Gemma 4 12B IT vs Llama 4 Scout 17B-16E Instruct
Gemma 4 12B IT (2026) and Llama 4 Scout 17B-16E Instruct (2025) are frontier reasoning models from Google DeepMind and AI at Meta. Gemma 4 12B IT ships a 256k-token context window, while Llama 4 Scout 17B-16E Instruct ships a 10m-token context window. On MMLU PRO, Gemma 4 12B IT leads by 2.9 pts. 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-16E Instruct fits 39x more tokens; pick it for long-context work and Gemma 4 12B IT for tighter calls.
Decision scorecard
Local evidence first| Signal | Gemma 4 12B IT | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| Best for | reasoning-heavy apps, multimodal apps, and tool-calling agents | multimodal apps, long-context analysis, and provider-routed production |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 256k | 10m |
| Cheapest output | - | $0.30/1M tokens |
| Provider routes | 2 tracked | 12 tracked |
| Shared benchmarks | MMLU PRO leader | 2 shared |
Decision tradeoffs
- Gemma 4 12B IT holds a shared-benchmark lead on MMLU PRO, ahead by 2.9 points.
- Gemma 4 12B IT uniquely exposes Reasoning, Function calling, and Tool use in local model data.
- Local decision data tags Gemma 4 12B IT for Coding, RAG, and Agents.
- 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.
- Local decision data tags Llama 4 Scout 17B-16E Instruct 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 12B IT
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
- No overlapping tracked provider route is sourced for Gemma 4 12B IT and Llama 4 Scout 17B-16E Instruct; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Reasoning, Function calling, and Tool use before moving production traffic.
- No overlapping tracked provider route is sourced for Llama 4 Scout 17B-16E Instruct and Gemma 4 12B IT; plan for SDK, billing, or endpoint changes.
- Gemma 4 12B IT adds Reasoning, Function calling, and Tool use in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-06-03 | 2025-04-05 |
| Context window | 256k | 10m |
| Parameters | 12B | 109B (17B active) |
| Architecture | Decoder Only | Mixture of Experts |
| License | Apache 2.0OSI-approved | Llama 4 Community |
| Openness | Open source | Open weights |
| Weights | Available | Unknown |
| Code | Unknown | Unknown |
| Commercial use | Commercial use: permitted | Commercial use: conditional |
| Knowledge cutoff | 2025-01 | 2024-08 |
Pricing and availability
| Pricing attribute | Gemma 4 12B IT | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| Input price | - | $0.08/1M tokens |
| Output price | - | $0.30/1M tokens |
| Providers |
Capabilities
| Capability | Gemma 4 12B IT | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | Yes | Yes |
| Reasoning | Yes | No |
| Function calling | Yes | No |
| Tool use | Yes | No |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | Gemma 4 12B IT | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| MMLU PRO | 77.2 | 74.3 |
| LiveCodeBench | 72.0 | 32.8 |
Deep dive
On shared benchmark coverage, MMLU PRO has Gemma 4 12B IT at 77.2 and Llama 4 Scout 17B-16E Instruct at 74.3, with Gemma 4 12B IT ahead by 2.9 points; LiveCodeBench has Gemma 4 12B IT at 72 and Llama 4 Scout 17B-16E Instruct at 32.8, with Gemma 4 12B IT ahead by 39.2 points. The largest visible gap is 39.2 points on LiveCodeBench, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint differs most on reasoning mode: Gemma 4 12B IT, function calling: Gemma 4 12B IT, and tool use: Gemma 4 12B IT. Both models share vision, multimodal input, and structured outputs, 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 IT has no token price sourced yet and Llama 4 Scout 17B-16E Instruct has $0.08/1M input tokens. Provider availability is 2 tracked routes versus 12. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Gemma 4 12B IT when reasoning depth 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.
FAQ
Which has a larger context window, Gemma 4 12B IT or Llama 4 Scout 17B-16E Instruct?
Llama 4 Scout 17B-16E Instruct supports 10m tokens, while Gemma 4 12B IT 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 IT or Llama 4 Scout 17B-16E Instruct open source?
Gemma 4 12B IT is listed under Apache 2.0. Llama 4 Scout 17B-16E Instruct is listed under Llama 4 Community. 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 IT or Llama 4 Scout 17B-16E Instruct?
Both Gemma 4 12B IT and Llama 4 Scout 17B-16E Instruct expose vision. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Which is better for multimodal input, Gemma 4 12B IT or Llama 4 Scout 17B-16E Instruct?
Both Gemma 4 12B IT and Llama 4 Scout 17B-16E 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 reasoning mode, Gemma 4 12B IT or Llama 4 Scout 17B-16E Instruct?
Gemma 4 12B IT 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 IT and Llama 4 Scout 17B-16E Instruct?
Gemma 4 12B IT is available on Hugging Face Inference Endpoints and Kaggle Models. Llama 4 Scout 17B-16E Instruct is available on Cloudflare Workers AI, OpenRouter, Together AI, Fireworks AI, and DeepInfra. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-07-09. Data sourced from public model cards and provider documentation.