# Nemotron 3.5 Bolt 74B-A9B NVIDIA · Open Weight · rank 125 · bench-align-v5 > Every figure below is reproduced as its upstream source published it: nothing is modelled, estimated, interpolated or converted. `Unavailable` means no source published the value — it is never a zero. Each value carries its evidence state and the date it was observed. Page: https://modelscale.dev/models/nemotron-3-5-bolt-74b-a9b JSON: https://modelscale.dev/api/model/nemotron-3-5-bolt-74b-a9b ## Facts | Field | Value | Evidence | | --- | --- | --- | | Canonical id | `nemotron-3-5-bolt-74b-a9b` | — | | Overall score | 45.21 | Observed 2026-10-23 · source benchlm:models | | Context window | 256K tokens | Observed 2026-10-23 · source benchlm:models | | Release date | 2026-10-15 | Observed 2026-10-23 · source benchlm:models | | Access type | Open Weight | — | | Blended $/1M (75% input / 25% output) | Unavailable | Derived from the input and output rates below | ## Capability evidence Seven axes from the ranking source. An axis the source did not score is unavailable, not zero. | Axis | Score | Evidence | | --- | --- | --- | | Agentic | 48.7 | Observed 2026-10-23 · source benchlm:models | | Coding | Unavailable | Unavailable · source benchlm:models | | Knowledge | 43.1 | Observed 2026-10-23 · source benchlm:models | | Reasoning | 72.9 | Observed 2026-10-23 · source benchlm:models | | Multimodal & Grounded | Unavailable | Unavailable · source benchlm:models | | Instruction Following | Unavailable | Unavailable · source benchlm:models | | Math | Unavailable | Unavailable · source benchlm:models | ## Runtime service evidence Measured values with the date they were observed. Nothing is inferred from a sibling model or a provider claim. Regional or per-endpoint measurements appear only when the API supplies them; none are modelled. | Measurement | Value | Observed | Last good | Evidence | | --- | --- | --- | --- | --- | | Time to first token | Unavailable | Unavailable | Unavailable | Unavailable · source benchlm:speed | | Throughput | Unavailable | Unavailable | Unavailable | Unavailable · source benchlm:speed | ## Endpoint and price matrix Every published price component, including cache reads and writes. | Component | USD | Evidence | | --- | --- | --- | | Input / 1M tokens | Unavailable | Unavailable · source benchlm:pricing | | Output / 1M tokens | Unavailable | Unavailable · source benchlm:pricing | | Cache read / 1M tokens | Unavailable | Unavailable · source benchlm:pricing | | Cache write / 1M tokens | Unavailable | Unavailable · source openrouter:pricing | | Blended / 1M (75% input / 25% output) | Unavailable | Derived — from the input and output rates above; it has no source record of its own | | Cost per successful task (LiveBench) | Unavailable | Fetch failed · source livebench:table | **Self-hosted listing.** NVIDIA publishes downloadable OpenMDW 1.1 weights for local inference. No first-party per-token hosted API tariff is established for this checkpoint; hardware costs are separate. No hosted token rate was published for this model, so its per-token price is unavailable rather than zero. ## Workload-aware monthly cost example 10 conversations per day × 8 messages × 22 active days, 1200 input and 400 output tokens per message, no cache. Derived here from the published rates above by this site's own calculator — not a figure any source published. | Field | Value | | --- | --- | | Modelled monthly cost | Unavailable | | Modelled tokens | Unavailable | | Reason | The applicable input rate is unavailable. | ## Benchmark record 17 matched benchmark rows with their published value, unit, and provenance. ### Knowledge | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | GPQA-D (GPQA Diamond) | 86.4 | Graduate-level science questions | Graduate level | [GPQA: A Graduate-Level Google-Proof Q&A Benchmark](https://arxiv.org/abs/2311.12022) | | Artificial Analysis