Head to head · Open-weight models you run yourself · October 2026 research run
EXAONE 4.5 33B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
EXAONE 4.5 33B and NVIDIA Nemotron 3.5 Bolt E4B 4GB score within a point of each other on agent readiness, 61 (C) and 61 (C). NVIDIA Nemotron 3.5 Bolt E4B 4GB leads on reach, risk and documentation and upkeep. 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
Which one, for what
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
NVIDIA Nemotron 3.5 Bolt E4B 4GB C
Best for Agents and apps that need a fast local conversational model with tool calls on an 8 to 12 GB GPU, a Mac or a Jetson, such as voice assistants and game characters.
Ahead on
- Reach, 90 against 65
- Risk, 80 against 50
- Documentation and upkeep, 65 against 50
Watch for
64K tokens of context against the base's 128K, and Ollama's and LM Studio's pages show 128K anyway
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.
Risk NVIDIA Nemotron 3.5 Bolt E4B 4GB +30
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 Agent guidance 5, General cautions: the limitation section lists inappropriate, biased, incorrect and outdated answers and tells users not to try to induce harmful output.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.
NVIDIA Nemotron 3.5 Bolt E4B 4GB 80
File safety earns 25, safetensors and GGUF only, and the documented routes run no repository code (an undocumented Inference Endpoints handler.py sits in the BF16 repository). Provenance earns 15, NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, since NVIDIA names its content-safety and bias datasets but publishes no results. Agent guidance earns 15, for instruction-hierarchy training NVIDIA documents and a subcard on prompt injection and the safeguards to add. Licence risk keeps all 15 under OpenMDW-1.1 and Apache 2.0. The security programme earns 10 for the card's reporting route and NVIDIA's public bulletins.
Reach NVIDIA Nemotron 3.5 Bolt E4B 4GB +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.
NVIDIA Nemotron 3.5 Bolt E4B 4GB 90
Memory earns 30, since NVIDIA's Q4_K_M GGUF is 6.18 GB, 7.4 GB with a fifth added. Runtimes earn the full 25 from released llama.cpp, vLLM, Transformers, SGLang, Ollama and LM Studio. Builds earn 10, as NVIDIA's only quantised build is the Q4_K_M GGUF and the DFlash repositories are drafters. Terms earn 10, because OpenMDW-1.1 is permissive with no threshold or use policy but isn't an OSI licence, and the Gemma 4 base adds plain Apache 2.0. Download earns 10, with no gate. Hosted earns 5, as NVIDIA's own API serves it.
Documentation and upkeep NVIDIA Nemotron 3.5 Bolt E4B 4GB +15
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.
NVIDIA Nemotron 3.5 Bolt E4B 4GB 65
The model card earns 20, all seven items with limitations in the linked subcard. Training data earns 15, the RL datasets published and a cut-off given, but the card's data section describes a larger SFT corpus the report says wasn't used, and the base's pre-training is described only in general. Last change earns 20 for 23 October 2026. Responsiveness earns 0, as NVIDIA hasn't answered either of the two discussions. Version policy earns 5 as a first release whose GGUF was replaced on release day with only a commit title. File health earns 5, since the card and the template disagree on the thinking default and both cards carry leftover lines.
Score by category
| Category | Weight this run | EXAONE 4.5 33B | NVIDIA Nemotron 3.5 Bolt E4B 4GB | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 65 | 90 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +25 |
| Risk | 20%20 | 50 | 80 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +30 |
| Agent interface | 10%10 | 85 | 85 | even |
| Documentation and upkeep | 10%10 | 50 | 65 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +15 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 61/100 |
Facts side by side
| Fact | EXAONE 4.5 33B | NVIDIA Nemotron 3.5 Bolt E4B 4GB |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | LG AI Research | NVIDIA |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | EXAONE AI Model License Agreement 1.2 - NC (research and education only) | OpenMDW-1.1 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-07-06 | 2026-10-23 |
| Terms last updated | not read yet | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 4/5 (4) |
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.
NVIDIA Nemotron 3.5 Bolt E4B 4GB
A small cousin for voice and device assistants, game characters and robot interfaces, trained for multi-turn tool calls, instruction hierarchy and short spoken replies, with thinking switchable per request. NVIDIA's Q4_K_M GGUF needs about 8 GB, and Ollama and LM Studio carry it. Context is 64K, half the Gemma 4 base, and NVIDIA publishes no safety results.
Before you call either
EXAONE 4.5 33B
- Don't use it, or anything it writes, commercially. The licence covers the output too
- Serve it with vLLM and
--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser hermes - Set
enable_thinkingfalse for a direct answer. Thinking is on by default - 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
- For llama.cpp, load a projector file from LG's GGUF repository with the weights for image input
- Its knowledge stops at December 2024
NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Quickest start is
ollama run nemotron-3.5-bolt:e4b, about 8 GB of files, and setnum_ctxto 65536 or less, since NVIDIA supports 64K - With NVIDIA's GGUF use
llama-server -m Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf -c 65536 -ngl 999 --jinja, and add--reasoning on --reasoning-budget <tokens>to cap thinking - With vLLM 0.31.0 or later pass
--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4and the repository's chat_template.jinja - Pass
enable_thinkingexplicitly inchat_template_kwargs, since the template defaults to off whatever the card says, and setskip_special_tokensfalse as the card's examples do - Use
temperature=1.0andtop_p=0.95in both modes, as the card says - Set
conversational_modetrue for spoken replies, which appends a brevity instruction to the system prompt - Answer a slow tool at once with a pending
toolmessage, then send the result later as a user message starting TOOL RESULT, as the card's asynchronous pattern says - Keep persistent rules in the system prompt and send state updates as separate user or system messages in order, per the card's prompting guidelines
Questions
Which is better for AI agents, EXAONE 4.5 33B or NVIDIA Nemotron 3.5 Bolt E4B 4GB?
EXAONE 4.5 33B and NVIDIA Nemotron 3.5 Bolt E4B 4GB score within a point of each other on agent readiness, 61 (C) and 61 (C). NVIDIA Nemotron 3.5 Bolt E4B 4GB leads on reach, risk and documentation and upkeep.
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- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-VL-8B-Instruct
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- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Recursive Flint
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Ripple-65B Preview
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs SenseNova-Loop-9B-Preview
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs SmolLM3-3B
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Spark-X2.6-4B
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Step 3.7 Flash
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Swift 1.5 Qwen3.8-27B
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Swift 1.5 Qwen3.8-Flash-Next
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs ThinkingCap (Qwen3.8 27B)
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Baidu Unlimited-OCR-2
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs WeVisOne-VL-2B
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs WeVisOne-VL-4B
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/exaone-4-5-33b-vs-nemotron-3-5-bolt-e4b-4gb.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/exaone-4-5-33b.json·/api/v1/tools/nemotron-3-5-bolt-e4b-4gb.json - From a terminal
anchor compare exaone-4-5-33b nemotron-3-5-bolt-e4b-4gb(the CLI) - Over MCP
compare_tools {"a": "exaone-4-5-33b", "b": "nemotron-3-5-bolt-e4b-4gb"}at/mcp, no key