# Arete-27B vs Kimi K3 > Kimi K3 scores 62.4 (B) to Arete-27B's 55.1 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/arete-27b-vs-kimi-k3 - Markdown: https://www.anchorterminal.com/compare/arete-27b-vs-kimi-k3.md (~8,300 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 Kimi K3 scores 62.4 (B) on agent readiness against Arete-27B's 55.1 (C), and leads in 2 of 4 scored categories. Arete-27B leads on documentation and upkeep. Both do open-weight models you run yourself. - Arete-27B: grade C, 55.1/100, rank #123 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/arete-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/arete-27b.json - Kimi K3: grade B, 62.4/100, rank #30 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/kimi-k3.md · JSON https://www.anchorterminal.com/api/v1/tools/kimi-k3.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 ### Arete-27B (C) Good for: Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine Ahead on: - Documentation and upkeep, 63 against 41 Watch for: No quantised build from Nous Research, so the GGUF route depends on a third party ### Kimi K3 (B) Good for: Teams with a multi-node GPU cluster who want an open 1M-context agent model for long coding and research work Ahead on: - Risk, 65 against 55 - Agent interface, 65 against 60 Watch for: 1,561 GB of files at its smallest publisher build, far beyond any single machine ## Why the edge ### Documentation and upkeep: Arete-27B +22 - Arete-27B (63): Model card 8, no limitations, context length, languages or cut-off. Training data 20, the training pairs and the evaluation set are published with their counts. Last change 20, a commit on 6 November 2026. Responsiveness 0, none of 13 discussions has a reply from Nous Research. Version policy 5, a first release. File health 10, the files agree. 8 + 20 + 20 + 0 + 5 + 10 = 63. - Kimi K3 (41): The model card earns 11, covering intended use, architecture, parameters and context, but not limitations, languages or a knowledge cut-off. Training data earns 5 for a general description with no cut-off. The last change earns 15, an encoding_k3.py update on 20 August, 74 days before 2 November and within 90; the GitHub repository's last commit is 6 August. Responsiveness earns nothing, with no Moonshot reply in the 40 most recent discussions. Version policy earns 5 for a first release. File health earns 5, because a report that the tokeniser goes past the declared vocabulary waits without a reply. ### Risk: Kimi K3 +10 - Arete-27B (55): File safety 25, safetensors only and no remote code. Provenance 15, Nous Research's own organisation, linked from its blog, with a hash on every file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 15, plain Apache 2.0. Security programme 0, no way to report a flaw found. 25 + 15 + 0 + 0 + 15 + 0 = 55. - Kimi K3 (65): File safety earns 15, safetensors only with no pickle files, less 10 because the documented loading path runs Moonshot's Python with trust_remote_code and builds prompts with encoding_k3.py. Provenance earns the full 15, files in the organisation Moonshot links to with a SHA-256 for every shard. Safety evaluation earns 20 for the cyber assessment the UK AI Security Institute and CAISI ran and published, which found its safeguards didn't stop exploit development. Agent guidance earns 5 for the blog's general cautions about proactiveness and passing back thinking. Licence risk earns 10, losing 5 for the model-as-a-service revenue threshold and nothing else. The security programme earns nothing, with no security.txt, disclosure policy, SECURITY.md or public advisories found. ### Agent interface: Kimi K3 +5 - Arete-27B (60): Chat template 20, a Jinja file in the repository. Tool-calling format 10, only the template says it. Trained tool calling 20, several calls a turn and agentic work as an intended use. Structured output 0 and reasoning control 0, neither is documented on the card. Context 10, 262,144 tokens native. 