# Kimi K3.1 Pro vs Qwopus3.8-27B-Flash-V2 > Qwopus3.8-27B-Flash-V2 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/kimi-k3-1-pro-vs-qwopus3-8-27b-flash-v2 - Markdown: https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwopus3-8-27b-flash-v2.md (~9,050 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 Qwopus3.8-27B-Flash-V2 scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 1 of 4 scored categories. Kimi K3.1 Pro leads on agent interface. Both do open-weight models you run yourself. - 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 - 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 ### 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 Ahead on: - Agent interface, 65 against 60 Watch for: 1,561 GB of files, so it needs a node of eight large accelerators at least ### 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 40 Watch for: Published from a personal account, with no organisation, security contact or safety evaluation ## Why the edge ### Reach: Qwopus3.8-27B-Flash-V2 +45 - 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. - 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. ### Agent interface: Kimi K3.1 Pro +5 - 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. - 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. ## Score by category | Category | Weight | Kimi K3.1 Pro | Qwopus3.8-27B-Flash-V2 | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 40 | 85 | Qwopus3.8-27B-Flash-V2 +45 | | Risk | 20% (20 this run) | 50 | 50 | even | | Agent interface | 10% (10 this run) | 65 | 60 | Kimi K3.1 Pro +5 | | Documentation and upkeep | 10% (10 this run) | 51 | 51 | even | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **48.6 · D** | **61 · C** | | ## Facts side by side | Fact | Kimi K3.1 Pro | Qwopus3.8-27B-Flash-V2 | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Moonshot AI | Jackrong | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | Kimi K3.1 Pro License (modified MIT) | 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 | 2026-11-05 | 2026-09-21 | | Terms last updated | no document linked | 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 ### Kimi K3.1 Pro No score change since it was listed. ### Qwopus3.8-27B-Flash-V2 - no change on 3 November 2026: time ## Verdicts **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. **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 ### 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 ### 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, Kimi K3.1 Pro or Qwopus3.8-27B-Flash-V2? Qwopus3.8-27B-Flash-V2 scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 1 of 4 scored categories. Kimi K3.1 Pro leads on agent interface. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwopus3-8-27b-flash-v2.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwopus3-8-27b-flash-v2.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "kimi-k3-1-pro", "b": "qwopus3-8-27b-flash-v2"}`. 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