Gemma 4 E2B vs Llama 4 Maverick 17B
Gemma 4 E2B (2026) and Llama 4 Maverick 17B (2025) are compact production models from Google DeepMind and AI at Meta. Gemma 4 E2B ships a 128k-token context window, while Llama 4 Maverick 17B ships a 128k-token context window. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Gemma 4 E2B is safer overall; choose Llama 4 Maverick 17B when coding workflow support matters.
Decision scorecard
Local evidence first| Signal | Gemma 4 E2B | Llama 4 Maverick 17B |
|---|---|---|
| Best for | multimodal apps and tool-calling agents | multimodal apps and provider-routed production |
| Decision fit | RAG, Agents, and Long context | Coding, RAG, and Agents |
| Context window | 128k | 128k |
| Cheapest output | - | $0.60/1M tokens |
| Provider routes | 1 tracked | 2 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Gemma 4 E2B uniquely exposes Function calling in local model data.
- Local decision data tags Gemma 4 E2B for RAG, Agents, and Long context.
- Llama 4 Maverick 17B has broader tracked provider coverage for fallback and procurement flexibility.
- Llama 4 Maverick 17B uniquely exposes Structured outputs and Code execution in local model data.
- Local decision data tags Llama 4 Maverick 17B for Coding, RAG, and Agents.
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 Maverick 17B
$270
Cheapest tracked route/tier: OpenRouter
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Gemma 4 E2B and Llama 4 Maverick 17B; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Function calling before moving production traffic.
- Llama 4 Maverick 17B adds Structured outputs and Code execution in local capability data.
- No overlapping tracked provider route is sourced for Llama 4 Maverick 17B and Gemma 4 E2B; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Structured outputs and Code execution before moving production traffic.
- Gemma 4 E2B adds Function calling in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-03-31 | 2025-10-01 |
| Context window | 128k | 128k |
| Parameters | 2B | 17 |
| Architecture | - | - |
| License | Open Source | Open Source |
| Knowledge cutoff | 2025-01 | 2024-08 |
Pricing and availability
| Pricing attribute | Gemma 4 E2B | Llama 4 Maverick 17B |
|---|---|---|
| Input price | - | $0.15/1M tokens |
| Output price | - | $0.60/1M tokens |
| Providers |
Capabilities
| Capability | Gemma 4 E2B | Llama 4 Maverick 17B |
|---|---|---|
| Vision | No | No |
| Multimodal | Yes | Yes |
| Reasoning | No | No |
| Function calling | Yes | No |
| Tool use | No | No |
| Structured outputs | No | Yes |
| Code execution | No | Yes |
| 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 function calling: Gemma 4 E2B, structured outputs: Llama 4 Maverick 17B, and code execution: Llama 4 Maverick 17B. 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: Gemma 4 E2B has no token price sourced yet and Llama 4 Maverick 17B has $0.15/1M input tokens. Provider availability is 1 tracked routes versus 2. 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 Maverick 17B when coding workflow support 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 Maverick 17B?
Gemma 4 E2B supports 128k tokens, while Llama 4 Maverick 17B 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 Maverick 17B open source?
Gemma 4 E2B is listed under Open Source. Llama 4 Maverick 17B 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 Maverick 17B?
Both Gemma 4 E2B and Llama 4 Maverick 17B 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 function calling, Gemma 4 E2B or Llama 4 Maverick 17B?
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 Maverick 17B?
Llama 4 Maverick 17B 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 Maverick 17B?
Gemma 4 E2B is available on GCP Vertex AI. Llama 4 Maverick 17B is available on AWS Bedrock and OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-06-03. Data sourced from public model cards and provider documentation.