{
  "data": {
    "a": {
      "slug": "cola2",
      "name": "Cola2",
      "vendor": "ByteDance (Seed)",
      "vendorUrl": "https://github.com/ByteDance-Seed",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Cola2 is a research checkpoint from ByteDance Seed, a base language model of 8.8B parameters that generates text through continuous latents with a diffusion transformer and a token decoder. Not a chat model, and the weights carry no stated licence.",
      "url": "https://www.anchorterminal.com/tools/cola2",
      "markdownUrl": "https://www.anchorterminal.com/tools/cola2.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/cola2.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/cola2.json",
      "dns": "cola2.grade.anchorterminal.com",
      "repo": "https://huggingface.co/ByteDance-Seed/Cola2",
      "license": "None stated for the weights (the code is Apache 2.0)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "ByteDance-Seed/Cola2"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download, but nothing says what you may do with the weights. The card says the code is Apache 2.0 and that the weights remain subject to their respective terms, without naming them. Running it yourself costs the hardware: 35.1 GB of F32 files, loaded as BF16 in the card's example (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://github.com/ByteDance-Seed/Cola2",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "base-model",
        "research",
        "diffusion",
        "continuous-latent",
        "no-licence",
        "safetensors",
        "custom-code",
        "self-hosted"
      ],
      "lastRelease": "2026-11-06",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 33.9,
        "grade": "F",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 113,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 20,
          "reach": 25,
          "risk": 40,
          "upkeep": 58
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A research release, and an honest one: ByteDance Seed says it's a base model with an unconverged latent stage, scoring below Llama-3-8B on its own table. It's here for people studying continuous-latent generation. Agents can't use it as a model: 32-token completions, no chat or tools, the repository's own code to load it, and no licence on the weights.",
        "bestFor": "Researchers working on latent or diffusion language models who want a checkpoint to study",
        "strengths": [
          "A documented new architecture with a paper and a reproduction protocol",
          "Evaluation data and checksums ship with the weights",
          "A security policy that points to ByteDance's security centre",
          "The model card says plainly what it isn't for"
        ],
        "weaknesses": [
          "No licence stated for the weights",
          "A base model: no chat, no tools, 32 new tokens by default",
          "Loads only with the repository's own Python, on Transformers 4.x",
          "35 GB of F32 files and no quantised build",
          "Below Llama-3-8B on the card's own eight-task table",
          "No stated context length or cut-off"
        ],
        "agentNotes": [
          "Don't plan to use this as your model. It completes short prompts and wasn't tuned to follow instructions",
          "Don't use it commercially on the strength of this listing. The weights have no stated licence",
          "If you study it, read modeling_cola2.py first: trust_remote_code runs it",
          "Pin Transformers below 5, as the card requires",
          "Pass a seeded torch.Generator to generate(). Output varies between runs even with greedy decoding",
          "Check the download with `sha256sum -c SHA256SUMS`"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "F",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 33.9
          }
        ],
        "editorialScores": {
          "interface": 20,
          "reach": 25,
          "risk": 40,
          "upkeep": 58
        },
        "provenanceScore": 58,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "pip install \"torch\u003e=2.2\" \"transformers\u003e=4.51,\u003c5\" \"safetensors\u003e=0.4\" accelerate\n# then AutoModel.from_pretrained(\"ByteDance-Seed/Cola2\", trust_remote_code=True, torch_dtype=torch.bfloat16)\n# trust_remote_code runs the three Python files in the repository. Read them first",
        "http": "# No server. The model runs in process through Transformers, and the GitHub repository has a\n# command-line chat script (scripts/run_chat.sh) and a benchmark runner"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/cola2"
      },
      "weights": {
        "trainedBy": "ByteDance Seed",
