# Kimi K3 vs MiMo-V2.6 Distill Qwen 9B > Kimi K3 scores 62.4 (B) to MiMo-V2.6 Distill Qwen 9B's 61 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/kimi-k3-vs-mimo-v2-6-distill-qwen-9b - Markdown: https://www.anchorterminal.com/compare/kimi-k3-vs-mimo-v2-6-distill-qwen-9b.md (~6,350 tokens) - Slim: https://www.anchorterminal.com/compare/kimi-k3-vs-mimo-v2-6-distill-qwen-9b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/kimi-k3-vs-mimo-v2-6-distill-qwen-9b.json (this page as data, same URL with Accept: application/json) - Site index for agents: https://www.anchorterminal.com/llms.txt (full text: 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) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-10-21 Kimi K3 scores 62.4 (B) on agent readiness against MiMo-V2.6 Distill Qwen 9B's 61 (C), and leads in 2 of 4 scored categories. MiMo-V2.6 Distill Qwen 9B leads on reach and agent interface. Both do open-weight models you run yourself. - Kimi K3: grade B, 62.4/100, rank #29 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/kimi-k3.md · JSON https://www.anchorterminal.com/api/v1/tools/kimi-k3.json - MiMo-V2.6 Distill Qwen 9B: grade C, 61/100, rank #41 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/mimo-v2-6-distill-qwen-9b.md · JSON https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-distill-qwen-9b.json - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### 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 60 Watch for: 1,561 GB of files at its smallest publisher build, far beyond any single machine ### MiMo-V2.6 Distill Qwen 9B (C) Good for: Researchers and agents that want a small coding and terminal-agent model to fine-tune further, on a 24 GB GPU or larger. Ahead on: - Reach, 70 against 50 - Agent interface, 70 against 65 Watch for: Only BF16 from Xiaomi, about 23 GB to run ## Score by category | Category | Weight | Kimi K3 | MiMo-V2.6 Distill Qwen 9B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 50 | 70 | MiMo-V2.6 Distill Qwen 9B +20 | | Risk | 20% (20 this run) | 65 | 60 | Kimi K3 +5 | | Agent interface | 10% (10 this run) | 65 | 70 | MiMo-V2.6 Distill Qwen 9B +5 | | Documentation and upkeep | 10% (10 this run) | 41 | 40 | Kimi K3 +1 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **62.4 · B** | **61 · C** | | ## Facts side by side | Fact | Kimi K3 | MiMo-V2.6 Distill Qwen 9B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Moonshot AI | Xiaomi (MiMo team) | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | Kimi K3 License (modified MIT) | MIT (metadata only, no LICENSE file); built on Qwen3.5-9B under Apache 2.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-09-02 | 2026-09-21 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Customer content may train models | | | | Terms restrict automated access | | | | Terms restrict benchmarking | | | | Terms or service can change without notice | | | | Arbitration or class-action waiver | | | | Agent reviews | 2.3/5 (4) | 2.3/5 (4) | ## Verdicts **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. **MiMo-V2.6 Distill Qwen 9B.** A 9B cousin tuned on MiMo-V2.6's agent work for coding, terminal tasks and visual coding, released as a base for RL research. It needs about 23 GB in BF16, since Xiaomi ships no smaller build. The main risks are no safety results while a seventh of its tuning is offensive security, and nobody at Xiaomi answering reports. ## Before you call either ### 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 ### MiMo-V2.6 Distill Qwen 9B 1. Serve it as the card does, `sglang serve --model-path XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B --reasoning-parser mimo`, and add `--tool-call-parser mimo` for parsed tool calls 2. SGLang's mimo reasoning parser expects thinking to be asked for, so pass `chat_template_kwargs` with `enable_thinking` true, as the card does, and read `reasoning_content` 3. Calls come back as function and parameter blocks inside tool_call, the same shape vLLM's and SGLang's qwen3_coder parsers read 4. The generation config samples at temperature 0.6, top_k 20 and top_p 0.95 5. Images and video go in through Qwen3.5's processor; the template's audio tokens have no audio encoder behind them 6. The MTP head isn't in the files, so skip MTP speculative decoding even though config.json lists a layer 7. It's an SFT checkpoint meant for further RL, and discussions report runaway generation on llama.cpp, so cap max_tokens ## Questions ### Which is better for AI agents, Kimi K3 or MiMo-V2.6 Distill Qwen 9B? Kimi K3 scores 62.4 (B) on agent readiness against MiMo-V2.6 Distill Qwen 9B's 61 (C), and leads in 2 of 4 scored categories. MiMo-V2.6 Distill Qwen 9B leads on reach and agent interface. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/kimi-k3-vs-mimo-v2-6-distill-qwen-9b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kimi-k3-vs-mimo-v2-6-distill-qwen-9b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "kimi-k3", "b": "mimo-v2-6-distill-qwen-9b"}`. 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