Llama 4 Scout 17B-16E Instruct vs Nemotron 3 Super-120B-A12B
Llama 4 Scout 17B-16E Instruct (2025) and Nemotron 3 Super-120B-A12B (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 Super-120B-A12B ships a 1.05m-token context window. On MMLU PRO, Nemotron 3 Super-120B-A12B leads by 9.3 pts. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Pick Nemotron 3 Super-120B-A12B for general evaluation; Llama 4 Scout 17B-16E Instruct is better when long-context analysis matters more.
Decision scorecard
Local evidence first| Signal | Llama 4 Scout 17B-16E Instruct | Nemotron 3 Super-120B-A12B |
|---|---|---|
| Best for | multimodal apps, long-context analysis, and provider-routed production | long-context analysis and provider-routed production |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 10m | 1.05m |
| Cheapest output | $0.30/1M tokens | $0.45/1M tokens |
| Provider routes | 12 tracked | 6 tracked |
| Shared benchmarks | 3 shared | MMLU PRO leader |
Decision tradeoffs
- Llama 4 Scout 17B-16E Instruct holds a shared-benchmark lead on τ-bench, ahead by 1.2 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 the lower cheapest tracked output price at $0.30/1M tokens.
- 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 Multimodal in local model data.
- Nemotron 3 Super-120B-A12B holds a shared-benchmark lead on MMLU PRO, ahead by 9.3 points.
- Local decision data tags Nemotron 3 Super-120B-A12B 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.
Llama 4 Scout 17B-16E Instruct
$139
Cheapest tracked route/tier: OpenRouter
Nemotron 3 Super-120B-A12B
$185
Cheapest tracked route/tier: OpenRouter
Estimated monthly gap: $45.50. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Cloudflare Workers AI, DeepInfra, and NVIDIA NIM; start route-level A/B tests there.
- Nemotron 3 Super-120B-A12B is $0.15/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Vision and Multimodal before moving production traffic.
- Provider overlap exists on Cloudflare Workers AI, OpenRouter, and Fireworks AI; start route-level A/B tests there.
- Llama 4 Scout 17B-16E Instruct is $0.15/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Llama 4 Scout 17B-16E Instruct adds Vision and Multimodal in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-04-05 | 2026-03-11 |
| Context window | 10m | 1.05m |
| Parameters | 109B (17B active) | 120B |
| Architecture | Mixture of Experts | Decoder Only |
| 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 Super-120B-A12B |
|---|---|---|
| Input price | $0.08/1M tokens | $0.09/1M tokens |
| Output price | $0.30/1M tokens | $0.45/1M tokens |
| Providers |
Capabilities
| Capability | Llama 4 Scout 17B-16E Instruct | Nemotron 3 Super-120B-A12B |
|---|---|---|
| Vision | Yes | No |
| Multimodal | Yes | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | Yes |
| 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 Super-120B-A12B |
|---|---|---|
| MMLU PRO | 74.3 | 83.6 |
| LiveCodeBench | 32.8 | 78.4 |
| τ-bench | 62.3 | 61.1 |
Deep dive
On shared benchmark coverage, MMLU PRO has Llama 4 Scout 17B-16E Instruct at 74.3 and Nemotron 3 Super-120B-A12B at 83.6, with Nemotron 3 Super-120B-A12B ahead by 9.3 points; LiveCodeBench has Llama 4 Scout 17B-16E Instruct at 32.8 and Nemotron 3 Super-120B-A12B at 78.4, with Nemotron 3 Super-120B-A12B ahead by 45.6 points; τ-bench has Llama 4 Scout 17B-16E Instruct at 62.3 and Nemotron 3 Super-120B-A12B at 61.1, with Llama 4 Scout 17B-16E Instruct ahead by 1.2 points. The largest visible gap is 45.6 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 vision: Llama 4 Scout 17B-16E Instruct and multimodal input: Llama 4 Scout 17B-16E Instruct. Both models share 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.
For cost, Llama 4 Scout 17B-16E Instruct lists $0.08/1M input and $0.30/1M output tokens on the cheapest tracked provider, while Nemotron 3 Super-120B-A12B lists $0.09/1M input and $0.45/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 4 Scout 17B-16E Instruct lower by about $0.05 per million blended tokens. Availability is 12 providers versus 6, so concentration risk also matters.
Choose Llama 4 Scout 17B-16E Instruct when long-context analysis, larger context windows, and lower input-token cost are central to the workload. Choose Nemotron 3 Super-120B-A12B 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 Super-120B-A12B?
Llama 4 Scout 17B-16E Instruct supports 10m tokens, while Nemotron 3 Super-120B-A12B supports 1.05m tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, Llama 4 Scout 17B-16E Instruct or Nemotron 3 Super-120B-A12B?
Llama 4 Scout 17B-16E Instruct is cheaper on tracked token pricing. Llama 4 Scout 17B-16E Instruct costs $0.08/1M input and $0.30/1M output tokens. Nemotron 3 Super-120B-A12B costs $0.09/1M input and $0.45/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 4 Scout 17B-16E Instruct or Nemotron 3 Super-120B-A12B open source?
Llama 4 Scout 17B-16E Instruct is listed under Llama 4 Community. Nemotron 3 Super-120B-A12B 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 Super-120B-A12B?
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 Super-120B-A12B?
Llama 4 Scout 17B-16E Instruct 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.
Where can I run Llama 4 Scout 17B-16E Instruct and Nemotron 3 Super-120B-A12B?
Llama 4 Scout 17B-16E Instruct is available on Cloudflare Workers AI, OpenRouter, Together AI, Fireworks AI, and DeepInfra. Nemotron 3 Super-120B-A12B is available on Cloudflare Workers AI, DeepInfra, NVIDIA NIM, OpenRouter, and Fireworks AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-06-15. Data sourced from public model cards and provider documentation.