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

EXAONE 4.5 33B vs Inkling

Inkling scores 64.7 (B) on agent readiness against EXAONE 4.5 33B's 61 (C), and leads in 2 of 4 scored categories. EXAONE 4.5 33B leads on reach. 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 InklingThinking Machines Lab · Open-weight model B64.7/100#19 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

Ahead on

  • Reach, 65 against 50

Watch for

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

Inkling B

Best for Agents calling a hosted open multimodal model with audio input, or teams with a multi-GPU datacentre node who want to run it themselves.

Ahead on

  • Risk, 70 against 50
  • Documentation and upkeep, 87 against 50

Watch for

The smallest build Thinking Machines publishes needs about 710 GB, beyond any single workstation

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 Inkling +37

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.

Inkling 87

Model card 17 of 20, everything but a dated knowledge cut-off, counting Thinking Machines' own model card page. Training data 15, an EU training-content summary with sources, mix, a July 2026 collection cut-off and filtering. Last change 20, the weights files last changed on 23 July 2026, and Thinking Machines' Inkling renderer in tinker-cookbook changed on 30 October 2026, 3 days ago. Responsiveness 20, Thinking Machines answered 8 of the 12 questions and reports in its discussions. Version policy 5, a first release. File health 10, the files agree and the reports were answered.

Risk Inkling +20

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.

Inkling 70

File safety 25, safetensors only and no repository code to run. Provenance 15, the files are in the organisation Thinking Machines links to, with a SHA-256 on every LFS file. Safety evaluation 10, Thinking Machines' own StrongREJECT and FORTRESS results and commissioned external testers who published nothing. Agent guidance 10, the card says what its refusals miss and which safeguards to add, with nothing on prompt injection. Licence risk 5, 10 off for a use policy that reaches beyond the law and that can change at any time. Security programme 5, a security.txt and a disclosure policy, with public advisories unchecked and scored as absent.

Reach EXAONE 4.5 33B +15

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.

Inkling 50

Memory 0 of 30, the smallest 4-bit build is Thinking Machines' NVFP4 at 592.0 GB, about 710 GB with a fifth added, far above 256 GB. Runtimes 15, released Transformers, vLLM and SGLang run it, llama.cpp, Ollama and LM Studio don't. Builds 10, one quantised build (NVFP4). Terms 10, Apache 2.0 with a binding acceptable use policy on top, read as a custom licence. Download 10, no gate. Hosted 5, Together AI, Fireworks and DeepInfra are live on Hugging Face, and Thinking Machines' Tinker serves it too.

Score by category

CategoryWeight this runEXAONE 4.5 33BInklingEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%256550EXAONE 4.5 33B +15
Risk20%205070Inkling +20
Agent interface10%108585even
Documentation and upkeep10%105087Inkling +37
Negative events≤1500
TotalC 61/100B 64.7/100

Facts side by side

FactEXAONE 4.5 33BInkling
KindOpen-weight modelOpen-weight model
VendorLG AI ResearchThinking Machines Lab
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceEXAONE AI Model License Agreement 1.2 - NC (research and education only)Apache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-07-062026-07-23
Terms last updatednot read yetno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviewsnone3/5 (4)

How the scores have moved

EXAONE 4.5 33B

No score change since it was listed.

Inkling

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.

Inkling

Thinking Machines' large multimodal generalist, stronger than Inkling-Small on factual recall by its own numbers. It needs about 710 GB of datacentre GPU memory even in NVFP4, so most agents will use a host such as Together AI or Tinker. The main risks are a use policy that binds on download and can change, and no outside safety results.

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

Inkling

  1. Unless you have a multi-GPU Blackwell or H200 node, call it through Together AI, Fireworks, DeepInfra or Tinker rather than starting it
  2. On 8 B200 GPUs, SGLang's recipe is sglang serve --trust-remote-code --model-path thinkingmachines/Inkling-NVFP4 --tp 8 --quantization modelopt_fp4 --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling
  3. Use the inkling tool parser, not Hermes, and the inkling reasoning parser
  4. Pass reasoning_effort as none, minimal, low, medium, high (the default) or max, or a number from 0 to 0.99. The repository's template has no xhigh preset and raises an error for it, though Thinking Machines' docs list xhigh
  5. Tool call arguments must reach the template as an object, not a JSON string, or it raises an error
  6. Audio should be 16 kHz WAV, ideally under 20 minutes, and images 40 to 4096 px a side, per the card
  7. Read the Model Acceptable Use Policy before deploying, it binds on download and bars some uses that are legal

Questions

Which is better for AI agents, EXAONE 4.5 33B or Inkling?

Inkling scores 64.7 (B) on agent readiness against EXAONE 4.5 33B's 61 (C), and leads in 2 of 4 scored categories. EXAONE 4.5 33B leads on reach.

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