BRIGHT EVIDENCE PACK / Deployed
An open reasoning model with its recipe beside it.
NVIDIA released Nemotron 3 Super, an open 120-billion-parameter mixture-of-experts reasoning model with 12 billion active parameters, plus stated releases of its methodology, data, reinforcement-learning environments, and evaluation recipes.
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Dates and assessment
- Source published
- 2026-03-11
- Bright published
- 2026-09-07
- Substantive update
- None recorded
- Evidence state
- Deployed
- Independent verification
- Not established by this source review
- Last source review
- 2026-09-07
The claim in context
The human problem
Long-running, multi-step AI work can become slow and expensive when each agent repeatedly carries a large task history and uses a large model for every subtask.
The prior constraint
Open model releases often provide weights without enough data and training information for researchers to inspect or reproduce how a system was made.
AI’s actual role
A hybrid Mamba-transformer mixture-of-experts model for complex agent subtasks, with a one-million-token context window and multiple active expert specialists at inference.
The documented result
NVIDIA says the model was available on March 11, 2026 with open weights under a permissive license. It says it published methodology, more than 10 trillion pre- and post-training tokens, 15 reinforcement-learning training environments, and evaluation recipes. NVIDIA reports up to 5 times higher throughput and up to 2 times higher accuracy than the prior Nemotron Super model.
Why it may matter
The combination of weights, large stated data release, training environments, and recipes could let more researchers inspect, reproduce, and adapt a modern reasoning-model pipeline rather than only consume a hosted tool.
Limitations
- The throughput, accuracy, and agent-quality results are NVIDIA's claims, not independent evidence of benefit in a workplace or public service.
- The underlying training data includes synthetic data from frontier reasoning models; openness does not itself settle provenance, bias, or safety questions.
- A one-million-token context window can retain more material, but it does not prevent mistakes, goal drift, or unsafe tool use.
- Hardware requirements and operational cost can still limit who can use or customize the model.
Original evidence
Attribution
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