Llama 4 Maverick 17B Instruct FP8 vs Nemotron 3 Nano Omni
Llama 4 Maverick 17B Instruct FP8 (2025) and Nemotron 3 Nano Omni (2026) are general-purpose language models from AI at Meta and NVIDIA AI. Llama 4 Maverick 17B Instruct FP8 ships a 1m-token context window, while Nemotron 3 Nano Omni ships a 262k-token context window. On MMLU PRO, Llama 4 Maverick 17B Instruct FP8 leads by 8.7 pts. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Nemotron 3 Nano Omni is safer overall; choose Llama 4 Maverick 17B Instruct FP8 when long-context analysis matters.
Decision scorecard
Local evidence first| Signal | Llama 4 Maverick 17B Instruct FP8 | 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 | 1m | 262k |
| Cheapest output | $0.60/1M tokens | - |
| Provider routes | 10 tracked | 1 tracked |
| Shared benchmarks | MMLU PRO leader | 1 shared |
Decision tradeoffs
- Llama 4 Maverick 17B Instruct FP8 holds a shared-benchmark lead on MMLU PRO, ahead by 8.7 points.
- Llama 4 Maverick 17B Instruct FP8 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama 4 Maverick 17B Instruct FP8 has broader tracked provider coverage for fallback and procurement flexibility.
- Llama 4 Maverick 17B Instruct FP8 uniquely exposes Vision and Structured outputs in local model data.
- Local decision data tags Llama 4 Maverick 17B Instruct FP8 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 Maverick 17B Instruct FP8
$270
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 Maverick 17B Instruct FP8 adds Vision and Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-04-05 | 2026-04-28 |
| Context window | 1m | 262k |
| Parameters | 400B (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 Maverick 17B Instruct FP8 | Nemotron 3 Nano Omni |
|---|---|---|
| Input price | $0.15/1M tokens | - |
| Output price | $0.60/1M tokens | - |
| Providers |
Capabilities
| Capability | Llama 4 Maverick 17B Instruct FP8 | 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 Maverick 17B Instruct FP8 | Nemotron 3 Nano Omni |
|---|---|---|
| MMLU PRO | 80.5 | 71.8 |
Deep dive
On shared benchmark coverage, MMLU PRO has Llama 4 Maverick 17B Instruct FP8 at 80.5 and Nemotron 3 Nano Omni at 71.8, with Llama 4 Maverick 17B Instruct FP8 ahead by 8.7 points. The largest visible gap is 8.7 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 Maverick 17B Instruct FP8 and structured outputs: Llama 4 Maverick 17B Instruct FP8. 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 Maverick 17B Instruct FP8 has $0.15/1M input tokens and Nemotron 3 Nano Omni has no token price sourced yet. Provider availability is 10 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 Maverick 17B Instruct FP8 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 Maverick 17B Instruct FP8 or Nemotron 3 Nano Omni?
Llama 4 Maverick 17B Instruct FP8 supports 1m 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 Maverick 17B Instruct FP8 or Nemotron 3 Nano Omni open source?
Llama 4 Maverick 17B Instruct FP8 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 Maverick 17B Instruct FP8 or Nemotron 3 Nano Omni?
Llama 4 Maverick 17B Instruct FP8 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 Maverick 17B Instruct FP8 or Nemotron 3 Nano Omni?
Both Llama 4 Maverick 17B Instruct FP8 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 Maverick 17B Instruct FP8 or Nemotron 3 Nano Omni?
Llama 4 Maverick 17B Instruct FP8 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 Maverick 17B Instruct FP8 and Nemotron 3 Nano Omni?
Llama 4 Maverick 17B Instruct FP8 is available on Microsoft Foundry, Together AI, OpenRouter, Fireworks AI, and DeepInfra. Nemotron 3 Nano Omni is available on OpenRouter. 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.