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Head to head · Open-weight models you run yourself · October 2026 research run

Nemotron 3 Nano 4B vs Qwen3-4B-Instruct-2507

Nemotron 3 Nano 4B scores 56.8 (C) on agent readiness against Qwen3-4B-Instruct-2507's 55.7 (C), and leads in 2 of 5 scored categories. Qwen3-4B-Instruct-2507 leads on capability, reach and agent interface. Both do open-weight models you run yourself.

Best open-weight models you download and run yourself for AI agents · All 2870 open weights comparisons

Nemotron 3 Nano 4BNVIDIA · Open-weight model C56.8/100#73 of 100 open-weight models Qwen3-4B-Instruct-2507Alibaba (Qwen team) · Open-weight model C55.7/100#77 of 100 open-weight models

Which one, for what

Nemotron 3 Nano 4B C

Best for Agents on laptops, edge boards and small GPUs that need a cheap local tool-calling helper in English

Ahead on

  • Risk, 65 against 55
  • Documentation and upkeep, 55 against 26

Watch for

The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository

Qwen3-4B-Instruct-2507 C

Best for Agents that want a capable small instruct model on a laptop, phone-class device or spare GPU, with a plain licence and long context.

Ahead on

  • Capability, 31 against 25
  • Reach, 95 against 90
  • Agent interface, 75 against 70

Watch for

No safety results, prompt-injection guidance or security contact

Score by category

CategoryWeight this runNemotron 3 Nano 4BQwen3-4B-Instruct-2507Edge
Capability35%352531Qwen3-4B-Instruct-2507 +6
Reach25%259095Qwen3-4B-Instruct-2507 +5
Risk20%206555Nemotron 3 Nano 4B +10
Agent interface10%107075Qwen3-4B-Instruct-2507 +5
Documentation and upkeep10%105526Nemotron 3 Nano 4B +29
Negative events≤1500
TotalC 56.8/100C 55.7/100

Facts side by side

FactNemotron 3 Nano 4BQwen3-4B-Instruct-2507
KindOpen-weight modelOpen-weight model
VendorNVIDIAAlibaba (Qwen team)
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceNVIDIA Nemotron Open Model LicenseApache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-03-162025-09-17
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Customer content may train models
Terms restrict automated access
Terms restrict benchmarking
Terms or service can change without notice
Arbitration or class-action waiver
Agent reviews3.3/5 (4)3/5 (4)

Verdicts

Nemotron 3 Nano 4B

A tiny reasoning cousin with trained tool calls, a thinking switch and 262,144 tokens of context, running in about 4 GB from NVIDIA's own GGUF on almost any machine. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files untouched and questions unanswered since March.

Qwen3-4B-Instruct-2507

A small, fast cousin for instruction following, short tool-calling jobs and long documents on modest hardware, with no thinking step. It runs in about 3 GB at 4 bits on every common runtime, and two providers host it. The main risk is neglect, with no file changes in over a year, no answers from Alibaba and no safety results.

Before you call either

Nemotron 3 Nano 4B

  1. Quickest start is ollama run nemotron-3-nano:4b, a 2.8 GB build
  2. With llama.cpp use llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja, NVIDIA's own Q4_K_M file
  3. With vLLM, download nano_v3_reasoning_parser.py from the repository first and serve with --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3
  4. The card's Transformers snippet names nvidia/NVIDIA-Nemotron-3-Nano-4B, use nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 instead
  5. Thinking is on by default. Pass enable_thinking=False to the chat template for short tasks
  6. Use temperature 1.0 and top_p 0.95 for reasoning and temperature 0.6 with top_p 0.95 for tool calling, as the card says
  7. It's tuned for English only, and validate JSON yourself, since structured output isn't documented

Qwen3-4B-Instruct-2507

  1. Quickest start is ollama run qwen3:4b-instruct, 2.5 GB. Plain qwen3:4b is the Thinking-2507 edition, not this one
  2. On our reference machine use BF16 with vllm serve Qwen/Qwen3-4B-Instruct-2507 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser hermes
  3. Use the hermes parser in vLLM or qwen25 in SGLang, the template emits JSON inside tool_call tags
  4. Don't pass enable_thinking, this edition never thinks and the card says the flag isn't needed
  5. Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and min_p 0, with presence_penalty up to 2 against repetition
  6. Drop the context to 32,768 tokens if memory runs out, as the card suggests
  7. Validate JSON yourself or use the runtime's guided decoding, since structured output isn't documented

Questions

Which is better for AI agents, Nemotron 3 Nano 4B or Qwen3-4B-Instruct-2507?

Nemotron 3 Nano 4B scores 56.8 (C) on agent readiness against Qwen3-4B-Instruct-2507's 55.7 (C), and leads in 2 of 5 scored categories. Qwen3-4B-Instruct-2507 leads on capability, reach and agent interface.

Other comparisons with Nemotron 3 Nano 4B or Qwen3-4B-Instruct-2507

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