NVIDIA Nemotron 3 Super 120B vs Claude Opus 4: when the free model is good enough

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: ```bash 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 ``` 200 free trial calls let 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.