# EXAONE 4.5 33B vs Kimi K3.1 Pro > EXAONE 4.5 33B scores 61 (C) to Kimi K3.1 Pro's 48.6 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-kimi-k3-1-pro - Markdown: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-kimi-k3-1-pro.md (~8,650 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 scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 2 of 4 scored categories. 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 - Kimi K3.1 Pro: grade D, 48.6/100, rank #151 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/kimi-k3-1-pro.md · JSON https://www.anchorterminal.com/api/v1/tools/kimi-k3-1-pro.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: - Reach, 65 against 40 - Agent interface, 85 against 65 Watch for: Research and education only. No commercial use of the model or its output ### Kimi K3.1 Pro (D) Good for: Operators with a multi-accelerator node who want Moonshot's newest open-weight model for coding and long agent sessions, and everyone else through an API Watch for: 1,561 GB of files, so it needs a node of eight large accelerators at least ## Why the edge ### Reach: EXAONE 4.5 33B +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. - Kimi K3.1 Pro (40): Memory 0, the only build is Moonshot's 1,561 GB MXFP4 release, far above 256 GB. Runtimes 5, llama.cpp b12242 alone has the model code in a release, since vLLM's recipe needs 0.34.0 and SGLang's cookbook a branch. Builds 10, one precision. Terms 10, free commercial use below a model-as-a-service revenue threshold. Download 10, not gated. Hosted 5, Moonshot's API and Together serve it. 0 + 5 + 10 + 10 + 10 + 5 = 40. ### Agent interface: EXAONE 4.5 33B +20 - 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. - Kimi K3.1 Pro (65): Chat template 10, the format is only code to run. Tool-calling format 10, the parser is named in the runtimes' recipes and the card doesn't say how tools are declared. Trained tool calling 20, several calls a turn on Kimi K3's tools channel. Structured output 5, named among linked guides without the how. Reasoning control 10, reasoning_effort per request. Context 10, 1M tokens. 10 + 10 + 20 + 5 + 10 + 10 = 65. ## Score by category | Category | Weight | EXAONE 4.5 33B | Kimi K3.1 Pro | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 65 | 40 | EXAONE 4.5 33B +25 | | Risk | 20% (20 this run) | 50 | 50 | even | | Agent interface | 10% (10 this run) | 85 | 65 | EXAONE 4.5 33B +20 | | Documentation and upkeep | 10% (10 this run) | 50 | 51 | Kimi K3.1 Pro +1 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **48.6 · D** | | ## Facts side by side | Fact | EXAONE 4.5 33B | Kimi K3.1 Pro | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | LG AI Research | Moonshot AI | | 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) | Kimi K3.1 Pro License (modified MIT) | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-07-06 | 2026-11-05 | | Terms last updated | not read yet | no document linked | | Privacy policy last updated | no document linked | no document linked | ## 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. **Kimi K3.1 Pro.** Moonshot's newest flagship, eight days old when read, for operators with a rack. Only llama.cpp has its model code in a release, with vLLM and SGLang support still on a release candidate and a branch. The licence is permissive below a revenue threshold, and Moonshot hasn't answered a discussion on the repository yet. ## 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 ### Kimi K3.1 Pro 1. Use Moonshot's API or Together unless you have a node of eight B300-class accelerators 2. With vLLM use 0.34.0 or later and the recipe's flags, `--tool-call-parser kimi_k3 --reasoning-parser kimi_k3` 3. Pass the whole assistant message back in later turns, reasoning_content and tool_calls included, as the card requires 4. Set `reasoning_effort` to low, high or max per request. Max is the default and thinking can't be turned off 5. Kimi K3's model code doesn't load it, because the attention layers changed 6. Validate JSON output yourself, since the card doesn't document structured output for the weights ## Questions ### Which is better for AI agents, EXAONE 4.5 33B or Kimi K3.1 Pro? EXAONE 4.5 33B scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 2 of 4 scored categories. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-kimi-k3-1-pro.json, and with the fewest tokens: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-kimi-k3-1-pro.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "exaone-4-5-33b", "b": "kimi-k3-1-pro"}`. 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[Kimi K3.1 Pro vs Recursive Flint](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-recursive-flint.md) - [Kimi K3.1 Pro vs Ripple-65B Preview](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ripple-65b-preview.md) - [Kimi K3.1 Pro vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-sensenova-loop-9b-preview.md) - [Kimi K3.1 Pro vs SmolLM3-3B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-smollm3-3b.md) - [Kimi K3.1 Pro vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-spark-x2-6-4b.md) - [Kimi K3.1 Pro vs Step 3.7 Flash](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-step-3-7-flash.md) - [Kimi K3.1 Pro vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-swift-1-5-qwen3-8-27b.md) - [Kimi K3.1 Pro vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-swift-1-5-qwen3-8-flash-next.md) - [Kimi K3.1 Pro vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-thinkingcap-qwen3-8-27b.md) - [Kimi K3.1 Pro vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-unlimited-ocr-2.md) - [Kimi K3.1 Pro vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-2b.md) - [Kimi K3.1 Pro vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-4b.md) - [Kimi K3.1 Pro vs ZAYA1-8B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-zaya1-8b.md) - [Kimi K3 vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/kimi-k3-vs-kimi-k3-1-pro.md) --- Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). How: https://www.anchorterminal.com/agents/#mcp