout 17B-16E Instruct has $0.08/1M input tokens. Provider availability is 1 tracked routes versus 10. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Gemma 4 E2B when provider fit are central to the workload. Choose Llama 4 Scout 17B-16E Instruct when long-context analysis, larger context windows, 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.
FAQ
Which has a larger context window, Gemma 4 E2B or Llama 4 Scout 17B-16E Instruct?
Llama 4 Scout 17B-16E Instruct supports 328k tokens, while Gemma 4 E2B supports 128k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Gemma 4 E2B or Llama 4 Scout 17B-16E Instruct open source?
Gemma 4 E2B is listed under Open Source. Llama 4 Scout 17B-16E Instruct is listed under Open Source. 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, Gemma 4 E2B or Llama 4 Scout 17B-16E Instruct?
Gemma 4 E2B has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for function calling, Gemma 4 E2B or Llama 4 Scout 17B-16E Instruct?
Gemma 4 E2B has the clearer documented function calling signal in this comparison. If function calling is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for structured outputs, Gemma 4 E2B or Llama 4 Scout 17B-16E Instruct?
Llama 4 Scout 17B-16E 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 Gemma 4 E2B and Llama 4 Scout 17B-16E Instruct?
Gemma 4 E2B is available on GCP Vertex AI. Llama 4 Scout 17B-16E Instruct is available on OpenRouter, Together AI, Fireworks AI, DeepInfra, and GCP Vertex AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-06-03. Data sourced from public model cards and provider documentation.