        "builtOn": "The decoder and encoder embedding are initialised from Qwen3-4B-Base, per the GitHub model card",
        "params": "8.8B",
        "goodFor": "Research on continuous-latent text generation: short completions, cloze and multiple-choice experiments, and reproducing the paper's numbers.",
        "runtimes": [],
        "builds": [
          {
            "name": "Cola2 (F32 safetensors)",
            "url": "https://huggingface.co/ByteDance-Seed/Cola2/tree/main",
            "who": "publisher",
            "precision": "f32",
            "sizeGB": 35.07,
            "memoryGB": 48,
            "format": "safetensors",
            "note": "Eight shards in 32-bit floats. The card's example loads them as BF16"
          }
        ],
        "run": "python -c \"from transformers import AutoModel; AutoModel.from_pretrained('ByteDance-Seed/Cola2', trust_remote_code=True)\"",
        "runBuild": "Cola2 (F32 safetensors)",
        "checked": "2026-11-10",
        "minMemoryGB": 48
      },
      "family": {
        "lab": "ByteDance (Seed)",
        "family": "Cola",
        "url": "https://www.anchorterminal.com/open-weights/#bytedance-seed",
        "others": [
          {
            "name": "Cola-DLM",
            "url": "https://huggingface.co/ByteDance-Seed/Cola-DLM"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "ByteDance, named in the GitHub repository's security policy. The legal entity wasn't looked up",
        "domain": "bytedance.com",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/ByteDance-Seed/Cola2/commits/main",
        "securityTxt": "unknown",
        "checked": "2026-11-10",
        "notes": [
          "The files are in the ByteDance-Seed organisation on Hugging Face and the code is in the ByteDance-Seed organisation on GitHub; each links the other.",
          "No licence is stated for the weights, so there are no terms to read.",
          "The domain's registration date and security.txt weren't looked up. The GitHub repository's SECURITY.md points to ByteDance's security centre.",
          "The changelog is the commit history, six commits on 6 November 2026."
        ],
        "score": 58
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/cola2.json"
    },
    "answer": "Kimi K3.1 Pro scores 48.6 (D) on agent readiness against Cola2's 33.9 (F), and leads in 3 of 4 scored categories. Cola2 leads on documentation and upkeep.",
    "b": {
      "slug": "kimi-k3-1-pro",
      "name": "Kimi K3.1 Pro",
      "vendor": "Moonshot AI",
      "vendorUrl": "https://www.moonshot.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Kimi K3.1 Pro is Moonshot AI's open-weight multimodal mixture-of-experts model, post-trained from Kimi K3, with 2.8T parameters, 104B active and a 1M-token context, published in MXFP4 under the modified-MIT Kimi K3.1 Pro Licence.",
      "url": "https://www.anchorterminal.com/tools/kimi-k3-1-pro",
      "markdownUrl": "https://www.anchorterminal.com/tools/kimi-k3-1-pro.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/kimi-k3-1-pro.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/kimi-k3-1-pro.json",
      "dns": "kimi-k3-1-pro.grade.anchorterminal.com",
      "repo": "https://huggingface.co/moonshotai/Kimi-K3.1-Pro",
      "license": "Kimi K3.1 Pro License (modified MIT)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "moonshotai/Kimi-K3.1-Pro"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the Kimi K3.1 Pro Licence. A company running a model-as-a-service business with more than $20 million in revenue over 12 months needs a separate agreement with Moonshot AI, at a price not published. Running it yourself costs the hardware, 1,561 GB of files (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://huggingface.co/moonshotai/Kimi-K3.1-Pro",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "modified-mit",
        "safetensors",
        "mxfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-11-05",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 48.6,
        "grade": "D",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 109,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 65,
          "reach": 40,
          "risk": 50,
          "upkeep": 51
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "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.",
        "bestFor": "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",
        "strengths": [
          "Published weights for a model Moonshot calls its most capable, with a 1M-token context and images in",
          "Free commercial use under a modified MIT licence, ungated, with a hash on every file",
          "Moonshot's API and Together serve the same model for agents without the hardware",
