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Gemma 2 9B SahabatAI Instruct vs Nemotron 3 Super-120B-A12B

Gemma 2 9B SahabatAI Instruct (2025) and Nemotron 3 Super-120B-A12B (2026) are compact production models from Google DeepMind and NVIDIA AI. Gemma 2 9B SahabatAI Instruct ships a 8K-token context window, while Nemotron 3 Super-120B-A12B ships a 1M-token context window. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.

Nemotron 3 Super-120B-A12B fits 131x more tokens; pick it for long-context work and Gemma 2 9B SahabatAI Instruct for tighter calls.

Decision scorecard

Local evidence first
SignalGemma 2 9B SahabatAI InstructNemotron 3 Super-120B-A12B
Decision fitGeneralRAG, Long context, and Classification
Context window8K1M
Cheapest output-$0.45/1M tokens
Provider routes1 tracked4 tracked
Shared benchmarks0 rows0 rows

Decision tradeoffs

Choose Gemma 2 9B SahabatAI Instruct when...
  • Use Gemma 2 9B SahabatAI Instruct when your own prompt tests beat the comparison signals; the local data does not show a decisive standalone advantage yet.
Choose Nemotron 3 Super-120B-A12B when...
  • Nemotron 3 Super-120B-A12B has the larger context window for long prompts, retrieval packs, or transcript analysis.
  • Nemotron 3 Super-120B-A12B has broader tracked provider coverage for fallback and procurement flexibility.
  • Nemotron 3 Super-120B-A12B uniquely exposes Structured outputs in local model data.
  • Local decision data tags Nemotron 3 Super-120B-A12B for RAG, Long context, and Classification.

Monthly cost at traffic

Estimate token spend from the cheapest tracked input and output prices on this page.

Gemma 2 9B SahabatAI Instruct

Unavailable

No complete token price in local provider data

Nemotron 3 Super-120B-A12B

$185

Cheapest tracked route: OpenRouter

Cost delta unavailable until both models have sourced input and output token prices.

Switch friction

Gemma 2 9B SahabatAI Instruct -> Nemotron 3 Super-120B-A12B
  • Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
  • Nemotron 3 Super-120B-A12B adds Structured outputs in local capability data.
Nemotron 3 Super-120B-A12B -> Gemma 2 9B SahabatAI Instruct
  • Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
  • Check replacement coverage for Structured outputs before moving production traffic.

Specs

Specification
Released2025-01-012026-03-11
Context window8K1M
Parameters9B120B
Architecturedecoder onlydecoder only
License1Unknown
Knowledge cutoff--

Pricing and availability

Pricing attributeGemma 2 9B SahabatAI InstructNemotron 3 Super-120B-A12B
Input price-$0.09/1M tokens
Output price-$0.45/1M tokens
Providers

Capabilities

CapabilityGemma 2 9B SahabatAI InstructNemotron 3 Super-120B-A12B
VisionNoNo
MultimodalNoNo
ReasoningNoNo
Function callingNoNo
Tool useNoNo
Structured outputsNoYes
Code executionNoNo

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint differs most on structured outputs: Nemotron 3 Super-120B-A12B. 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: Gemma 2 9B SahabatAI Instruct has no token price sourced yet and Nemotron 3 Super-120B-A12B has $0.09/1M input tokens. Provider availability is 1 tracked routes versus 4. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.

Choose Gemma 2 9B SahabatAI Instruct when provider fit are central to the workload. Choose Nemotron 3 Super-120B-A12B when long-context analysis, larger context windows, 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. 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. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.

FAQ

Which has a larger context window, Gemma 2 9B SahabatAI Instruct or Nemotron 3 Super-120B-A12B?

Nemotron 3 Super-120B-A12B supports 1M tokens, while Gemma 2 9B SahabatAI Instruct supports 8K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.

Is Gemma 2 9B SahabatAI Instruct or Nemotron 3 Super-120B-A12B open source?

Gemma 2 9B SahabatAI Instruct is listed under 1. Nemotron 3 Super-120B-A12B is listed under Unknown. 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 structured outputs, Gemma 2 9B SahabatAI Instruct or Nemotron 3 Super-120B-A12B?

Nemotron 3 Super-120B-A12B 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 Gemma 2 9B SahabatAI Instruct and Nemotron 3 Super-120B-A12B?

Gemma 2 9B SahabatAI Instruct is available on NVIDIA NIM. Nemotron 3 Super-120B-A12B is available on DeepInfra, NVIDIA NIM, OpenRouter, and Fireworks AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

When should I pick Gemma 2 9B SahabatAI Instruct over Nemotron 3 Super-120B-A12B?

Nemotron 3 Super-120B-A12B fits 131x more tokens; pick it for long-context work and Gemma 2 9B SahabatAI Instruct for tighter calls. If your workload also depends on provider fit, start with Gemma 2 9B SahabatAI Instruct; if it depends on long-context analysis, run the same evaluation with Nemotron 3 Super-120B-A12B.

Continue comparing

Last reviewed: 2026-05-16. Data sourced from public model cards and provider documentation.