{
  "fixes": {
    "slug": "minimax-m2-5",
    "name": "MiniMax-M2.5",
    "listing": "https://www.anchorterminal.com/tools/minimax-m2-5",
    "markdown": "# Fix list: MiniMax-M2.5\n\nFrom Anchor Terminal's listing at https://www.anchorterminal.com/tools/minimax-m2-5, the October 2026 research run, assessed 21 October 2026. Grade C, 57.5 out of 100.\n\nThis is everything the published grade says the listing lacks, the biggest possible gain to the total first. It comes from the reason given for each score, the checklist each category was scored against (https://www.anchorterminal.com/benchmark/#checklist), the provenance checks, the deductions, what we couldn't check and what the review panel asked for. A fix counts at the next check, once it's public.\n\nFor a coding agent working on MiniMax-M2.5: work through the items below in the product, its docs and its public pages. Each category gives the reason for its score, with the points each checklist item earned, and the checklist itself, so the gap is the items that earned less than their points. Change the product, not the wording, and keep a note of what you changed and where it's published.\n\n## 1. Risk, 30 out of 100, up to 14 more on the total\n\nWhy it scored 30: File safety earns 15, safetensors only with no pickle files, less 10 because every documented loading route passes trust_remote_code for the repository's Python. Provenance earns the full 15, files in the organisation MiniMax links to with a SHA-256 for each shard. Safety evaluation and agent guidance earn nothing, as MiniMax publishes neither. Licence risk earns nothing (read 21 October 2026). The Prohibited Uses Policy goes beyond the law, MiniMax can end the licence for any breach and replaced its terms three weeks after release, and that change on 6 March 2026 came with no dated note while the card still links to a deleted file. The security programme earns nothing, with no security.txt, disclosure policy, SECURITY.md or advisories, and only a general contact address on the card.\n\nThe checklist (https://www.anchorterminal.com/benchmark/#checklist-risk):\n\nWhat could go wrong when you run it, and what the publisher has done about it. A higher score means less risk.\n\n- 0 to 25, file safety. 25 for weights in safetensors or GGUF only, 15 when pickle files (.bin, .pt, .pth) sit beside them, 0 for pickle only. 10 off when the documented way to load it or to build its prompts runs code from the repository (`trust_remote_code`, or the publisher's scripts in place of a template).\n- 0 to 15, provenance. 10 when the files come from the publisher's own organisation, the one its site or docs link to, and 5 for a checksum on every file.\n- 0 to 20, safety evaluation. 20 when results are published and someone other than the publisher ran them or can rerun them, 10 for the publisher's own results, 0 for none.\n- 0 to 15, guidance for agent use. 15 for documented prompt-injection guidance or an instruction hierarchy the model was trained to follow, 10 for a statement of what its trained refusals cover and the safeguards to add around it, 5 for general cautions, 0 for none.\n- 0 to 15, licence risk. 15 to start, 5 off for each of these, a use policy or field-of-use limits beyond the law, terms the licensor can change or revoke, a revenue or user threshold, and a licence that changed without a dated note or whose link is broken. Not below 0.\n- 0 to 10, a security programme. 5 for a way to report a flaw (security.txt, a disclosure policy or a security contact), 5 for advisories handled in public.\n\nWhen our reference-machine runs start, measured robustness (prompt injection with AgentDojo, over-refusal with XSTest) joins Risk.\n\n## 2. Capability, 73 out of 100, up to 9.5 more on the total\n\nWhy it scored 73: From 16 independent benchmark results in 5 areas, each scored as its share of the way from the benchmark's floor to the best result any model has on the same leaderboard. By area, Coding 89, Instruction following 86, Long context 83, Tool calling and agent tasks 65, Reasoning and knowledge 55. Publisher-submitted results count at half weight, and numbers that appear only on the publisher's card don't count.\n\nThe checklist (https://www.anchorterminal.com/benchmark/#checklist-capability):\n\nHow well the model does on the benchmarks that matter to an agent. We don't average a publisher's own numbers into a grade. A result counts in full when we ran it on our reference machine, or when an independent party ran it and published the method (the Berkeley Function Calling Leaderboard, Artificial Analysis, Epoch AI's Benchmarking Hub, the SWE-bench and Terminal-Bench leaderboards, the Open ASR Leaderboard and others that run models themselves). A result the publisher submitted to such a leaderboard counts at half weight. A number that appears only on the publisher's card is shown on the listing as the publisher's claim and doesn't count.\n\nEach result is scored as its share of the way from the benchmark's floor (chance for multiple choice, zero for a pass rate) to the best result any model, open or closed, has on the same leaderboard and setting on the day we read it. A model that matches the best model anywhere scores 100 on that benchmark. For an error rate such as word error rate, lower is better and the scale runs the other way, and where the leaderboard gives no floor the rate is read against the best model's, so twice the best model's error rate scores 50. Speed as a multiple of real time (RTFx) runs from about 1 to over 20,000, so it's read on a log scale from real time to the fastest model. On the MTEB leaderboard, where most models score between 0.5 and 0.75, the floor is the median of every model the same tab lists, and its results count at half weight because MTEB doesn't say who ran them and most are submitted with the model. An arena rating (Elo) isn't a ratio, so its floor is 400 points below the best model on that arena, the gap at which the better model wins about ten games in eleven. Where a model has results at several settings (reasoning effort, say), its best one counts, because that's the setting an agent would run.\n\nThe results group into areas, each weighted within the category.\n\n- Tool calling and agent tasks, 35 (BFCL, τ²-bench, Terminal-Bench and agent suites like them)\n- Coding, 20 (SWE-bench Verified, LiveCodeBench, Aider Polyglot)\n- Reasoning and knowledge, 20 (GPQA Diamond, MMLU-Pro, Humanity's Last Exam, composite indexes that publish their components)\n- Instruction following, 15 (IFEval, IFBench)\n- Long context, 10 (RULER, LongBench and long-context retrieval tests)\n\nA speech model is read on its own areas, recognition accuracy (word error rate on the Open ASR Leaderboard's sets, 60) and speed (real-time factor, 40) for speech to text, and intelligibility and speaker similarity (Seed-TTS-eval, the Open TTS Leaderboard, 70) and speed (30) for text to speech. A publisher's own converted copy of the same weights (Qwen's -hf builds, say) counts as the same checkpoint. An embedding model is read on retrieval (60) and the other MTEB task types, classification, clustering, reranking and similarity among them (40), from the MTEB leaderboard. A vision model adds an image area (MMMU, ChartQA, DocVQA or OCRBench) at 20, and the others shrink to fit.\n\nCapability is the weighted mean of the areas with results, and needs results in at least two areas (one for a speech or embedding model). With less than that it's pending for that listing, its weight is shared across the other four, and the total is capped at 61, so a model nobody has benchmarked independently can't reach B, and every model with results ranked B or better stays above it. The listing says so. Until 21 October 2026 the cap was 69, which put models nobody had benchmarked above ones that had been.\n\n## 3. Reach, 65 out of 100, up to 8.8 more on the total\n\nWhy it scored 65: Memory earns 5. MiniMax only publishes its 230 GB FP8 release, and the smallest build of 4-bit or better from a runtime's own library is LM Studio's MLX 4-bit at 128.7 GB, about 154 GB with the fifth added, so it needs more than our 107.4 GB machine and fits within 256 GB. Runtimes earn the full 25, with model code in released vLLM, SGLang, llama.cpp and Transformers and an LM Studio catalogue entry; Ollama only ever served it from its cloud and has retired that. Builds earn 10 for one publisher precision, the FP8 release. Terms earn 10 for a custom licence that allows free commercial use under a use policy. Download earns 10, with no gate or account. Hosted earns 5, since Novita and Featherless AI serve it through Hugging Face, while MiniMax's own API is closed to new users and retires it on 18 November 2026.\n\nThe checklist (https://www.anchorterminal.com/benchmark/#checklist-reach):\n\nWhether an agent can run it, and where.\n\n- 0 to 30, memory. The smallest build from the publisher or a runtime's own library that keeps 4-bit precision or better, at its file size plus a fifth for the runtime and a working context. 30 for 16 GB or less, 25 for 24 GB, 20 for 32 GB, 15 for 64 GB, 10 for what fits our reference machine (the GPU share of 128 GB of unified memory), 5 for 256 GB, 0 above.\n- 0 to 25, runtimes. 5 for each of llama.cpp, Ollama, vLLM, SGLang, LM Studio and Hugging Face Transformers that runs the architecture in a released version, from the runtime's own code, library or documentation. A publisher's fork or an unreleased branch doesn't count. 25 at most.\n- 0 to 15, builds from the publisher. 15 for quantised builds at two precisions or more (FP8, 4-bit, GGUF), 10 for one, 0 when only other people's quantisations exist.\n- 0 to 15, terms of use. 15 for an OSI-approved licence with nothing added, 10 for a custom licence that lets anyone use it commercially for free below a threshold or under a use policy, 5 for research or non-commercial use only, 0 for no licence or unclear terms.\n- 0 to 10, download. 10 when the files download with no account and no terms to accept, 5 for a click-through that needs an account, 0 when a person approves each request.\n- 5, hosted too. The publisher's own API serves it, or at least two inference providers do, so an agent without the hardware can still call it.\n\nWhen our reference-machine runs start, speed on that machine (tokens per second and time to first token at the build that fits) joins Reach.\n\n## 4. Documentation and upkeep, 22 out of 100, up to 7.8 more on the total\n\nWhy it scored 22: The model card earns 2, stating only intended use and leaving out limitations, architecture, parameters, context, natural languages and a cut-off. Training data earns 5 for a general description with no cut-off. The last change earns 5, the licence and evaluation results in March 2026, within a year. Responsiveness earns 5, with a MiniMax reply in 2 of the 40 most recent discussions and none since April. Version policy earns 5, since earlier and later models sit in their own repositories without dated notes. File health earns nothing, because the card's licence metadata contradicts the licence in the files and an FP8 output report waits without a reply.\n\nThe checklist (https://www.anchorterminal.com/benchmark/#checklist-upkeep):\n\n- 0 to 20, the model card. Intended use, limitations, architecture, parameters, context length, languages and knowledge cut-off. 20 for all seven, 3 off for each that's missing.\n- 0 to 20, training data. 20 for the data published or listed with sources and sizes, 15 for a data card that names the sources or the mix, the cut-off and the filtering, 10 for a general description with a cut-off, 5 without one, 0 for none.\n- 0 to 20, the last change to the files, the template or the reference code. 20 within 30 days, 15 within 90, 10 within 180, 5 within a year, 0 older.\n- 0 to 20, responsiveness. The publisher's replies to the 40 most recent questions and reports in the repository's discussions or issues. 20 for most, 10 for some, 5 for a few, 0 for none.\n- 0 to 10, version policy. 10 for earlier versions kept in their own repositories with a dated note when one is replaced, 5 for earlier versions kept without notes or a first release, 0 when files were replaced in place without a note.\n- 0 to 10, file health. 10 when config, tokenizer, template and card agree and no report of a broken file waits without a reply, 5 when one does, 0 when the files contradict the card.\n\n## 5. Agent interface, 75 out of 100, up to 2.5 more on the total\n\nWhy it scored 75: The chat template earns 20 for chat_template.jinja in the repository. The tool-calling format earns 25, as MiniMax's own guide documents how tools go in and how calls come back, gives a parsing function, and its deployment guides name the vLLM and SGLang parsers. Trained tool calling earns 20, with several invoke blocks in one call. Structured output earns nothing, since JSON output isn't documented or claimed. Reasoning control earns nothing, because thinking is always on with no documented way to turn it off or budget it. Context earns 10 for 196,608 tokens with no position scaling.\n\nThe checklist (https://www.anchorterminal.com/benchmark/#checklist-interface):\n\nWhat a model and a developer need to talk to it. Speech, image and other models with no chat interface read the tool-calling and structured-output lines for what the model takes and returns (voices, timestamps, streaming, typed outputs), and the note says how.\n\n- 0 to 20, the chat template. 20 for a template in the repository (Jinja, in the tokenizer config or its own file), 10 when the prompt format is only given as code you run.\n- 0 to 25, the tool-calling format. 25 when the publisher documents how tools are declared and how calls come back, with the parser to use in a named runtime, 10 when only the template or a runtime's own recipe says it.\n- 0 to 20, tool calling the model was trained for. 20 with several calls in one turn, 10 for one call at a time.\n- 0 to 15, structured output. 15 when JSON or JSON Schema output is documented for the model with how to ask for it, 5 when the card claims it without saying how.\n- 10, reasoning control. Thinking that can be turned off or budgeted per request, with where the reasoning text comes back.\n- 0 to 10, context. 10 for 128K tokens or more trained, 5 for 32K to 127K. Context reached only by scaling the position encoding counts one band down.\n\n## What we couldn't check\n\nWhat we couldn't read counted as absent. Publishing it on a page a plain HTTP fetch can read (not only in a browser) lets the next check count it.\n\n- Unchecked, whether model@minimax.io takes security reports; it's scored as a general contact, not a security contact, so the security programme is 0\n- Unchecked, replies on the MiniMax-AI/MiniMax-M2.5 GitHub issues; github.com answered 403 to this session, so responsiveness rests on the Hugging Face discussions alone\n- Responsiveness was recounted on the 40-discussion window, 2 of 40 with a MiniMax reply, where the earlier research counted 9 of all 51\n- Unchecked, whether Transformers 5.20.0 loads the FP8 checkpoint without trust_remote_code\n- Builds read as one precision (10) for MiniMax's native FP8 release, as for the other native FP8 and MXFP4 releases; a stricter reading would give 0\n- Licence risk takes off both the terms MiniMax can change or revoke (termination for any breach, and the replacement after release) and the change without a dated note with a broken link; a reader who sees the replacement as one fault would give 5\n- Whether the licence replacement of 6 March 2026 is also a negative event (a misleading claim, −2 to −3) is still the founder's call; none is applied\n- No sameModel set; MiniMax's API still serves M2.5 to existing users until 18 November 2026, after which hosted rests on Novita and Featherless AI\n- Confidence follows Capability, which the script computes; medium is carried from the dossier until that result is in\n- The model doesn't fit the 107.4 GB reference machine at any publisher or runtime-library build (the smallest, LM Studio's MLX 4-bit, needs about 154 GB), so our own runs won't add speed or robustness\n\n## Weaknesses\n\n- 230 GB in FP8, and about 155 GB for the smallest 4-bit library build, more than a 128 GB machine\n- Thinking is always on, with no documented way to turn it off or budget it\n- The card says modified MIT and links to a deleted file, while the licence in the repository adds a Prohibited Uses Policy and termination for breach\n- No safety evaluation, agent guidance or security contact\n- No MiniMax reply in discussions since April 2026, and the card leaves out limitations, architecture, parameters and a cut-off\n\n## What costs an agent a turn today\n\nThe notes we give agents before they call it. Each one is a workaround an agent shouldn't need.\n\n- Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think\n- In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code\n- MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache\n- On a single large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.5 lists a Q4_K_M GGUF of 138 GB and an MLX 4-bit of 129 GB (Mac only), and LM Studio states 121 GB minimum memory\n- Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends\n- Budget output tokens for thinking on every turn, since the template always opens a think block\n- Read LICENSE-MODEL, not the card's licence link, before shipping, and keep the required Notice text in every copy\n- Without the hardware, call it through Novita or Featherless AI; MiniMax's own API retires M2.5 on 18 November 2026\n\n## What the review panel asked for\n\n- fix the card's licence metadata\n- add a successor note\n- a dated licence change note\n- a way to turn thinking off\n- native runtime support without remote code\n- a dated licence history\n\n## When it's done\n\nSend what changed and where it's published as a dispute (https://www.anchorterminal.com/builders/#disputes, or `POST https://www.anchorterminal.com/api/v1/contact` with `\"kind\": \"dispute\"`). Disputes are answered in public, and the listing is checked again by the same checklist. Paying for an audit or a listing claim changes nothing here.\n",
    "grade": "C",
    "score": 57.5,
    "assessed": "2026-10-21",
    "run": "October 2026 research run",
    "categories": [
      {
        "key": "risk",
        "name": "Risk",
        "score": 30,
        "maxGain": 14,
        "reason": "File safety earns 15, safetensors only with no pickle files, less 10 because every documented loading route passes trust_remote_code for the repository's Python. Provenance earns the full 15, files in the organisation MiniMax links to with a SHA-256 for each shard. Safety evaluation and agent guidance earn nothing, as MiniMax publishes neither. Licence risk earns nothing (read 21 October 2026). The Prohibited Uses Policy goes beyond the law, MiniMax can end the licence for any breach and replaced its terms three weeks after release, and that change on 6 March 2026 came with no dated note while the card still links to a deleted file. The security programme earns nothing, with no security.txt, disclosure policy, SECURITY.md or advisories, and only a general contact address on the card.",
        "checklist": [
          "What could go wrong when you run it, and what the publisher has done about it. A higher score means less risk.",
          "- 0 to 25, file safety. 25 for weights in safetensors or GGUF only, 15 when pickle files (.bin, .pt, .pth) sit beside them, 0 for pickle only. 10 off when the documented way to load it or to build its prompts runs code from the repository (`trust_remote_code`, or the publisher's scripts in place of a template).\n- 0 to 15, provenance. 10 when the files come from the publisher's own organisation, the one its site or docs link to, and 5 for a checksum on every file.\n- 0 to 20, safety evaluation. 20 when results are published and someone other than the publisher ran them or can rerun them, 10 for the publisher's own results, 0 for none.\n- 0 to 15, guidance for agent use. 15 for documented prompt-injection guidance or an instruction hierarchy the model was trained to follow, 10 for a statement of what its trained refusals cover and the safeguards to add around it, 5 for general cautions, 0 for none.\n- 0 to 15, licence risk. 15 to start, 5 off for each of these, a use policy or field-of-use limits beyond the law, terms the licensor can change or revoke, a revenue or user threshold, and a licence that changed without a dated note or whose link is broken. Not below 0.\n- 0 to 10, a security programme. 5 for a way to report a flaw (security.txt, a disclosure policy or a security contact), 5 for advisories handled in public.",
          "When our reference-machine runs start, measured robustness (prompt injection with AgentDojo, over-refusal with XSTest) joins Risk."
        ],
        "checklistUrl": "https://www.anchorterminal.com/benchmark/#checklist-risk"
      },
      {
        "key": "capability",
        "name": "Capability",
        "score": 73,
        "maxGain": 9.5,
        "reason": "From 16 independent benchmark results in 5 areas, each scored as its share of the way from the benchmark's floor to the best result any model has on the same leaderboard. By area, Coding 89, Instruction following 86, Long context 83, Tool calling and agent tasks 65, Reasoning and knowledge 55. Publisher-submitted results count at half weight, and numbers that appear only on the publisher's card don't count.",
        "checklist": [
          "How well the model does on the benchmarks that matter to an agent. We don't average a publisher's own numbers into a grade. A result counts in full when we ran it on our reference machine, or when an independent party ran it and published the method (the Berkeley Function Calling Leaderboard, Artificial Analysis, Epoch AI's Benchmarking Hub, the SWE-bench and Terminal-Bench leaderboards, the Open ASR Leaderboard and others that run models themselves). A result the publisher submitted to such a leaderboard counts at half weight. A number that appears only on the publisher's card is shown on the listing as the publisher's claim and doesn't count.",
          "Each result is scored as its share of the way from the benchmark's floor (chance for multiple choice, zero for a pass rate) to the best result any model, open or closed, has on the same leaderboard and setting on the day we read it. A model that matches the best model anywhere scores 100 on that benchmark. For an error rate such as word error rate, lower is better and the scale runs the other way, and where the leaderboard gives no floor the rate is read against the best model's, so twice the best model's error rate scores 50. Speed as a multiple of real time (RTFx) runs from about 1 to over 20,000, so it's read on a log scale from real time to the fastest model. On the MTEB leaderboard, where most models score between 0.5 and 0.75, the floor is the median of every model the same tab lists, and its results count at half weight because MTEB doesn't say who ran them and most are submitted with the model. An arena rating (Elo) isn't a ratio, so its floor is 400 points below the best model on that arena, the gap at which the better model wins about ten games in eleven. Where a model has results at several settings (reasoning effort, say), its best one counts, because that's the setting an agent would run.",
          "The results group into areas, each weighted within the category.",
          "- Tool calling and agent tasks, 35 (BFCL, τ²-bench, Terminal-Bench and agent suites like them)\n- Coding, 20 (SWE-bench Verified, LiveCodeBench, Aider Polyglot)\n- Reasoning and knowledge, 20 (GPQA Diamond, MMLU-Pro, Humanity's Last Exam, composite indexes that publish their components)\n- Instruction following, 15 (IFEval, IFBench)\n- Long context, 10 (RULER, LongBench and long-context retrieval tests)",
          "A speech model is read on its own areas, recognition accuracy (word error rate on the Open ASR Leaderboard's sets, 60) and speed (real-time factor, 40) for speech to text, and intelligibility and speaker similarity (Seed-TTS-eval, the Open TTS Leaderboard, 70) and speed (30) for text to speech. A publisher's own converted copy of the same weights (Qwen's -hf builds, say) counts as the same checkpoint. An embedding model is read on retrieval (60) and the other MTEB task types, classification, clustering, reranking and similarity among them (40), from the MTEB leaderboard. A vision model adds an image area (MMMU, ChartQA, DocVQA or OCRBench) at 20, and the others shrink to fit.",
          "Capability is the weighted mean of the areas with results, and needs results in at least two areas (one for a speech or embedding model). With less than that it's pending for that listing, its weight is shared across the other four, and the total is capped at 61, so a model nobody has benchmarked independently can't reach B, and every model with results ranked B or better stays above it. The listing says so. Until 21 October 2026 the cap was 69, which put models nobody had benchmarked above ones that had been."
        ],
        "checklistUrl": "https://www.anchorterminal.com/benchmark/#checklist-capability"
      },
      {
        "key": "reach",
        "name": "Reach",
        "score": 65,
        "maxGain": 8.8,
        "reason": "Memory earns 5. MiniMax only publishes its 230 GB FP8 release, and the smallest build of 4-bit or better from a runtime's own library is LM Studio's MLX 4-bit at 128.7 GB, about 154 GB with the fifth added, so it needs more than our 107.4 GB machine and fits within 256 GB. Runtimes earn the full 25, with model code in released vLLM, SGLang, llama.cpp and Transformers and an LM Studio catalogue entry; Ollama only ever served it from its cloud and has retired that. Builds earn 10 for one publisher precision, the FP8 release. Terms earn 10 for a custom licence that allows free commercial use under a use policy. Download earns 10, with no gate or account. Hosted earns 5, since Novita and Featherless AI serve it through Hugging Face, while MiniMax's own API is closed to new users and retires it on 18 November 2026.",
        "checklist": [
          "Whether an agent can run it, and where.",
          "- 0 to 30, memory. The smallest build from the publisher or a runtime's own library that keeps 4-bit precision or better, at its file size plus a fifth for the runtime and a working context. 30 for 16 GB or less, 25 for 24 GB, 20 for 32 GB, 15 for 64 GB, 10 for what fits our reference machine (the GPU share of 128 GB of unified memory), 5 for 256 GB, 0 above.\n- 0 to 25, runtimes. 5 for each of llama.cpp, Ollama, vLLM, SGLang, LM Studio and Hugging Face Transformers that runs the architecture in a released version, from the runtime's own code, library or documentation. A publisher's fork or an unreleased branch doesn't count. 25 at most.\n- 0 to 15, builds from the publisher. 15 for quantised builds at two precisions or more (FP8, 4-bit, GGUF), 10 for one, 0 when only other people's quantisations exist.\n- 0 to 15, terms of use. 15 for an OSI-approved licence with nothing added, 10 for a custom licence that lets anyone use it commercially for free below a threshold or under a use policy, 5 for research or non-commercial use only, 0 for no licence or unclear terms.\n- 0 to 10, download. 10 when the files download with no account and no terms to accept, 5 for a click-through that needs an account, 0 when a person approves each request.\n- 5, hosted too. The publisher's own API serves it, or at least two inference providers do, so an agent without the hardware can still call it.",
          "When our reference-machine runs start, speed on that machine (tokens per second and time to first token at the build that fits) joins Reach."
        ],
        "checklistUrl": "https://www.anchorterminal.com/benchmark/#checklist-reach"
      },
      {
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "score": 22,
        "maxGain": 7.8,
        "reason": "The model card earns 2, stating only intended use and leaving out limitations, architecture, parameters, context, natural languages and a cut-off. Training data earns 5 for a general description with no cut-off. The last change earns 5, the licence and evaluation results in March 2026, within a year. Responsiveness earns 5, with a MiniMax reply in 2 of the 40 most recent discussions and none since April. Version policy earns 5, since earlier and later models sit in their own repositories without dated notes. File health earns nothing, because the card's licence metadata contradicts the licence in the files and an FP8 output report waits without a reply.",
        "checklist": [
          "- 0 to 20, the model card. Intended use, limitations, architecture, parameters, context length, languages and knowledge cut-off. 20 for all seven, 3 off for each that's missing.\n- 0 to 20, training data. 20 for the data published or listed with sources and sizes, 15 for a data card that names the sources or the mix, the cut-off and the filtering, 10 for a general description with a cut-off, 5 without one, 0 for none.\n- 0 to 20, the last change to the files, the template or the reference code. 20 within 30 days, 15 within 90, 10 within 180, 5 within a year, 0 older.\n- 0 to 20, responsiveness. The publisher's replies to the 40 most recent questions and reports in the repository's discussions or issues. 20 for most, 10 for some, 5 for a few, 0 for none.\n- 0 to 10, version policy. 10 for earlier versions kept in their own repositories with a dated note when one is replaced, 5 for earlier versions kept without notes or a first release, 0 when files were replaced in place without a note.\n- 0 to 10, file health. 10 when config, tokenizer, template and card agree and no report of a broken file waits without a reply, 5 when one does, 0 when the files contradict the card."
        ],
        "checklistUrl": "https://www.anchorterminal.com/benchmark/#checklist-upkeep"
      },
      {
        "key": "interface",
        "name": "Agent interface",
        "score": 75,
        "maxGain": 2.5,
        "reason": "The chat template earns 20 for chat_template.jinja in the repository. The tool-calling format earns 25, as MiniMax's own guide documents how tools go in and how calls come back, gives a parsing function, and its deployment guides name the vLLM and SGLang parsers. Trained tool calling earns 20, with several invoke blocks in one call. Structured output earns nothing, since JSON output isn't documented or claimed. