Why Nemotron 3.5 Bolt’s Size Comes With A Token Trade-Off

🔍 Read the full analysis: Why Nemotron 3.5 Bolt’s Size Comes With A Token Trade-Off on ThorstenMeyerAI.com

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NVIDIA released Nemotron 3.5 Bolt 74B-A9B on October 15, with benchmark results that put the open-weight model close to the company’s much larger Nemotron 3 Ultra—but with a notable trade-off: Bolt generated about 82,000 output tokens per Artificial Analysis Intelligence Index task, nearly three times Ultra’s 28,000. That token count may mean longer waits for people running the model locally, although its speed has not been measured in a hosted API.

Bolt is a mixture-of-experts model with 74 billion total parameters and 9 billion active per token. It accepts text and images, produces text, supports reasoning and has a stated 256,000-token context window. NVIDIA released it with open weights for self-hosting. Artificial Analysis evaluated its reasoning version and gave it 21.4 on Intelligence Index v4.3.2, compared with 22.9 for Nemotron 3 Ultra, which has 550 billion total and 55 billion active parameters.

The benchmark gap is small relative to the difference in model size, but it does not mean the two models perform alike across tasks. Artificial Analysis scores Bolt higher on AutomationBench-AA, a benchmark of multi-step workflows across software tools: 15.5% versus Ultra’s 3.0%. Bolt also scores higher on AA-Briefcase and CritPt. Ultra leads on several knowledge and long-document measures, including AA-LCR at 79% to Bolt’s 70%; both score near zero on Terminal-Bench 4.0, according to the supplied results.

Bolt ranks fourth among models under 100 billion total parameters in Artificial Analysis’s comparison, and first among US models in that group. The listed leader, Alibaba’s Qwen3.8 27B, scores 33.7, well above Bolt’s 21.4. Artificial Analysis lists no hosted API provider for Bolt, so a provider price, measured inference speed and cost per task were not available in the source material.

The Cost of Longer Answers

The token count changes how Bolt’s benchmark result may translate to practical use. On a self-hosted system, output tokens take time to generate. The source’s arithmetic estimates that 82,000 tokens would take roughly 14 minutes at 100 tokens per second, or about 27 minutes at 50 tokens per second. These are hypothetical calculations, not measured Bolt performance; actual throughput depends on hardware and deployment.

That distinction matters for users choosing a model for interactive work. A high score per evaluation task does not establish high throughput or a short response time. Bolt’s strong AutomationBench result could interest developers testing agentic workflows, while its long outputs may be less suitable when people need quick answers. For batch jobs that can run unattended, longer generation may be more acceptable.

The low active-parameter count suggests a potential compute advantage over larger models, but the available information does not confirm that advantage in actual runtime. Artificial Analysis has not measured Bolt’s speed, and no hosted API pricing is listed. Readers should treat claims about faster or cheaper inference as unverified until comparable hardware and workload measurements are published.

How Bolt Compares Within NVIDIA

Bolt is part of NVIDIA’s October 15 release of two open-weight models. The other release, Nemotron 3.5 Super VL, drew more attention in the source account, which describes it as the highest-scoring permissively licensed open-weight model from the US. The source identifies Bolt as the first model in a new Bolt family, but does not provide a roadmap for further releases.

The model also marks a sharp change from NVIDIA’s earlier Nemotron 3 Super. That model, released in March with 120 billion total parameters and 12 billion active, scored 12.8 on the same index; Bolt scored 21.4, an 8.6-point difference while using fewer total parameters. These are benchmark results reported by Artificial Analysis, not proof that Bolt is better for every task.

Within the under-100-billion-parameter group, Bolt’s fourth-place position puts its near-Ultra score in perspective. The source comparison lists Qwen3.8 27B at 33.7 and K2 Horizon MoVA 36B-A4B at 25.3, ahead of Bolt. Bolt’s stated distinguishing features include image input and its standing as the top US model in that class. The source says the license is classed as permissive but does not name the license on the model page.

Speed and Deployment Costs

There is no measured Bolt inference speed in the source material, and Artificial Analysis lists no hosted API provider. That leaves latency, pricing and cost per task unknown. The estimates of 14 or 27 minutes apply only to assumed generation rates; they are not tests of Bolt on specific hardware.

The source also reports that Bolt’s AA-Omniscience score is −19, a measure that rewards correct answers and penalizes confident errors. It does not provide enough detail here to determine how that result varies by question type or deployment. More broadly, benchmark scores cannot establish how the model will perform for a particular user’s prompts, tools or hardware.

The benchmark snapshot is dated October 16, 2026. Rankings may change as evaluation results or model configurations are updated. The model page’s license classification is described as permissive, but the license name is not specified in the supplied material.

Awaiting Runtime Measurements

The next useful evidence would be independent speed tests on specified hardware, alongside results for common workloads and a clear account of the model’s output-token behavior. A hosted API listing, if one appears, could add provider pricing and latency data, but none is listed in the source material.

Users evaluating Bolt can compare its benchmark profile with their own needs: its reported strength in multi-step software workflows, its weaker long-context result relative to Ultra, and the time potentially required for long generations. Until deployment measurements are published, its practical advantage in speed or cost over larger models remains unresolved.

Key Questions

What is Nemotron 3.5 Bolt?

It is NVIDIA’s open-weight, 74-billion-parameter mixture-of-experts model, with 9 billion parameters active per token. It accepts text and images, generates text, supports reasoning and has a stated 256,000-token context window.

How does Bolt compare with Nemotron 3 Ultra?

Artificial Analysis gives Bolt 21.4 and Nemotron 3 Ultra 22.9 on Intelligence Index v4.3.2. Bolt has far fewer total and active parameters, but its scores vary by task: it leads Ultra on AutomationBench-AA, while Ultra leads on several knowledge and long-document measures.

Why is Bolt’s token count a trade-off?

Bolt generated about 82,000 output tokens per index task, compared with 28,000 for Ultra. On local hardware, generating more tokens can take longer. The source’s time estimates assume specific token rates and are not measured Bolt benchmarks.

Is Bolt the top model in its size class?

No. In the source’s Artificial Analysis comparison, Bolt ranks fourth among models under 100 billion total parameters. It ranks first among US models in that group, while Qwen3.8 27B leads the listed class-wide ranking.

Can users compare Bolt’s hosted price and speed?

Not from the supplied information. Artificial Analysis lists no hosted API provider for Bolt, so API pricing and measured hosted speed are unavailable. Local performance will depend on the hardware and setup used.

Source: ThorstenMeyerAI.com