          "reasoning_effort per request and several tool calls a turn",
          "vLLM and SGLang both publish serving recipes for it"
        ],
        "weaknesses": [
          "1,561 GB of files, so it needs a node of eight large accelerators at least",
          "Only llama.cpp has the model code in a release, and no GGUF is published",
          "No chat template, and prompts are built by code from the repository",
          "No limitations, safety guidance or security contact on the card, and no independent safety evaluation yet",
          "None of the newest discussions has a reply from Moonshot"
        ],
        "agentNotes": [
          "Use Moonshot's API or Together unless you have a node of eight B300-class accelerators",
          "With vLLM use 0.34.0 or later and the recipe's flags, `--tool-call-parser kimi_k3 --reasoning-parser kimi_k3`",
          "Pass the whole assistant message back in later turns, reasoning_content and tool_calls included, as the card requires",
          "Set `reasoning_effort` to low, high or max per request. Max is the default and thinking can't be turned off",
          "Kimi K3's model code doesn't load it, because the attention layers changed",
          "Validate JSON output yourself, since the card doesn't document structured output for the weights"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "D",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 48.6
          }
        ],
        "editorialScores": {
          "interface": 65,
          "reach": 40,
          "risk": 50,
          "upkeep": 51
        },
        "provenanceScore": 70,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# vLLM, per its Kimi-K3.1-Pro recipe linked from the card (vLLM 0.34.0 or later, two GB300 trays)\nvllm serve moonshotai/Kimi-K3.1-Pro --trust-remote-code \\\n  --tensor-parallel-size 8 --nnodes 2 --node-rank 0 --master-addr $HEAD_IP \\\n  --load-format fastsafetensors --enable-auto-tool-choice \\\n  --tool-call-parser kimi_k3 --reasoning-parser kimi_k3",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"model\": \"moonshotai/Kimi-K3.1-Pro\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n    \"reasoning_effort\": \"high\"\n  }'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/kimi-k3-1-pro"
      },
      "sameCompany": [
        "kimi-k3"
      ],
      "weights": {
        "trainedBy": "Moonshot AI",
        "builtOn": "Kimi K3, post-trained, per the card. Not declared in the metadata",
        "params": "2.8T",
        "active": "104B",
        "context": 1048576,
        "goodFor": "Large multimodal agent model for coding, research and long tool-calling sessions, text and images in, with always-on reasoning you can budget.",
        "runtimes": [
          "llama.cpp"
        ],
        "builds": [
          {
            "name": "Kimi-K3.1-Pro",
            "url": "https://huggingface.co/moonshotai/Kimi-K3.1-Pro",
            "who": "publisher",
            "precision": "mxfp4",
            "sizeGB": 1561.3,
            "memoryGB": 2048,
            "format": "safetensors",
            "note": "Trained in MXFP4, the only build Moonshot publishes. vLLM's recipe needs 0.34.0 or later"
          }
        ],
        "run": "vllm serve moonshotai/Kimi-K3.1-Pro --trust-remote-code --tensor-parallel-size 8 --nnodes 2 --node-rank 0 --master-addr $HEAD_IP --load-format fastsafetensors --enable-auto-tool-choice --tool-call-parser kimi_k3 --reasoning-parser kimi_k3",
        "runBuild": "Kimi-K3.1-Pro",
        "checked": "2026-11-10",
        "minMemoryGB": 2048
      },
      "family": {
        "lab": "Moonshot AI (Kimi)",
        "family": "Kimi K3.1",
        "url": "https://www.anchorterminal.com/open-weights/#moonshot",
        "olderCount": 18
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Moonshot AI (the licence names Moonshot AI; the Kimi Platform terms name Moonshot AI PTE. LTD.)",
        "domain": "moonshot.ai",
        "domainRegistered": "2017-12-15",
        "domainNote": "The .ai registry's RDAP server gave moonshot.ai a registration date of 15 December 2017, read 24 October 2026 for the Kimi K3 listing.",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/moonshotai/Kimi-K3.1-Pro/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "The licence names Moonshot AI. The Kimi Platform's terms name Moonshot AI PTE. LTD. as the party for its API, as the Kimi K3 listing records.",
          "Nothing is hosted, so the Kimi K3.1 Pro Licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history, two commits, the first on 2 November 2026 and an update to encoding_k3.py on 5 November.",
          "No security.txt was found on moonshot.ai or kimi.com for the Kimi K3 listing on 24 October 2026. unchecked on 10 November."
