Llama 4 Scout 17B-16E Instruct vs Nemotron 3 Nano Omni
Llama 4 Scout 17B-16E Instruct (2025) and Nemotron 3 Nano Omni (2026) are general-purpose language models from AI at Meta and NVIDIA AI. Llama 4 Scout 17B-16E Instruct ships a 10m-token context window, while Nemotron 3 Nano Omni ships a 262k-token context window. On MMLU PRO, Llama 4 Scout 17B-16E Instruct leads by 2.5 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 38x more tokens; pick it for long-context work and Nemotron 3 Nano Omni for tighter calls.
Decision scorecard
Local evidence first| Signal | Llama 4 Scout 17B-16E Instruct | Nemotron 3 Nano Omni |
|---|---|---|
| Best for | multimodal apps, long-context analysis, and provider-routed production | multimodal apps |
| Decision fit | Coding, RAG, and Agents | Long context, Vision, and Classification |
| Context window | 10m | 262k |
| Cheapest output | $0.30/1M tokens | - |
| Provider routes | 12 tracked | 1 tracked |
| Shared benchmarks | MMLU PRO leader | 1 shared |
Decision tradeoffs
- Llama 4 Scout 17B-16E Instruct holds a shared-benchmark lead on MMLU PRO, ahead by 2.5 points.
- 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 Vision and Structured outputs in local model data.
- Local decision data tags Llama 4 Scout 17B-16E Instruct for Coding, RAG, and Agents.
- Local decision data tags Nemotron 3 Nano Omni for Long context, Vision, and Classification.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Llama 4 Scout 17B-16E Instruct
$139
Cheapest tracked route/tier: OpenRouter
Nemotron 3 Nano Omni
Unavailable
No complete token price in local provider data
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- Check replacement coverage for Vision and Structured outputs before moving production traffic.
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- Llama 4 Scout 17B-16E Instruct adds Vision and Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-04-05 | 2026-04-28 |
| Context window | 10m | 262k |
| Parameters | 109B (17B active) | 30B |
| Architecture | Mixture of Experts | MoE + SSM Hybrid |
| License | Llama 4 Community | NVIDIA Open Model |
| Openness | Open weights | Open weights |
| Commercial use | Commercial use: conditional | Commercial use: permitted |
| Knowledge cutoff | 2024-08 | - |
Pricing and availability
| Pricing attribute | Llama 4 Scout 17B-16E Instruct | Nemotron 3 Nano Omni |
|---|---|---|
| Input price | $0.08/1M tokens | - |
| Output price | $0.30/1M tokens | - |
| Providers |
Capabilities
| Capability | Llama 4 Scout 17B-16E Instruct | Nemotron 3 Nano Omni |
|---|---|---|
| Vision | Yes | No |
| Multimodal | Yes | Yes |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | No |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | Llama 4 Scout 17B-16E Instruct | Nemotron 3 Nano Omni |
|---|---|---|
| MMLU PRO | 74.3 | 71.8 |
Deep dive
On shared benchmark coverage, MMLU PRO has Llama 4 Scout 17B-16E Instruct at 74.3 and Nemotron 3 Nano Omni at 71.8, with Llama 4 Scout 17B-16E Instruct ahead by 2.5 points. The largest visible gap is 2.5 points on MMLU PRO, 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 vision: Llama 4 Scout 17B-16E Instruct and structured outputs: Llama 4 Scout 17B-16E Instruct. 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: Llama 4 Scout 17B-16E Instruct has $0.08/1M input tokens and Nemotron 3 Nano Omni has no token price sourced yet. Provider availability is 12 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Llama 4 Scout 17B-16E Instruct when long-context analysis, larger context windows, and broader provider choice are central to the workload. Choose Nemotron 3 Nano Omni when provider fit 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, Llama 4 Scout 17B-16E Instruct or Nemotron 3 Nano Omni?
Llama 4 Scout 17B-16E Instruct supports 10m tokens, while Nemotron 3 Nano Omni supports 262k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Llama 4 Scout 17B-16E Instruct or Nemotron 3 Nano Omni open source?
Llama 4 Scout 17B-16E Instruct is listed under Llama 4 Community. Nemotron 3 Nano Omni is listed under NVIDIA Open Model. 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, Llama 4 Scout 17B-16E Instruct or Nemotron 3 Nano Omni?
Llama 4 Scout 17B-16E Instruct 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, Llama 4 Scout 17B-16E Instruct or Nemotron 3 Nano Omni?
Both Llama 4 Scout 17B-16E Instruct and Nemotron 3 Nano Omni 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 structured outputs, Llama 4 Scout 17B-16E Instruct or Nemotron 3 Nano Omni?
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 Llama 4 Scout 17B-16E Instruct and Nemotron 3 Nano Omni?
Llama 4 Scout 17B-16E Instruct is available on Cloudflare Workers AI, OpenRouter, Together AI, Fireworks AI, and DeepInfra. Nemotron 3 Nano Omni is available on OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-06-15. Data sourced from public model cards and provider documentation.