Llama 4 Scout 17B-16E Instruct vs Mistral Nemotron
Llama 4 Scout 17B-16E Instruct (2025) and Mistral Nemotron (2025) are general-purpose language models from AI at Meta and MistralAI. Llama 4 Scout 17B-16E Instruct ships a 10m-token context window, while Mistral Nemotron ships a not-yet-sourced context window. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Mistral Nemotron is safer overall; choose Llama 4 Scout 17B-16E Instruct when vision-heavy evaluation matters.
Decision scorecard
Local evidence first| Signal | Llama 4 Scout 17B-16E Instruct | Mistral Nemotron |
|---|---|---|
| Best for | multimodal apps, long-context analysis, and provider-routed production | general production evaluation |
| Decision fit | Coding, RAG, and Agents | General |
| Context window | 10m | — |
| Cheapest output | $0.30/1M tokens | - |
| Provider routes | 12 tracked | 1 tracked |
| Shared benchmarks | 0 shared | 0 shared |
Decision tradeoffs
- 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, Multimodal, and Structured outputs in local model data.
- Local decision data tags Llama 4 Scout 17B-16E Instruct for Coding, RAG, and Agents.
- Use Mistral Nemotron when your own prompt tests beat the comparison signals; the local data does not show a decisive standalone advantage yet.
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
Mistral Nemotron
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 NVIDIA NIM; start route-level A/B tests there.
- Check replacement coverage for Vision, Multimodal, and Structured outputs before moving production traffic.
- Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
- Llama 4 Scout 17B-16E Instruct adds Vision, Multimodal, and Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-04-05 | 2025-12-01 |
| Context window | 10m | — |
| Parameters | 109B (17B active) | 70B |
| Architecture | Mixture of Experts | Decoder Only |
| License | Llama 4 Community | Proprietary |
| Openness | Open weights | Proprietary |
| Commercial use | Commercial use: conditional | - |
| Knowledge cutoff | 2024-08 | - |
Pricing and availability
| Pricing attribute | Llama 4 Scout 17B-16E Instruct | Mistral Nemotron |
|---|---|---|
| Input price | $0.08/1M tokens | - |
| Output price | $0.30/1M tokens | - |
| Providers |
Capabilities
| Capability | Llama 4 Scout 17B-16E Instruct | Mistral Nemotron |
|---|---|---|
| Vision | Yes | No |
| Multimodal | Yes | No |
| 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
No shared benchmark scores are currently available for this pair.
Deep dive
The capability footprint differs most on vision: Llama 4 Scout 17B-16E Instruct, multimodal input: Llama 4 Scout 17B-16E Instruct, and structured outputs: Llama 4 Scout 17B-16E Instruct. Both models share the core language-model surface, 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 Mistral Nemotron 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 vision-heavy evaluation and broader provider choice are central to the workload. Choose Mistral Nemotron when provider fit 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
Is Llama 4 Scout 17B-16E Instruct or Mistral Nemotron open source?
Llama 4 Scout 17B-16E Instruct is listed under Llama 4 Community. Mistral Nemotron is listed under Proprietary. 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 Mistral Nemotron?
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 Mistral Nemotron?
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.
Which is better for structured outputs, Llama 4 Scout 17B-16E Instruct or Mistral Nemotron?
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 Mistral Nemotron?
Llama 4 Scout 17B-16E Instruct is available on Cloudflare Workers AI, OpenRouter, Together AI, Fireworks AI, and DeepInfra. Mistral Nemotron is available on NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 4 Scout 17B-16E Instruct over Mistral Nemotron?
Mistral Nemotron is safer overall; choose Llama 4 Scout 17B-16E Instruct when vision-heavy evaluation matters. If your workload also depends on vision-heavy evaluation, start with Llama 4 Scout 17B-16E Instruct; if it depends on provider fit, run the same evaluation with Mistral Nemotron.
Continue comparing
Last reviewed: 2026-06-15. Data sourced from public model cards and provider documentation.