        ],
        "score": 70
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/kimi-k3-1-pro.json"
    },
    "edges": [
      {
        "by": 45,
        "category": "interface",
        "leads": "kimi-k3-1-pro",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, A chat template in tokenizer_config.json (4,116 characters), which came with the Qwen3 tokenizer. Tool-calling format 0, none. Trained tool calling 0, not trained for tool calling. Structured output 0, Nothing about JSON or schema-constrained output. Reasoning control 0, no reasoning mode. Context 0, no context length is stated. 20 + 0 + 0 + 0 + 0 + 0 = 20.",
          "b": "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."
        },
        "scores": {
          "a": 20,
          "b": 65
        }
      },
      {
        "by": 15,
        "category": "reach",
        "leads": "kimi-k3-1-pro",
        "name": "Reach",
        "reasons": {
          "a": "Memory 15, the only build is the F32 safetensors set, 35.07 GB, and 35.07 GB × 1.2 = 42.1 GB, within 64 GB. Loaded as BF16, as the card's example does, it needs about half, but the line reads the file size. Runtimes 0, none of the six runs it in a released version. Builds 0, one set of weights and no quantised build. Terms 0, no licence for the weights. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and nothing names an API that serves it. 15 + 0 + 0 + 0 + 10 + 0 = 25.",
          "b": "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."
        },
        "scores": {
          "a": 25,
          "b": 40
        }
      },
      {
        "by": 10,
        "category": "risk",
        "leads": "kimi-k3-1-pro",
        "name": "Risk",
        "reasons": {
          "a": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the ByteDance-Seed organisation, which the ByteDance-Seed GitHub repository for the model links to (10). Safety evaluation 0, no safety results. Agent guidance 5, General cautions in the GitHub model card: not instruction-tuned or safety-aligned, not for use as an assistant, for facts or advice, or for high-impact decisions about people. Licence risk 0, With no licence for the weights there is nothing to hold ByteDance to. Security programme 5, the GitHub repository's SECURITY.md sends reports to ByteDance's security centre and says how they're assessed (5). 15 + 15 + 0 + 5 + 0 + 5 = 40.",
          "b": "File safety 15, safetensors only, less 10 for remote code and prompts built by the repository's script. Provenance 15, Moonshot's own organisation with a hash on every file. Safety evaluation 10, Moonshot's own cyber benchmark results and nobody else's yet. Agent guidance 0, none on the card. Licence risk 10, the revenue threshold. Security programme 0, no way to report a flaw found. 15 + 15 + 10 + 0 + 10 + 0 = 50."
        },
        "scores": {
          "a": 40,
          "b": 50
        }
      },
      {
        "by": 7,
        "category": "upkeep",
        "leads": "cola2",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 8, Intended use and limitations (the out-of-scope list, and the unconverged latent model) and the architecture are on the card and the GitHub model card it links. Training data 15, the GitHub model card names the source (OLMo 2 pretraining text, retokenised) and the mix by stage with token counts, 346.03B in all. Last change 20, All six commits are from 6 November 2026, four days before this check, the last one updating a checksum in the README. Responsiveness 0, no discussions on the repository, so nobody has asked anything yet and the line reads as none answered, as settled in calibration. Version policy 5, A first release. File health 10, Config, files and card agree: F32 weights, a float32 config and a card that says to load them as bfloat16, with SHA256SUMS to check the download. 8 + 15 + 20 + 0 + 5 + 10 = 58.",
          "b": "Model card 11, no limitations, languages or cut-off. Training data 5, one general sentence. Last change 20, a commit on 5 November 2026. Responsiveness 0, none of the 8 newest discussions has a reply from Moonshot. Version policy 5, Kimi K3 keeps its repository and no dated note was found. File health 10, the files agree. 11 + 5 + 20 + 0 + 5 + 10 = 51."
        },
        "scores": {
          "a": 58,
          "b": 51
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "ByteDance (Seed)",
        "b": "Moonshot AI",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "",
        "b": "",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "None",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Free",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "None stated for the weights (the code is Apache 2.0)",
        "b": "Kimi K3.1 Pro License (modified MIT)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-11-06",
        "b": "2026-11-05",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      }
    ],
    "faq": [
      {
        "answer": "Kimi K3.1 Pro scores 48.6 (D) on agent readiness against Cola2's 33.9 (F), and leads in 3 of 4 scored categories. Cola2 leads on documentation and upkeep.",
        "question": "Which is better for AI agents, Cola2 or Kimi K3.1 Pro?"
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        "aheadOn": [
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        "also": null,
        "goodFor": "Researchers working on latent or diffusion language models who want a checkpoint to study",
        "slug": "cola2",
        "watchFor": "No licence stated for the weights"
      },
      {
        "aheadOn": [
          "Reach, 40 against 25",
          "Risk, 50 against 40",
          "Agent interface, 65 against 20"
        ],
        "also": null,
        "goodFor": "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",
        "slug": "kimi-k3-1-pro",
        "watchFor": "1,561 GB of files, so it needs a node of eight large accelerators at least"
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    "history": null,
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        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kimi-k3-1-pro.json",
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        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3-1-pro.json",
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        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kimi-k3-1-pro.json",
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-kimi-k3-1-pro.json",
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        "json": "https://www.anchorterminal.com/compare/cola2-vs-cosmos-reason2-2b.json",
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        "json": "https://www.anchorterminal.com/compare/cola2-vs-darwin-36b-opus.json",
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        "json": "https://www.anchorterminal.com/compare/cola2-vs-deepseek-v3-2.json",
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        "json": "https://www.anchorterminal.com/compare/cola2-vs-deepseek-v4-1-flash.json",
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-deepseek-v4-flash-0731.json",
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        "json": "https://www.anchorterminal.com/compare/cola2-vs-devstral-small-2.json",
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        "json": "https://www.anchorterminal.com/compare/cola2-vs-diffusiongemma-26b-a4b-it.json",
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        "json": "https://www.anchorterminal.com/compare/cola2-vs-exaone-4-5-33b.json",
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        "json": "https://www.anchorterminal.com/compare/cola2-vs-gemma-4-12b-it.json",
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        "json": "https://www.anchorterminal.com/compare/cola2-vs-gemma-4-26b-a4b-it.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-gemma-4-26b-a4b-it"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-gemma-4-31b-it.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-gemma-4-31b-it"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-gemma-4-e2b-it.json",
        "title": "Cola2 vs Gemma 4 E2B IT",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-gemma-4-e2b-it"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-gemma-4-e4b-it.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-gemma-4-e4b-it"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-glm-4-5-air.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-glm-4-5-air"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-glm-4-7-flash.json",
        "title": "Cola2 vs GLM-4.7-Flash",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-glm-4-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-glm-5-3.json",
        "title": "Cola2 vs GLM-5.3",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-glm-5-3"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-glm-5-3-flash.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-glm-5-3-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-gpt-oss-120b.json",
        "title": "Cola2 vs gpt-oss-120b",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-gpt-oss-120b"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-gpt-oss-20b.json",
        "title": "Cola2 vs gpt-oss-20b",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-gpt-oss-20b"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-granite-4-2-30b.json",
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        "json": "https://www.anchorterminal.com/compare/cola2-vs-granite-4-2-3b.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-granite-4-2-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-granite-4-2-8b.json",
        "title": "Cola2 vs Granite 4.2 8B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-granite-4-2-8b"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-hy-memory-30b-a3b.json",
        "title": "Cola2 vs Hy-Memory-30B-A3B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-hy-memory-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-hy3.json",
        "title": "Cola2 vs Hy3",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-hy3"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-inkling.json",
        "title": "Cola2 vs Inkling",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-inkling"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-inkling-small.json",
        "title": "Cola2 vs Inkling-Small",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-inkling-small"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-internvl3-5-gpt-oss-20b-a4b-preview.json",
        "title": "Cola2 vs InternVL3.5-GPT-OSS-20B-A4B-Preview",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-internvl3-5-gpt-oss-20b-a4b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-kimi-k3.json",
        "title": "Cola2 vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-kimi-k3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-kolibri-1.json",
        "title": "Cola2 vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-kolibri-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-kronos.json",
        "title": "Cola2 vs Kronos",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-kronos"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-laguna-m-1.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-laguna-m-1"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-laguna-s-2-1.json",
        "title": "Cola2 vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-laguna-s-2-1"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-laguna-xs-2-1.json",
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-lbs-27b.json",
        "title": "Cola2 vs LBS-27B",
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-lfm2-5-2-6b.json",
        "title": "Cola2 vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-lfm3-1-7b.json",
        "title": "Cola2 vs LFM3-1.7B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-lfm3-1-7b"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-lfm3-audio-2b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-lfm3-omni-2b.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-lfm3-omni-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-lfm3-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-lfm3-vl-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-ling-3-0-tiny.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-ling-3-0-tiny"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-ling-3-1-flash-1019.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-ling-3-1-flash-1019"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-longcat-2-5.json",
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-mimo-v2-6-distill-qwen-9b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-mimo-v2-6-flash-rl.json",
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-minicpm-o-4-5.json",
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-minicpm-v-4-7-1b.json",
        "title": "Cola2 vs MiniCPM-V 4.7 1B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-minicpm-v-4-7-1b"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "Cola2 vs MiniCPM-V 4.7 35B-A3B",
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        "json": "https://www.anchorterminal.com/compare/cola2-vs-minicpm5-2b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-minimax-m2-5.json",
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-minimax-m2-7.json",
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-minimax-m3-1-flash.json",
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-mistral-large-4.json",
        "title": "Cola2 vs Mistral Large 4",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-mistral-large-4"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-muse-glimmer-30b.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-muse-glimmer-30b"
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        "json": "https://www.anchorterminal.com/compare/cola2-vs-nanbeige-4-2-3b.json",
        "title": "Cola2 vs Nanbeige4.2-3B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-nanbeige-4-2-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-nanbeige-4-3-33b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-5-bolt-e4b-4gb.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-5-bolt-e4b-4gb"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-5-lightning-30b-a3b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-nano-30b-a3b.json",
        "title": "Cola2 vs Nemotron 3 Nano 30B A3B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-nano-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-nano-4b.json",
        "title": "Cola2 vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-nano-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
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        "json": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-super-120b-a12b.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-super-120b-a12b"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-ultra-550b-a55b.json",
        "title": "Cola2 vs Nemotron 3 Ultra 550B A55B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-ultra-550b-a55b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-north-mini-code-1-0.json",
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-occamy-1-0.json",
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      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-olmo-3-7b-think.json",
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-mimo-v2-6-distill-qwen-9b.json",
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minicpm-v-4-7-35b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-zaya1-8b.json",
