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

EXAONE 4.5 33B vs Swift 1.5 Qwen3.8-27B

EXAONE 4.5 33B and Swift 1.5 Qwen3.8-27B score within a point of each other on agent readiness, 61 (C) and 61 (C). Swift 1.5 Qwen3.8-27B 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

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

  • Agent interface, 85 against 60
  • Documentation and upkeep, 50 against 43

Watch for

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

Swift 1.5 Qwen3.8-27B C

Best for Agents on a 24 to 32 GB GPU or Mac that want Qwen3.8-27B's skills with shorter thinking, for teams under US$1M revenue or research.

Ahead on

  • Reach, 80 against 65

Watch for

Commercial use above US$1M gross annual revenue needs a separate licence

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.

Agent interface EXAONE 4.5 33B +25

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.

Swift 1.5 Qwen3.8-27B 60

Chat template 20, the base's Jinja file. Tool-calling format 10 of 25, the format is only in the template and the parser only in serving commands. Trained tool calling 20, several calls a turn and agentic post-training. Structured output 0, not documented. Reasoning control 0, the card mentions reasoning_effort but not how to set it, turn thinking off or read the reasoning. Context 10, 262,144 tokens native.

Reach Swift 1.5 Qwen3.8-27B +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.

Swift 1.5 Qwen3.8-27B 80

Memory 25 of 30, UkisAI's 4-bit MLX build is 15.9 GB and its 4-bit GGUF with the vision projector 16.4 GB, both under 20 GB with a fifth added, which fits 24 GB. Runtimes 20, the architecture is Qwen3.8-27B's, which released llama.cpp, vLLM, SGLang and Transformers run, while Ollama's entry is the base's. Builds 15, GGUF, INT4, NVFP4, FP8 and MLX builds from UkisAI. Terms 10, a custom licence that is free for commercial use below US$1M revenue. Download 10, not gated. Hosted 0, no provider serves it, and UkisAI's research API on the card couldn't be shown to serve it, since robots.txt disallows calling it.

Documentation and upkeep EXAONE 4.5 33B +7

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.

Swift 1.5 Qwen3.8-27B 43

Model card 8 of 20, intended use, parameters and context only. Training data 5, a general description with no cut-off. Last change 20, UkisAI's own ONNX builds of the model took a new card and licence on 8 October, 26 days ago (the main repository's last commit is the 3 October README update). Responsiveness 0, UkisAI answered none of 8 discussions. Version policy 5, Swift 1.0 stays in its own repository without a note. File health 5, reports of a broken model wait without a reply and the card's metadata claims a gate that doesn't exist.

Score by category

CategoryWeight this runEXAONE 4.5 33BSwift 1.5 Qwen3.8-27BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%256580Swift 1.5 Qwen3.8-27B +15
Risk20%205050even
Agent interface10%108560EXAONE 4.5 33B +25
Documentation and upkeep10%105043EXAONE 4.5 33B +7
Negative events≤1500
TotalC 61/100C 61/100

Facts side by side

FactEXAONE 4.5 33BSwift 1.5 Qwen3.8-27B
KindOpen-weight modelOpen-weight model
VendorLG AI ResearchUkisAI
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceEXAONE AI Model License Agreement 1.2 - NC (research and education only)Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue with a separate Swift Enterprise License above it. Built on Qwen3.8-27B, whose Apache 2.0 still governs the base
Read-only variant documentednono
llms.txtnono
Last release2026-07-062026-09-24
Terms last updatednot read yetno date given
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 reviewsnone2.3/5 (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.

Swift 1.5 Qwen3.8-27B

A Qwen3.8-27B cousin post-trained to think in fewer tokens, for coding and agent work with images in, that fits a 24 GB machine with UkisAI's own 4-bit builds. Commercial use is free only below US$1M revenue. The main risks are that nobody at UkisAI answers its discussions, and that the shorter thinking rests on UkisAI's own benchmarks.

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

Swift 1.5 Qwen3.8-27B

  1. Quickest start is llama-server -hf ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M --jinja, about 18.4 GB with the vision projector
  2. On our reference machine BF16 fits, vllm serve ukisai/Swift-1.5-Qwen3.8-27b --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder
  3. The template is Qwen3.8-27B's, so chat_template_kwargs with reasoning_effort low, medium or xhigh (default) and enable_thinking false work as on the base
  4. UkisAI benchmarked it at temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 0, which is a reasonable starting point
  5. The MLX 4-bit build needs UkisAI's patched MLX-LM and has no image or video chat
  6. Check the licence before commercial use, it stops at US$1M gross annual revenue including affiliates
  7. MTP self-speculative decoding is available with the base's head, flags on the card

Questions

Which is better for AI agents, EXAONE 4.5 33B or Swift 1.5 Qwen3.8-27B?

EXAONE 4.5 33B and Swift 1.5 Qwen3.8-27B score within a point of each other on agent readiness, 61 (C) and 61 (C). Swift 1.5 Qwen3.8-27B leads on reach.

Other comparisons with EXAONE 4.5 33B or Swift 1.5 Qwen3.8-27B

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