Claude Opus 4 costs $15 per million input tokens and $75 per million output tokens. NVIDIA Nemotron 3 Super 120B-a12b costs $0 input and $0 output through the NVIDIA NIM open-model program. They are not the same model — Opus is better at several things — but the gap is smaller than the 90× price multiple suggests. Here's where each wins.
Where Nemotron 3 Super matches or beats Opus
General reasoning chains. On most public reasoning benchmarks (MMLU, ARC-Challenge, HellaSwag) the two are within ±2 points. The 120B parameter count puts Nemotron in the same scale class as Opus.
Code generation. HumanEval / MBPP scores are comparable for typical Python / TypeScript tasks. Nemotron's training mix includes more open-source code corpora; Opus's mix includes more Anthropic curation. Both are solid for daily code work.
Long single-turn output. Nemotron supports 128K context. For "given this 50K-token spec, produce a 5K-token implementation" tasks, the quality difference is minimal and you're paying ~$0.40 vs $0 for the same output.
Where Opus 4 still wins
Multi-turn agentic flows with tool use. Anthropic's tool-use protocol + Opus's planning behavior on long horizons is genuinely ahead of the open-source frontier. If you're building an agent that takes 10+ steps with branching tool calls, Opus is meaningfully better; Nemotron starts to drift around step 5-7.
Reasoning under constraint / refusal navigation. Opus's RLHF training makes it better at "I need you to do X but only under condition Y" prompts where the model needs to track multiple constraints. Nemotron handles single constraints fine but struggles with stacked ones.
Verbatim recall from long context. Opus's needle-in-a-haystack scores at 200K context are still industry-leading. Nemotron at 128K is fine for most use cases but loses fidelity at the tail.
Voice / tone calibration. Opus is markedly better at adopting and maintaining a specific writing voice across a long output. If you're producing marketing copy or sustained narrative, Opus's outputs need less editing.
The cost math
For a typical Claude Code session — 10K input tokens, 4K output tokens per round, 20 rounds in a focused work session:
| Per session | Per month (50 sessions) | |
|---|---|---|
| Opus 4 (Anthropic published rate) | ~$8.50 | ~$425 |
| Nemotron 3 Super (InferAll free NIM) | $0 | $0 |
If your work is "Opus is meaningfully better here" the $425/mo is well-spent. If your work is "GPT-4 / Sonnet / open-source are all roughly equivalent," Nemotron is the $5,100/yr savings.
How to actually try this
Through InferAll, both are available behind the same API key. Default routing sends Claude Code to Nemotron at $0; force Opus with a model prefix when you need it:
export ANTHROPIC_BASE_URL=https://api.inferall.ai
export ANTHROPIC_API_KEY=your_inferall_key
# Default → Nemotron, $0
claude
# Force Opus 4 when needed
claude --model anthropic/claude-opus-4-7
A free trial lets you A/B test on your own workload before committing. The $5 starter pack unlocks ongoing free Nemotron use + becomes spendable balance for the Opus calls you actually need.
The right answer for most teams is: default to Nemotron, pay for Opus on the 10% of tasks where the quality delta matters. That's how the math works out.