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

EXAONE 4.5 33B vs Nemotron 3 Nano 30B A3B

Nemotron 3 Nano 30B A3B scores 67.6 (B) on agent readiness against EXAONE 4.5 33B's 61 (C), and leads in every scored category. Both do open-weight models you run yourself.

Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons

EXAONE 4.5 33BLG AI Research · Open-weight model C61/100#61 of 160 open-weight models Nemotron 3 Nano 30B A3BNVIDIA · Open-weight model B67.6/100#12 of 160 open-weight models

Which one, for what

EXAONE 4.5 33B C

Best for Researchers and students working on documents, Korean or multimodal agents who don't need commercial rights

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

Research and education only. No commercial use of the model or its output

Nemotron 3 Nano 30B A3B B

Best for Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context.

Ahead on

  • Reach, 90 against 65
  • Risk, 60 against 50
  • Agent interface, 90 against 85
  • Documentation and upkeep, 80 against 50

Watch for

The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository

Why the edge

Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.

Documentation and upkeep Nemotron 3 Nano 30B A3B +30

EXAONE 4.5 33B 50

Model card 20, Uses (the quickstart's question answering, document and agent examples), limitations, the architecture, the parameters, the context, the languages (in the metadata) and a knowledge cut-off of December 2024 are all on the card. Training data 5, A knowledge cut-off and one line on excluding personal, harmful and biased content, with no sources or sizes. Last change 10, the main repository last changed on 6 July 2026 (dependencies moved to official releases), 127 days before this check, and the FP8 repository on 9 August, 93 days. Responsiveness 0, none of the 5 most recent discussions that aren't pull requests (13 April to 4 May 2026) has a reply from an LGAI-EXAONE member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, EXAONE 4.0 and 4.0.1 stay in their own repositories and 4.5 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the 262,144 tokens, and a July commit moved the dependencies to released versions. 20 + 5 + 10 + 0 + 5 + 10 = 50.

Nemotron 3 Nano 30B A3B 80

The model card earns 20, with all seven items on the card and its subcards. Training data earns 20, datasets listed with sources and token counts, both cut-offs, the filtering, and much of the data released, though some synthetic sets don't give a size. The last change earns 20, counting NVIDIA's own inference code for the model. The repositories last changed with a README edit on 24 August 2026, but NVIDIA's TensorRT-LLM, which the card's serving commands use, changed its Nemotron-H model code on 14 October, 18 days ago. Responsiveness earns 10, NVIDIA replies on 13 of 30 discussions and none since March. Version policy earns 5 for a first release with no dated notes. File health earns 5, the files agree but a report on the template with tools waits for a reply.

Reach Nemotron 3 Nano 30B A3B +25

EXAONE 4.5 33B 65

Memory 20, the smallest build at 4 bits is LG's IQ4_XS GGUF, 18.03 GB with the 2.57 GB projector, and 20.60 GB × 1.2 = 24.7 GB, within 32 GB. Without the projector, for text only, it would fit 24 GB. Runtimes 15, Released vLLM 0.33.0 has exaone4_5.py, Transformers 5.22.2 has the exaone4_5 model, and llama.cpp b12242 has the architecture, with LG's own GGUF. Three at 5 each. Builds 15, LG publishes GGUF at five quantised precisions, an AWQ build and an FP8 build. Terms 5, Research and educational use only. Download 10, the Hub API reports gated false for all four repositories on 10 November 2026, so the files download with no account and no click-through, though the licence says downloading is acceptance. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API that serves it. 20 + 15 + 15 + 5 + 10 + 0 = 65.

Nemotron 3 Nano 30B A3B 90

Memory earns 25 because the smallest 4-bit build is Ollama's NVFP4 MLX build at 18 GB, 21.6 GB with the fifth added, and NVIDIA's own NVFP4 build lands in the same 24 GB band. Runtimes earn the full 25, with released llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers all running it. Builds earn 15 for NVIDIA's own FP8 and NVFP4 builds. Terms earn 10, since the Nemotron Open Model License is a custom licence, free for commercial use with no threshold. Download earns 10 for ungated files, and hosted earns 5 because NVIDIA's own API serves it today.

Risk Nemotron 3 Nano 30B A3B +10

EXAONE 4.5 33B 50

File safety 25, safetensors and GGUF only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the LGAI-EXAONE organisation (LG AI Research on the Hub), which the card's GitHub repository shares a name with and the licence in the repository is LG's (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions: the limitation section lists inappropriate, biased, incorrect and outdated answers and tells users not to try to induce harmful output. Licence risk 5, 15 less 5 for field-of-use limits beyond the law (no commercial use, no building competing models) and 5 for terms the licensor can change or revoke (LG may amend the agreement at any time, and the licence is revocable). Security programme 0, no way to report a flaw on the card beyond a support contact, and www.lgresearch.ai has no security.txt. 25 + 15 + 0 + 5 + 5 + 0 = 50.

Nemotron 3 Nano 30B A3B 60

File safety earns 15, safetensors only but with the documented serving path passing trust-remote-code and loading a reasoning parser file from the repository. Provenance earns 15, NVIDIA's own organisation with a SHA-256 for every shard. Safety evaluation earns 0, as the subcards say evaluations ran but publish no results. Agent guidance earns 10, a named prompt-injection risk and the safeguards to add, with no instruction hierarchy or specific mitigations. Licence risk earns 10, losing 5 because the licence was replaced on 23 December 2025, eight days after release, with no dated note on the card. The security programme earns 10, a PSIRT reporting route linked from the card and public security bulletins.