Intelligence Index | 21.4 | Cross-benchmark intelligence index | Display-only external reference | [Artificial Analysis](https://artificialanalysis.ai/) | | AA-HLE (Artificial Analysis Humanity's Last Exam) | 24.7 | Expert-level questions | Frontier expert reasoning | [Artificial Analysis Humanity's Last Exam Benchmark Leaderboard](https://artificialanalysis.ai/evaluations/hle) | | AA-Omniscience Index (Artificial Analysis Omniscience Index) | -19.2 | Knowledge questions | Broad factual knowledge | [AA-Omniscience: Knowledge and Hallucination Benchmark](https://artificialanalysis.ai/evaluations/omniscience) | | AA-Omniscience Accuracy (Artificial Analysis Omniscience Accuracy) | 17.1 | Knowledge questions | Broad knowledge | [Artificial Analysis model benchmarks](https://artificialanalysis.ai/models/grok-4-3) | | AA-Omniscience Hallucination Rate (Artificial Analysis Omniscience Hallucination Rate) | 43.7 | Knowledge questions | Factuality | [Artificial Analysis model benchmarks](https://artificialanalysis.ai/models/grok-4-3) | | HLE w/o tools (Humanity's Last Exam without tools) | 25.3 | Expert-level questions | Frontier expert level | [Introducing GPT-5.4 mini and nano](https://openai.com/index/introducing-gpt-5-4-mini-and-nano/) | ### Coding | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | AA-SciCode (Artificial Analysis SciCode) | 39.7 | Scientific coding subproblems | Scientific programming | [Artificial Analysis SciCode Benchmark Leaderboard](https://artificialanalysis.ai/evaluations/scicode) | ### Reasoning | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | AA-LCR (Artificial Analysis Long Context Reasoning) | 69.7 | Long-context reasoning tasks | Long-context reasoning | [Artificial Analysis model benchmarks](https://artificialanalysis.ai/models/grok-4-3) | | CritPt (Critical Physics Tasks) | 4.3 | Research-level physics questions | Research-level physics reasoning | [CritPt Benchmark Leaderboard](https://artificialanalysis.ai/evaluations/critpt) | ### Agentic | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | Terminal-Bench 2.1 | 51.0 | Terminal-based software-agent tasks | Professional software engineering | [Terminal-Bench](https://www.tbench.ai/) | | BrowseComp | 66.75 | Research questions requiring browsing | Hard web research | [BrowseComp](https://openai.com/index/browsecomp/) | | GDPval-AA (GDPval-AA normalized) | 24.0 | Economically valuable tasks | Professional agentic workflows | [Artificial Analysis model benchmarks](https://artificialanalysis.ai/models/grok-4-3) | | MCP Atlas | 52.2 | Tool-integrated agent tasks | Advanced tool use | [Introducing GPT-5.4 mini and nano](https://openai.com/index/introducing-gpt-5-4-mini-and-nano/) | | PinchBench | 87.8 | 23 OpenClaw agent tasks | Long-horizon agent workflows | [About PinchBench](https://pinchbench.com/about) | | τ³-bench results (τ³-Bench Tool-Agent-User Evaluation) | 17.0 | Corrected customer-service tasks plus knowledge and voice evaluation modes | Long-horizon, multimodal, and knowledge-aware tool use | [Official τ³-bench repository and release notes](https://github.com/sierra-research/tau2-bench) | | WebArena (WebArena Web Agent Benchmark) | 50.86 | 812 long-horizon browser tasks | Stateful multi-site browser work | [WebArena: A Realistic Web Environment for Building Autonomous Agents](https://arxiv.org/abs/2307.13854) | ## Lifecycle and limitations log Lifecycle events the source associates with this model. No lifecycle event references this model. That is not evidence the model has no lifecycle plan — only that this source published none. ## What this profile does not claim Values are reproduced exactly as their sources published them, in the units those sources declared; none are converted, interpolated, or averaged across providers. Any field marked unavailable was attempted and not returned. Last attempted fetch for this model's score: 2026-10-23 13:17 UTC.