# EXAONE 4.5 33B vs Qwopus3.8-27B-Flash-V2 > EXAONE 4.5 33B and Qwopus3.8-27B-Flash-V2 score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwopus3-8-27b-flash-v2 - Markdown: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwopus3-8-27b-flash-v2.md (~9,300 tokens) - Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: https://www.anchorterminal.com/llms-full.txt) - API: https://www.anchorterminal.com/api/v1/index.json - MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key) - DNS: dig +short TXT .grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.. and .vs.. under the zone, and the same under grade.letme.dev) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-11-10 EXAONE 4.5 33B and Qwopus3.8-27B-Flash-V2 score within a point of each other on agent readiness, 61 (C) and 61 (C). Qwopus3.8-27B-Flash-V2 leads on reach. Both do open-weight models you run yourself. - EXAONE 4.5 33B: grade C, 61/100, rank #61 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/exaone-4-5-33b.md · JSON https://www.anchorterminal.com/api/v1/tools/exaone-4-5-33b.json - Qwopus3.8-27B-Flash-V2: grade C, 61/100, rank #53 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwopus3-8-27b-flash-v2.md · JSON https://www.anchorterminal.com/api/v1/tools/qwopus3-8-27b-flash-v2.json - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### EXAONE 4.5 33B (C) Good for: Researchers and students working on documents, Korean or multimodal agents who don't need commercial rights Ahead on: - Agent interface, 85 against 60 Watch for: Research and education only. No commercial use of the model or its output ### Qwopus3.8-27B-Flash-V2 (C) Good for: Hobbyist and research agents that want a Qwen3.8-27B tuned for shorter reasoning, for local coding loops on a 24 GB GPU. Ahead on: - Reach, 85 against 65 Watch for: Published from a personal account, with no organisation, security contact or safety evaluation ## Why the edge ### 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. - Qwopus3.8-27B-Flash-V2 (60): Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, only the template shows the Hermes-style JSON calls, which differ from the base's format. Trained tool calling 20, several calls a turn. Structured output 0, not documented. Reasoning control 0, the switches are documented but not where the reasoning comes back. Context 10, 262,144 tokens from the base. ### Reach: Qwopus3.8-27B-Flash-V2 +20 - 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. - Qwopus3.8-27B-Flash-V2 (85): Memory 25 of 30, Jackrong's IQ4_XS GGUF is 15.4 GB, about 18.5 GB with a fifth added, which fits 24 GB. Runtimes 20, it keeps Qwen3.8-27B's architecture, which released vLLM, SGLang, Transformers and llama.cpp run, while Ollama's entry is the base's, not this model's. Builds 15, GGUFs at many precisions from 4-bit to 8-bit and BF16. Terms 15, Apache 2.0 through the chain. Download 10, nothing is gated. Hosted 0, no provider serves it. ## Score by category | Category | Weight | EXAONE 4.5 33B | Qwopus3.8-27B-Flash-V2 | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 65 | 85 | Qwopus3.8-27B-Flash-V2 +20 | | Risk | 20% (20 this run) | 50 | 50 | even | | Agent interface | 10% (10 this run) | 85 | 60 | EXAONE 4.5 33B +25 | | Documentation and upkeep | 10% (10 this run) | 50 | 51 | Qwopus3.8-27B-Flash-V2 +1 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **61 · C** | | ## Facts side by side | Fact | EXAONE 4.5 33B | Qwopus3.8-27B-Flash-V2 | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | LG AI Research | Jackrong | | 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) | Apache-2.0 (metadata only, no LICENSE file); built on Qwen3.8-27B under Apache 2.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-07-06 | 2026-09-21 | | Terms last updated | not read yet | no document linked | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | none | 2.5/5 (4) | ## How the scores have moved ### EXAONE 4.5 33B No score change since it was listed. ### Qwopus3.8-27B-Flash-V2 - no change on 3 November 2026: time ## 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. **Qwopus3.8-27B-Flash-V2.** A Qwen3.8-27B cousin tuned to think less per turn in long agent loops, with an MTP head for speculative decoding in llama.cpp. It fits 24 GB with Jackrong's 4-bit GGUF. The risks are one person's experimental release with no safety work, training traces taken from Claude and GPT, and a tool format that differs from Qwen's. ## 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 ### Qwopus3.8-27B-Flash-V2 1. Quickest start is `llama-server -hf Jackrong/Qwopus3.8-27B-Flash-V2-GGUF:Q4_K_M --jinja`, about 17 GB, and add mmproj-F32.gguf for images 2. The card's tests used Q5_K_M with the bundled MTP head and `--spec-type draft-mtp` in llama.cpp 3. The template asks for tool calls as JSON inside tool_call tags, so use a Hermes-style parser (vLLM's hermes), not Qwen3.8's qwen3_coder 4. For coding the card recommends enable_thinking true, reasoning_effort xhigh and temperature 0.3, which you must pass yourself, since generation_config.json says 1.0 5. reasoning_effort takes xhigh (default), medium or low, and any other value makes the template raise an error 6. Earlier reasoning stays in the context unless preserve_thinking is false 7. Cap max_tokens, since a report says it loops more than V1 ## Questions ### Which is better for AI agents, EXAONE 4.5 33B or Qwopus3.8-27B-Flash-V2? EXAONE 4.5 33B and Qwopus3.8-27B-Flash-V2 score within a point of each other on agent readiness, 61 (C) and 61 (C). Qwopus3.8-27B-Flash-V2 leads on reach. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwopus3-8-27b-flash-v2.json, and with the fewest tokens: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwopus3-8-27b-flash-v2.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "exaone-4-5-33b", "b": "qwopus3-8-27b-flash-v2"}`. 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