20 + 10 + 20 + 0 + 0 + 10 = 60. - Kimi K3 (65): The chat template earns 10, because there is no Jinja template and the prompt format exists only as encoding_k3.py, code you run. The tool-calling format earns 10, since Moonshot documents the tools channel but the parser name comes only from vLLM's and SGLang's own recipes. Trained tool calling earns 20 for indexed parallel calls in one turn. Structured output earns 5, as the card names it among its linked guides without saying how to ask the weights for it, and the how sits in hosted-API docs. Reasoning control earns 10, with per-request effort documented on the card and reasoning returned as reasoning_content. Context earns 10 for 1,048,576 tokens trained in. ## Score by category | Category | Weight | Arete-27B | Kimi K3 | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 50 | 50 | even | | Risk | 20% (20 this run) | 55 | 65 | Kimi K3 +10 | | Agent interface | 10% (10 this run) | 60 | 65 | Kimi K3 +5 | | Documentation and upkeep | 10% (10 this run) | 63 | 41 | Arete-27B +22 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **55.1 · C** | **62.4 · B** | | ## Facts side by side | Fact | Arete-27B | Kimi K3 | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Nous Research | Moonshot AI | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | Apache-2.0 | Kimi K3 License (modified MIT) | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-11-06 | 2026-09-02 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | none | 2.3/5 (4) | ## Verdicts **Arete-27B.** Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits. **Kimi K3.** Kimi K3 is a 2.8T multimodal agent model with a 1M-token context, parallel tool calls and per-request effort, for long coding and research sessions. Its 1,561 GB of files need a multi-node GPU cluster, so most agents will call a hosted copy. The main risks are Moonshot's Python running at load and prompt time, and no answers in its repository. ## Before you call either ### Arete-27B 1. Start it with `llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja`, a third party's conversion, or serve the BF16 files with vLLM 2. Use the base's parsers in vLLM, `--reasoning-parser qwen3 --tool-call-parser qwen3_coder`, and check tool calls first, since the template differs slightly from Qwen3.8-27B's 3. Thinking switches with `enable_thinking` in the template. The card's own evaluation ran with it off 4. Expect answers on politically sensitive topics to differ from Qwen3.8-27B's. That is what the fine-tune is for 5. Validate JSON output yourself, since structured output isn't documented ### Kimi K3 1. Start it with SGLang on a node of 8 B300s, sglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 --context-length 65536 --mem-fraction-static 0.82 --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000 2. Use the kimi_k3 tool-call and reasoning parsers in vLLM or SGLang, and validate tool-call arguments against the schema, since vLLM's recipe says K3 sometimes emits a format its parser doesn't expect 3. Pass the whole assistant message back every turn, reasoning_content and tool_calls included, or output becomes unstable 4. Thinking is always on; set reasoning_effort to low or high when max is too slow 5. Sampling per the card is temperature 1.0, with top-p 0.95 for single-step tasks and 1.0 for agentic ones 6. Don't hand an ongoing session from another model to K3, and state boundaries in the system prompt, since Moonshot warns it can act on its own when intent is unclear 7. Without the hardware, call it through Moonshot's API or an inference provider listed on its Hugging Face page ## Questions ### Which is better for AI agents, Arete-27B or Kimi K3? Kimi K3 scores 62.4 (B) on agent readiness against Arete-27B's 55.1 (C), and leads in 2 of 4 scored categories. Arete-27B leads on documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/arete-27b-vs-kimi-k3.json, and with the fewest tokens: https://www.anchorterminal.com/compare/arete-27b-vs-kimi-k3.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "arete-27b", "b": "kimi-k3"}`. 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[Kimi K3 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/kimi-k3-vs-thinkingcap-qwen3-8-27b.md) - [Kimi K3 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/kimi-k3-vs-unlimited-ocr-2.md) - [Kimi K3 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/kimi-k3-vs-wevisone-vl-2b.md) - [Kimi K3 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/kimi-k3-vs-wevisone-vl-4b.md) - [Kimi K3 vs ZAYA1-8B](https://www.anchorterminal.com/compare/kimi-k3-vs-zaya1-8b.md) --- Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). How: https://www.anchorterminal.com/agents/#mcp