Nemotron 3 Nano Omni vs Qwen3.5-9B
Nemotron 3 Nano Omni (2026) and Qwen3.5-9B (2026) are general-purpose language models from NVIDIA AI and Alibaba. Nemotron 3 Nano Omni ships a 262k-token context window, while Qwen3.5-9B ships a 262k-token context window. On MMLU PRO, Qwen3.5-9B leads by 10.7 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 Qwen3.5-9B when vision-heavy evaluation matters.
Decision scorecard
Local evidence first| Signal | Nemotron 3 Nano Omni | Qwen3.5-9B |
|---|---|---|
| Best for | multimodal apps | multimodal apps, tool-calling agents, and provider-routed production |
| Decision fit | Long context, Vision, and Classification | RAG, Agents, and Long context |
| Context window | 262k | 262k |
| Cheapest output | - | $0.15/1M tokens |
| Provider routes | 1 tracked | 3 tracked |
| Shared benchmarks | 1 rows | MMLU PRO leader |
Decision tradeoffs
- Local decision data tags Nemotron 3 Nano Omni for Long context, Vision, and Classification.
- Qwen3.5-9B leads the largest shared benchmark signal on MMLU PRO by 10.7 points.
- Qwen3.5-9B has broader tracked provider coverage for fallback and procurement flexibility.
- Qwen3.5-9B uniquely exposes Vision, Function calling, and Tool use in local model data.
- Local decision data tags Qwen3.5-9B for RAG, Agents, and Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Nemotron 3 Nano Omni
Unavailable
No complete token price in local provider data
Qwen3.5-9B
$118
Cheapest tracked route/tier: Together AI
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.
- Qwen3.5-9B adds Vision, Function calling, and Tool use in local capability data.
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- Check replacement coverage for Vision, Function calling, and Tool use before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-04-28 | 2026-03-02 |
| Context window | 262k | 262k |
| Parameters | 30B | 9B |
| Architecture | Hybrid Mamba-Transformer MoE | decoder only |
| License | NVIDIA Open Model | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Nemotron 3 Nano Omni | Qwen3.5-9B |
|---|---|---|
| Input price | - | $0.10/1M tokens |
| Output price | - | $0.15/1M tokens |
| Providers |
Capabilities
| Capability | Nemotron 3 Nano Omni | Qwen3.5-9B |
|---|---|---|
| Vision | No | Yes |
| Multimodal | Yes | Yes |
| Reasoning | No | No |
| Function calling | No | Yes |
| Tool use | No | Yes |
| Structured outputs | No | Yes |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | Nemotron 3 Nano Omni | Qwen3.5-9B |
|---|---|---|
| MMLU PRO | 71.8 | 82.5 |
Deep dive
On shared benchmark coverage, MMLU PRO has Nemotron 3 Nano Omni at 71.8 and Qwen3.5-9B at 82.5, with Qwen3.5-9B ahead by 10.7 points. The largest visible gap is 10.7 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: Qwen3.5-9B, function calling: Qwen3.5-9B, tool use: Qwen3.5-9B, and structured outputs: Qwen3.5-9B. 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: Nemotron 3 Nano Omni has no token price sourced yet and Qwen3.5-9B has $0.10/1M input tokens. Provider availability is 1 tracked routes versus 3. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Nemotron 3 Nano Omni when provider fit are central to the workload. Choose Qwen3.5-9B when vision-heavy evaluation 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, Nemotron 3 Nano Omni or Qwen3.5-9B?
Nemotron 3 Nano Omni supports 262k tokens, while Qwen3.5-9B supports 262k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Nemotron 3 Nano Omni or Qwen3.5-9B open source?
Nemotron 3 Nano Omni is listed under NVIDIA Open Model. Qwen3.5-9B is listed under Apache 2.0. 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, Nemotron 3 Nano Omni or Qwen3.5-9B?
Qwen3.5-9B 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. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, Nemotron 3 Nano Omni or Qwen3.5-9B?
Both Nemotron 3 Nano Omni and Qwen3.5-9B 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 function calling, Nemotron 3 Nano Omni or Qwen3.5-9B?
Qwen3.5-9B has the clearer documented function calling signal in this comparison. If function calling is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Nemotron 3 Nano Omni and Qwen3.5-9B?
Nemotron 3 Nano Omni is available on OpenRouter. Qwen3.5-9B is available on Together AI, OpenRouter, and Alibaba Cloud PAI-EAS. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.