Gemma 4 12B vs Llama 4 Scout 17B
Gemma 4 12B (2026) and Llama 4 Scout 17B (2025) are frontier reasoning models from Google DeepMind and AI at Meta. Gemma 4 12B ships a 256k-token context window, while Llama 4 Scout 17B ships a 10m-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.
Llama 4 Scout 17B fits 39x more tokens; pick it for long-context work and Gemma 4 12B for tighter calls.
Decision scorecard
Local evidence first| Signal | Gemma 4 12B | Llama 4 Scout 17B |
|---|---|---|
| Best for | reasoning-heavy apps, multimodal apps, and tool-calling agents | multimodal apps and long-context analysis |
| Decision fit | RAG, Agents, and Long context | RAG, Long context, and Vision |
| Context window | 256k | 10m |
| Cheapest output | - | $0.66/1M tokens |
| Provider routes | 2 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Gemma 4 12B has broader tracked provider coverage for fallback and procurement flexibility.
- Gemma 4 12B uniquely exposes Vision, Reasoning, and Function calling in local model data.
- Local decision data tags Gemma 4 12B for RAG, Agents, and Long context.
- Llama 4 Scout 17B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama 4 Scout 17B uniquely exposes Structured outputs in local model data.
- Local decision data tags Llama 4 Scout 17B for RAG, Long context, and Vision.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Gemma 4 12B
Unavailable
No complete token price in local provider data
Llama 4 Scout 17B
$301
Cheapest tracked route/tier: AWS Bedrock
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 12B and Llama 4 Scout 17B; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Vision, Reasoning, and Function calling before moving production traffic.
- Llama 4 Scout 17B adds Structured outputs in local capability data.
- No overlapping tracked provider route is sourced for Llama 4 Scout 17B and Gemma 4 12B; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Structured outputs before moving production traffic.
- Gemma 4 12B adds Vision, Reasoning, and Function calling in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-06-03 | 2025-10-01 |
| Context window | 256k | 10m |
| Parameters | 11.9B | 17 |
| Architecture | encoder free unified multimodal | - |
| License | Apache 2.0 | Open Source |
| Knowledge cutoff | 2025-01 | 2024-08 |
Pricing and availability
| Pricing attribute | Gemma 4 12B | Llama 4 Scout 17B |
|---|---|---|
| Input price | - | $0.17/1M tokens |
| Output price | - | $0.66/1M tokens |
| Providers |
Capabilities
| Capability | Gemma 4 12B | Llama 4 Scout 17B |
|---|---|---|
| Vision | Yes | No |
| Multimodal | Yes | Yes |
| Reasoning | Yes | No |
| Function calling | Yes | No |
| Tool use | Yes | 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 vision: Gemma 4 12B, reasoning mode: Gemma 4 12B, function calling: Gemma 4 12B, tool use: Gemma 4 12B, and structured outputs: Llama 4 Scout 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 12B has no token price sourced yet and Llama 4 Scout 17B has $0.17/1M input tokens. Provider availability is 2 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Gemma 4 12B when reasoning depth and broader provider choice are central to the workload. Choose Llama 4 Scout 17B when long-context analysis and larger context windows 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 12B or Llama 4 Scout 17B?
Llama 4 Scout 17B supports 10m tokens, while Gemma 4 12B supports 256k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Gemma 4 12B or Llama 4 Scout 17B open source?
Gemma 4 12B is listed under Apache 2.0. Llama 4 Scout 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 vision, Gemma 4 12B or Llama 4 Scout 17B?
Gemma 4 12B has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for multimodal input, Gemma 4 12B or Llama 4 Scout 17B?
Both Gemma 4 12B and Llama 4 Scout 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 reasoning mode, Gemma 4 12B or Llama 4 Scout 17B?
Gemma 4 12B has the clearer documented reasoning mode signal in this comparison. If reasoning mode 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 12B and Llama 4 Scout 17B?
Gemma 4 12B is available on Hugging Face Inference Endpoints and Kaggle Models. Llama 4 Scout 17B is available on AWS Bedrock. 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.