# EXAONE 4.5 33B vs Qwen3.8-Flash-Next > Qwen3.8-Flash-Next and EXAONE 4.5 33B score within a point of each other for open-weight models you run yourself, 61.8 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-qwen3-8-flash-next - Markdown: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-8-flash-next.md (~9,850 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 Qwen3.8-Flash-Next and EXAONE 4.5 33B score within a point of each other on agent readiness, 61.8 (C) and 61 (C). EXAONE 4.5 33B leads on risk, agent interface and documentation and upkeep. 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 - Qwen3.8-Flash-Next: grade C, 61.8/100, rank #34 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-8-flash-next.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-8-flash-next.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: - Risk, 50 against 45 - Agent interface, 85 against 70 - Documentation and upkeep, 50 against 36 Watch for: Research and education only. No commercial use of the model or its output ### Qwen3.8-Flash-Next (C) Good for: Teams with a multi-GPU server or a 128 GB Mac who want Qwen's newest sparse architecture for agentic coding and multimodal work, and aren't a hosted-model or coding-assistant business. Watch for: No build fits the reference machine's 107.4 GB, and Ollama's smallest Linux build is 120 GB ## Why the edge ### Agent interface: EXAONE 4.5 33B +15 - 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. - Qwen3.8-Flash-Next (70): Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the format lives only in the template and the parser name only in serving commands, and Alibaba doesn't describe how tools are declared or returned. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented for the weights. Reasoning control 10, thinking turns off per request, reasoning_effort budgets it and the card shows the reasoning coming back in reasoning_content. Context 10, 262,144 tokens native. ### Documentation and upkeep: EXAONE 4.5 33B +14 - 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. - Qwen3.8-Flash-Next (36): Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, languages and a knowledge cut-off aren't. Training data 0, not described beyond a token count relative to an earlier model. Last change 15, the FP8 config fix on 31 August 2026, 63 days before 2 November. Responsiveness 0, Qwen answered none of the 40 most recent questions and reports (29 to 79, 27 August to 26 October), its only reply came in the first week. Version policy 5, a first release with nothing replaced. File health 5, the files agree with the card, but a 19 October report about the template and a pull request to fix it still wait without a reply. 11 + 0 + 15 + 0 + 5 + 5 = 36. ### Risk: EXAONE 4.5 33B +5 - 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. - Qwen3.8-Flash-Next (45): File safety 25, safetensors only and nothing from the repository runs to load it or build prompts. Provenance 15, the files are in the Qwen organisation that Qwen's GitHub links to, and every file has a checksum. Safety evaluation 0, none published on the card or in the technical report. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 5 of 15, 5 off for the field-of-use limit on model-as-a-service and coding or office assistant businesses and 5 off for the naming duty above a user or revenue threshold. Security programme 0, no security.txt, disclosure policy or security contact for Qwen was found, and Alibaba's group security centre couldn't be checked, so it's scored as absent. ## Score by category | Category | Weight | EXAONE 4.5 33B | Qwen3.8-Flash-Next | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 65 | 65 | even | | Risk | 20% (20 this run) | 50 | 45 | EXAONE 4.5 33B +5 | | Agent interface | 10% (10 this run) | 85 | 70 | EXAONE 4.5 33B +15 | | Documentation and upkeep | 10% (10 this run) | 50 | 36 | EXAONE 4.5 33B +14 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **61.8 · C** | | ## Facts side by side | Fact | EXAONE 4.5 33B | Qwen3.8-Flash-Next | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | LG AI Research | Alibaba (Qwen team) | | 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) | Qwen Community License 1.0 (custom, not an OSI licence). Free of charge, the model name shown in products above 100M monthly users or US$20M monthly revenue, and a separate licence from Qwen before commercial use by a Model as a Service or AI Work Assistant business | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-07-06 | 2026-08-31 | | Terms last updated | not read yet | no document linked | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | none | 2/5 (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. **Qwen3.8-Flash-Next.** A large sparse cousin with 6B active parameters for agentic coding and multimodal work, previewing Qwen's next architecture. It needs a 128 GB Mac for Ollama's MLX build or about 144 GB for the 4-bit GGUF, and Qwen Cloud serves it. The main risk is a licence that needs a separate deal for hosted-model and coding or office assistant businesses. ## 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 ### Qwen3.8-Flash-Next 1. On a 128 GB Mac start it with `ollama run qwen3.8-flash-next:125b-mlx` (105 GB, NVFP4), on Linux the smallest Ollama build is `qwen3.8-flash-next:125b-a6b-q4_K_M` at 120 GB, which won't fit a 107.4 GB GPU share 2. On a server, `vllm serve Qwen/Qwen3.8-Flash-Next-FP8 --tensor-parallel-size 4 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` 3. Use the qwen3_coder tool parser and the qwen3 reasoning parser, as Qwen's GitHub README does 4. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false to skip it, and read the reasoning from `reasoning_content`, even though SGLang's cookbook says thinking can't be turned off 5. `reasoning_effort` takes xhigh (default), medium or low, and any other value makes the template raise an error 6. Send at most one system message and put it first, since the template raises an error otherwise 7. Sampling per the card is temperature 1.0 and top_p 0.95 when thinking, temperature 0.7, top_p 0.8 and presence_penalty 1.5 without 8. vLLM keeps the 51B n-gram table in host memory on 80 GB GPUs by default, per its recipe 9. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented 10. Read the Qwen Community License before serving it to others as an API or inside a coding or office assistant product ## Questions ### Which is better for AI agents, EXAONE 4.5 33B or Qwen3.8-Flash-Next? Qwen3.8-Flash-Next and EXAONE 4.5 33B score within a point of each other on agent readiness, 61.8 (C) and 61 (C). EXAONE 4.5 33B leads on risk, agent interface and documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-8-flash-next.json, and with the fewest tokens: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-8-flash-next.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "exaone-4-5-33b", "b": "qwen3-8-flash-next"}`. 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