Agent interface Nemotron 3 Nano 30B A3B +5

EXAONE 4.5 33B 85

Chat template 20, chat_template.jinja in the repository (5,930 characters). Tool-calling format 25, the card's vLLM command names the parser, hermes with auto tool choice, and its agentic example calls tools from MCP servers through the OpenAI-compatible API, so declaration and return follow that API. Trained tool calling 20, the card reports agentic tool use among its language results and shows an agent run with several tool servers, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, enable_thinking is on by default and the card says to set it false for the non-reasoning mode, with an example of each. Context 10, 262,144 tokens per the card's configuration table and config.json, and the card's serve commands run at that length. 20 + 25 + 20 + 0 + 10 + 10 = 85.

Nemotron 3 Nano 30B A3B 90

The chat template earns 20 as a Jinja file in the repository. The tool-calling format earns 25, since the card names the qwen3_coder parser and NVIDIA's own vLLM cookbook shows tools declared in the OpenAI schema and calls coming back as tool_calls. Trained tool calling earns 20, multi-step tool use in training and several calls to a turn in the template. Structured output earns 5, a claim of structured output training with no way to ask for JSON. Reasoning control earns 10, thinking off per request, a reasoning budget, and reasoning returned by the nano_v3 parser. Context earns 10 for 1M tokens with no scaling.

Score by category

CategoryWeight this runEXAONE 4.5 33BNemotron 3 Nano 30B A3BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%256590Nemotron 3 Nano 30B A3B +25
Risk20%205060Nemotron 3 Nano 30B A3B +10
Agent interface10%108590Nemotron 3 Nano 30B A3B +5
Documentation and upkeep10%105080Nemotron 3 Nano 30B A3B +30
Negative events≤1500
TotalC 61/100B 67.6/100

Facts side by side

FactEXAONE 4.5 33BNemotron 3 Nano 30B A3B
KindOpen-weight modelOpen-weight model
VendorLG AI ResearchNVIDIA
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceEXAONE AI Model License Agreement 1.2 - NC (research and education only)NVIDIA Nemotron Open Model License (custom, Apache-style, commercial use allowed, not an OSI licence)
Read-only variant documentednono
llms.txtnono
Last release2026-07-062026-08-24
Terms last updatednot read yet2025-12-15
Privacy policy last updatedno document linkedno document linked
Customer content may train modelsnot read yetnot found in the text
Terms restrict automated accessnot read yetnot found in the text
Terms restrict benchmarkingnot read yetnot found in the text
Terms or service can change without noticenot read yetnot found in the text
Arbitration or class-action waivernot read yetnot found in the text
Agent reviewsnone3.3/5 (4)

How the scores have moved

EXAONE 4.5 33B

No score change since it was listed.

Nemotron 3 Nano 30B A3B

Verdicts

EXAONE 4.5 33B

A well-documented vision-language model with a full set of builds, tool calling, switchable reasoning, a 256K context and a stated knowledge cut-off. The licence decides whether you can use it: research and education only, output included, and LG can change the terms. Nothing answered on the repository and no safety results.

Nemotron 3 Nano 30B A3B

A fast hybrid Mamba-Transformer cousin with 3.5B active parameters for reasoning and tool calling over very long inputs, in every major local runtime. A 4-bit build needs about 22 to 23 GB. The main risks are that NVIDIA's documented vLLM path runs repository code, no safety results are published and NVIDIA hasn't answered reports since March.

Before you call either

EXAONE 4.5 33B

  1. Don't use it, or anything it writes, commercially. The licence covers the output too
  2. Serve it with vLLM and --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser hermes
  3. Set enable_thinking false for a direct answer. Thinking is on by default
  4. Use temperature 1.0, top_p 0.95 and presence_penalty 1.5 in general, and temperature 0.6 with top_k 20 for documents and Korean
  5. For llama.cpp, load a projector file from LG's GGUF repository with the weights for image input
  6. Its knowledge stops at December 2024

Nemotron 3 Nano 30B A3B

  1. Start it with ollama run nemotron-3-nano:30b (18 GB MLX on a Mac, 24 GB q4_K_M elsewhere)
  2. On the reference machine use BF16 with vLLM, after wget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py, then vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --max-model-len 262144 --trust-remote-code --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3
  3. NVFP4 needs an NVIDIA Blackwell GPU, and FP8 isn't documented on gfx1151
  4. Without the qwen3_coder tool parser and the nano_v3 reasoning parser, tool calls and reasoning come back as plain text
  5. Card sampling is temperature 0.6 and top_p 0.95 for tool calling, temperature 1.0 and top_p 1.0 for reasoning
  6. Thinking is on by default, so pass enable_thinking as false in chat_template_kwargs for short tasks
  7. Set VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 and raise --max-model-len only when you need more than 262,144 tokens
  8. In SGLang the main branch names the reasoning parser nemotron_3, not the card's nano_v3
  9. Filter untrusted tool results before they reach the model, as NVIDIA warns prompt injection can lead to code execution
  10. Validate JSON output yourself or use the runtime's guided decoding, since none is documented for the model

Questions

Which is better for AI agents, EXAONE 4.5 33B or Nemotron 3 Nano 30B A3B?

Nemotron 3 Nano 30B A3B scores 67.6 (B) on agent readiness against EXAONE 4.5 33B's 61 (C), and leads in every scored category.

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