# NVIDIA-Nemotron-3-Nano-4B-BF16 vs Qwen3-4B-Instruct-2507 Compared Source: https://savrn.com/models/compare/nvidia-nemotron-3-nano-4b-bf16-vs-qwen3-4b-instruct-2507 Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- | Field | [NVIDIA-Nemotron-3-Nano-4B-BF16](https://savrn.com/models/nvidia-nemotron-3-nano-4b-bf16) nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 | [Qwen3-4B-Instruct-2507](https://savrn.com/models/qwen3-4b-instruct-2507) Qwen/Qwen3-4B-Instruct-2507 | | --- | --- | --- | | Publisher | NVIDIA | Qwen | | Task | Text generation | Text generation | | Modality | Text | Text | | Parameters, as reported | 4B parameters | 4B parameters | | Architecture | NemotronHForCausalLM | Qwen3ForCausalLM | | Library | transformers | transformers | | Context length | 262,144 tokens | 262,144 tokens | | Repository size | 8.0 GB | 8.1 GB | | Artifact formats | safetensors, pytorch | safetensors | | License | other | apache-2.0 | | Access | Open weights, no gate | Open weights, no gate | | Memory at 16-bit (weights and margin) | 9.5 GB | 9.7 GB | | Cheapest GPUs at 16-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 | | Memory at 4-bit (weights and margin) | 2.4 GB | 2.4 GB | | Cheapest GPUs at 4-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 | | Revision viewed | dfaf35de3e30 | cdbee75f17c0 | | Downloads reported by the hub | 2.5M | 2.7M | | Last observed | 2026-10-23 | 2026-10-23 | An evaluation row appears only where at least two of these models report the same benchmark with the same stated configuration, metric, unit and setup. Different evaluators stay named in each cell. Values are shown as reported: no unit conversion, no ranking. ## Other Reported Results These results are listed for each model on its own, because the conditions needed to compare them are not stated or do not match. Two results that leave a condition blank are not assumed to share it. ### Qwen3-4B-Instruct-2507 | Benchmark | Conditions | Result | Reported by | Revision | Date | | --- | --- | --- | --- | --- | --- | | Idavidrein/gpqa | Task diamondMetric diamondComparison conditions not established | 62 | [Model Card](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) Reported by a third party | Evaluated revision not stated | 2026-01-27 | | TIGER-Lab/MMLU-Pro | Task mmlu_proMetric mmlu_proComparison conditions not established | 69.6 | [Model Card](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) Reported by a third party | Evaluated revision not stated | 2026-02-03 | ## SAVRN's Notes on NVIDIA-Nemotron-3-Nano-4B-BF16 Where in the rack does a 4 billion parameter reasoning model belong? At 16-bit it runs in 9.5 GB, at 8-bit in 4.8 GB, at 4-bit in 2.4 GB. The Index's cheapest fit is a single MI300X at $1.85 an hour on-demand, and its 192 GB is more than one copy needs. NVIDIA trained it from scratch for reasoning and non-reasoning work: it writes a reasoning trace before the final answer, and a system prompt switches the trace off, which sets how many output tokens each request burns. The license field reads other, with no summary on our side, so the terms come from NVIDIA, not a standard license; read them before production. Measure your longest inputs against the 262,144 token window, note the September 2024 pretraining cutoff and the lineage: derived from NVIDIA-Nemotron-Nano-9B-v2, trained on seven named NVIDIA sets, Nemotron-CC-v2 to Nemotron-Math-Proofs-v1. No per-token host prices are listed. SAVRN Research, 2026-09-18 ## SAVRN's Notes on Qwen3-4B-Instruct-2507 This July 2025 update of Qwen3 4B drops the thinking mode and answers directly, with better instruction following and reasoning than the original, and a 262,144-token context. About 4 million downloads a month make it one of the most used small models anywhere. It needs about 10 GB at 16-bit and runs on a laptop at 4-bit. Apache 2.0 allows commercial use. For agents, classification, extraction and chat that must run cheaply at volume, this is a strong default. SAVRN Research, 2026-09-18 ## Questions ### Which is larger, NVIDIA-Nemotron-3-Nano-4B-BF16 or Qwen3-4B-Instruct-2507? Qwen3-4B-Instruct-2507 (4B parameters) is larger than NVIDIA-Nemotron-3-Nano-4B-BF16 (4B parameters), by the parameter counts their publishers report. ### Which is cheaper to run, NVIDIA-Nemotron-3-Nano-4B-BF16 or Qwen3-4B-Instruct-2507? At 4-bit, NVIDIA-Nemotron-3-Nano-4B-BF16 fits on 1x MI300X from $1.85 an hour and Qwen3-4B-Instruct-2507 on 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists. ### Can I use Qwen3-4B-Instruct-2507 commercially? Yes. Qwen3-4B-Instruct-2507 is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors. ## Related Comparisons - [NVIDIA-Nemotron-3-Nano-4B-BF16 vs Qwen2.5-3B-Instruct](https://savrn.com/models/compare/nvidia-nemotron-3-nano-4b-bf16-vs-qwen2-5-3b-instruct) - [NVIDIA-Nemotron-3-Nano-4B-BF16 vs Qwen3-4B](https://savrn.com/models/compare/nvidia-nemotron-3-nano-4b-bf16-vs-qwen3-4b) - [qwen3-4b-base-dapo-v4 vs Qwen3-4B-Instruct-2507](https://savrn.com/models/compare/qwen3-4b-base-dapo-v4-vs-qwen3-4b-instruct-2507) - [Qwen3-4B-Base vs Qwen3-4B-Instruct-2507](https://savrn.com/models/compare/qwen3-4b-base-vs-qwen3-4b-instruct-2507)