Nemotron 3 Super-120B-A12B vs Qwen3.5-397B-A17B
Nemotron 3 Super-120B-A12B (2026) and Qwen3.5-397B-A17B (2026) are frontier-tier reasoning models from NVIDIA AI and Alibaba. Nemotron 3 Super-120B-A12B ships a 1M-token context window, while Qwen3.5-397B-A17B ships a 256K-token context window. On MMLU PRO, Qwen3.5-397B-A17B leads by 4.2 pts. On pricing, Nemotron 3 Super-120B-A12B costs $0.09/1M input tokens versus $0.45/1M for the alternative. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Nemotron 3 Super-120B-A12B is ~429% cheaper at $0.09/1M; pay for Qwen3.5-397B-A17B only for vision-heavy evaluation.
Decision scorecard
Local evidence first| Signal | Nemotron 3 Super-120B-A12B | Qwen3.5-397B-A17B |
|---|---|---|
| Best for | reasoning-heavy apps, tool-calling agents, and long-context analysis | reasoning-heavy apps, multimodal apps, and tool-calling agents |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 1M | 256K |
| Cheapest output | $0.40/1M tokens | $3/1M tokens |
| Provider routes | 6 tracked | 5 tracked |
| Shared benchmarks | 5 shared | MMLU PRO leader |
Decision tradeoffs
- Nemotron 3 Super-120B-A12B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Nemotron 3 Super-120B-A12B has the lower cheapest tracked output price at $0.40/1M tokens.
- Nemotron 3 Super-120B-A12B has broader tracked provider coverage for fallback and route flexibility.
- Local decision data tags Nemotron 3 Super-120B-A12B for Coding, RAG, and Agents.
- Qwen3.5-397B-A17B holds a shared-benchmark lead on MMLU PRO, ahead by 4.2 points.
- Qwen3.5-397B-A17B uniquely exposes Vision and Multimodal in local model data.
- Local decision data tags Qwen3.5-397B-A17B 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.
Nemotron 3 Super-120B-A12B
$168
Cheapest tracked route/tier: DeepInfra
Qwen3.5-397B-A17B
$1,110
Cheapest tracked route/tier: DeepInfra
Estimated monthly gap: $942. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter and DeepInfra; start route-level A/B tests there.
- Qwen3.5-397B-A17B is $2.60/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Qwen3.5-397B-A17B adds Vision and Multimodal in local capability data.
- Provider overlap exists on DeepInfra and OpenRouter; start route-level A/B tests there.
- Nemotron 3 Super-120B-A12B is $2.60/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Vision and Multimodal before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-03-11 | 2026-02-16 |
| Context window | 1M | 256K |
| Parameters | 120B | 397B (17B active) |
| Architecture | Decoder Only | Mixture of Experts |
| License | NVIDIA Nemotron Open Model License | Apache 2.0OSI-approved |
| Openness | Open weights | Open source |
| Weights | Available·NVIDIA Open Model | Available |
| Code | Unknown | Unknown |
| Commercial use | Permitted | Permitted |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Nemotron 3 Super-120B-A12B | Qwen3.5-397B-A17B |
|---|---|---|
| Input price | $0.09/1M tokens | $0.45/1M tokens |
| Output price | $0.40/1M tokens | $3/1M tokens |
| Providers |
Capabilities
| Capability | Nemotron 3 Super-120B-A12B | Qwen3.5-397B-A17B |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | Yes | Yes |
| JSON / Tool use | Yes | Yes |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | Nemotron 3 Super-120B-A12B | Qwen3.5-397B-A17B |
|---|---|---|
| MMLU PRO | 83.6 | 87.8 |
| SWE-bench Verified | 60.5 | 76.2 |
| LiveCodeBench | 78.4 | 83.6 |
| LMArena Text Arena - Creative Writing | 1317.0 | 1402.0 |
| τ-bench | 61.1 | 87.9 |
Deep dive
On shared benchmark coverage, MMLU PRO has Nemotron 3 Super-120B-A12B at 83.6 and Qwen3.5-397B-A17B at 87.8, with Qwen3.5-397B-A17B ahead by 4.2 points; SWE-bench Verified has Nemotron 3 Super-120B-A12B at 60.5 and Qwen3.5-397B-A17B at 76.2, with Qwen3.5-397B-A17B ahead by 15.7 points; LiveCodeBench has Nemotron 3 Super-120B-A12B at 78.4 and Qwen3.5-397B-A17B at 83.6, with Qwen3.5-397B-A17B ahead by 5.2 points. The largest visible gap is 15.7 points on SWE-bench Verified, 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: Qwen3.5-397B-A17B, multimodal input: Qwen3.5-397B-A17B, and JSON / Tool use: Qwen3.5-397B-A17B. Both models share reasoning mode, JSON / Tool use, and 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, Nemotron 3 Super-120B-A12B lists $0.09/1M input and $0.40/1M output tokens on the cheapest listed provider, while Qwen3.5-397B-A17B lists $0.45/1M input and $3/1M output tokens on the cheapest listed provider. A 70/30 input-output blend puts Nemotron 3 Super-120B-A12B lower by about $1.04 per million blended tokens. Availability is 6 providers versus 5, so concentration risk also matters.
Choose Nemotron 3 Super-120B-A12B when long-context analysis, larger context windows, and lower input-token cost are central to the workload. Choose Qwen3.5-397B-A17B when vision-heavy evaluation 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 Super-120B-A12B or Qwen3.5-397B-A17B?
Nemotron 3 Super-120B-A12B supports 1M tokens, while Qwen3.5-397B-A17B supports 256K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, Nemotron 3 Super-120B-A12B or Qwen3.5-397B-A17B?
Nemotron 3 Super-120B-A12B is cheaper on listed token pricing. Nemotron 3 Super-120B-A12B costs $0.09/1M input and $0.40/1M output tokens. Qwen3.5-397B-A17B costs $0.45/1M input and $3/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Nemotron 3 Super-120B-A12B or Qwen3.5-397B-A17B open source?
Nemotron 3 Super-120B-A12B is listed under NVIDIA Nemotron Open Model License. Qwen3.5-397B-A17B 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 Super-120B-A12B or Qwen3.5-397B-A17B?
Qwen3.5-397B-A17B 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 Super-120B-A12B or Qwen3.5-397B-A17B?
Qwen3.5-397B-A17B 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 Nemotron 3 Super-120B-A12B and Qwen3.5-397B-A17B?
Nemotron 3 Super-120B-A12B is available on Cloudflare Workers AI, DeepInfra, NVIDIA NIM, OpenRouter, and Fireworks AI. Qwen3.5-397B-A17B is available on OpenRouter, Together AI, Alibaba Cloud PAI-EAS, Novita AI, and DeepInfra. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-10-27. Data sourced from public model cards and provider documentation.