        "title": "Kimi K3.1 Pro vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-kimi-k3-1-pro.json",
        "title": "Kimi K3 vs Kimi K3.1 Pro",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-kimi-k3-1-pro"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 15,
        "cola2": 25,
        "edge": "kimi-k3-1-pro",
        "key": "reach",
        "kimi-k3-1-pro": 40,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 10,
        "cola2": 40,
        "edge": "kimi-k3-1-pro",
        "key": "risk",
        "kimi-k3-1-pro": 50,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 45,
        "cola2": 20,
        "edge": "kimi-k3-1-pro",
        "key": "interface",
        "kimi-k3-1-pro": 65,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 7,
        "cola2": 58,
        "edge": "cola2",
        "key": "upkeep",
        "kimi-k3-1-pro": 51,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Kimi K3.1 Pro scores 48.6 (D) on agent readiness against Cola2's 33.9 (F), and leads in 3 of 4 scored categories. Cola2 leads on documentation and upkeep. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "cola2": "A research release, and an honest one: ByteDance Seed says it's a base model with an unconverged latent stage, scoring below Llama-3-8B on its own table. It's here for people studying continuous-latent generation. Agents can't use it as a model: 32-token completions, no chat or tools, the repository's own code to load it, and no licence on the weights.",
      "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."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/cola2-vs-kimi-k3-1-pro",
    "json": "https://www.anchorterminal.com/compare/cola2-vs-kimi-k3-1-pro.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/cola2-vs-kimi-k3-1-pro.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/cola2-vs-kimi-k3-1-pro.min.md"
  },
  "markdown": "Kimi K3.1 Pro scores 48.6 (D) on agent readiness against Cola2's 33.9 (F), and leads in 3 of 4 scored categories. Cola2 leads on documentation and upkeep. Both do open-weight models you run yourself.\n\n- Cola2: grade F, 33.9/100, rank #160 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/cola2.md · JSON https://www.anchorterminal.com/api/v1/tools/cola2.json\n- 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\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Cola2 (F)\n\nGood for: Researchers working on latent or diffusion language models who want a checkpoint to study\n\nAhead on:\n- Documentation and upkeep, 58 against 51\n\nWatch for: No licence stated for the weights\n\n### Kimi K3.1 Pro (D)\n\nGood 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\n\nAhead on:\n- Reach, 40 against 25\n- Risk, 50 against 40\n- Agent interface, 65 against 20\n\nWatch for: 1,561 GB of files, so it needs a node of eight large accelerators at least\n\n\n## Why the edge\n\n### Agent interface: Kimi K3.1 Pro +45\n\n- Cola2 (20): Chat template 20, A chat template in tokenizer_config.json (4,116 characters), which came with the Qwen3 tokenizer. Tool-calling format 0, none. Trained tool calling 0, not trained for tool calling. Structured output 0, Nothing about JSON or schema-constrained output. Reasoning control 0, no reasoning mode. Context 0, no context length is stated. 20 + 0 + 0 + 0 + 0 + 0 = 20.\n- 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.\n\n### Reach: Kimi K3.1 Pro +15\n\n- Cola2 (25): Memory 15, the only build is the F32 safetensors set, 35.07 GB, and 35.07 GB × 1.2 = 42.1 GB, within 64 GB. Loaded as BF16, as the card's example does, it needs about half, but the line reads the file size. Runtimes 0, none of the six runs it in a released version. Builds 0, one set of weights and no quantised build. Terms 0, no licence for the weights. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and nothing names an API that serves it. 15 + 0 + 0 + 0 + 10 + 0 = 25.\n- 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.\n\n### Risk: Kimi K3.1 Pro +10\n\n- Cola2 (40): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the ByteDance-Seed organisation, which the ByteDance-Seed GitHub repository for the model links to (10). Safety evaluation 0, no safety results. Agent guidance 5, General cautions in the GitHub model card: not instruction-tuned or safety-aligned, not for use as an assistant, for facts or advice, or for high-impact decisions about people. Licence risk 0, With no licence for the weights there is nothing to hold ByteDance to. Security programme 5, the GitHub repository's SECURITY.md sends reports to ByteDance's security centre and says how they're assessed (5). 15 + 15 + 0 + 5 + 0 + 5 = 40.\n- Kimi K3.1 Pro (50): File safety 15, safetensors only, less 10 for remote code and prompts built by the repository's script. Provenance 15, Moonshot's own organisation with a hash on every file. Safety evaluation 10, Moonshot's own cyber benchmark results and nobody else's yet. Agent guidance 0, none on the card. Licence risk 10, the revenue threshold. Security programme 0, no way to report a flaw found. 15 + 15 + 10 + 0 + 10 + 0 = 50.\n\n### Documentation and upkeep: Cola2 +7\n\n- Cola2 (58): Model card 8, Intended use and limitations (the out-of-scope list, and the unconverged latent model) and the architecture are on the card and the GitHub model card it links. Training data 15, the GitHub model card names the source (OLMo 2 pretraining text, retokenised) and the mix by stage with token counts, 346.03B in all. Last change 20, All six commits are from 6 November 2026, four days before this check, the last one updating a checksum in the README. Responsiveness 0, no discussions on the repository, so nobody has asked anything yet and the line reads as none answered, as settled in calibration. Version policy 5, A first release. File health 10, Config, files and card agree: F32 weights, a float32 config and a card that says to load them as bfloat16, with SHA256SUMS to check the download. 8 + 15 + 20 + 0 + 5 + 10 = 58.\n- Kimi K3.1 Pro (51): Model card 11, no limitations, languages or cut-off. Training data 5, one general sentence. Last change 20, a commit on 5 November 2026. Responsiveness 0, none of the 8 newest discussions has a reply from Moonshot. Version policy 5, Kimi K3 keeps its repository and no dated note was found. File health 10, the files agree. 11 + 5 + 20 + 0 + 5 + 10 = 51.\n\n## Score by category\n\n| Category | Weight | Cola2 | Kimi K3.1 Pro | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 25 | 40 | Kimi K3.1 Pro +15 |\n| Risk | 20% (20 this run) | 40 | 50 | Kimi K3.1 Pro +10 |\n| Agent interface | 10% (10 this run) | 20 | 65 | Kimi K3.1 Pro +45 |\n| Documentation and upkeep | 10% (10 this run) | 58 | 51 | Cola2 +7 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **33.9 · F** | **48.6 · D** | |\n\n## Facts side by side\n\n| Fact | Cola2 | Kimi K3.1 Pro |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | ByteDance (Seed) | Moonshot AI |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports |  |  |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | None stated for the weights (the code is Apache 2.0) | Kimi K3.1 Pro License (modified MIT) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-11-06 | 2026-11-05 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n\n## Verdicts\n\n**Cola2.** A research release, and an honest one: ByteDance Seed says it's a base model with an unconverged latent stage, scoring below Llama-3-8B on its own table. It's here for people studying continuous-latent generation. Agents can't use it as a model: 32-token completions, no chat or tools, the repository's own code to load it, and no licence on the weights.\n\n**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.\n\n## Before you call either\n\n### Cola2\n\n1. Don't plan to use this as your model. It completes short prompts and wasn't tuned to follow instructions\n2. Don't use it commercially on the strength of this listing. The weights have no stated licence\n3. If you study it, read modeling_cola2.py first: trust_remote_code runs it\n4. Pin Transformers below 5, as the card requires\n5. Pass a seeded torch.Generator to generate(). Output varies between runs even with greedy decoding\n6. Check the download with `sha256sum -c SHA256SUMS`\n\n### Kimi K3.1 Pro\n\n1. Use Moonshot's API or Together unless you have a node of eight B300-class accelerators\n2. With vLLM use 0.34.0 or later and the recipe's flags, `--tool-call-parser kimi_k3 --reasoning-parser kimi_k3`\n3. Pass the whole assistant message back in later turns, reasoning_content and tool_calls included, as the card requires\n4. Set `reasoning_effort` to low, high or max per request. Max is the default and thinking can't be turned off\n5. Kimi K3's model code doesn't load it, because the attention layers changed\n6. Validate JSON output yourself, since the card doesn't document structured output for the weights\n\n## Questions\n\n### Which is better for AI agents, Cola2 or Kimi K3.1 Pro?\n\nKimi K3.1 Pro scores 48.6 (D) on agent readiness against Cola2's 33.9 (F), and leads in 3 of 4 scored categories. Cola2 leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/cola2-vs-kimi-k3-1-pro.json, and with the fewest tokens: https://www.anchorterminal.com/compare/cola2-vs-kimi-k3-1-pro.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"cola2\", \"b\": \"kimi-k3-1-pro\"}`. From a terminal: `anchor compare cola2 kimi-k3-1-pro`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/cola2.json and https://www.anchorterminal.com/api/v1/tools/kimi-k3-1-pro.json\n\n## Other comparisons with Cola2 or Kimi K3.1 Pro\n\n- [Agents-A1-4B vs Cola2](https://www.anchorterminal.com/compare/agents-a1-4b-vs-cola2.md)\n- [Agents-A1-4B vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/agents-a1-4b-vs-kimi-k3-1-pro.md)\n- [Apertus v1.5 8B vs Cola2](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-cola2.md)\n- [Apertus v1.5 8B vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3-1-pro.md)\n- [Apodex-1.1-mini vs Cola2](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-cola2.md)\n- [Apodex-1.1-mini vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kimi-k3-1-pro.md)\n- [Arete-27B vs Cola2](https://www.anchorterminal.com/compare/arete-27b-vs-cola2.md)\n- [Arete-27B vs Kimi K3.1 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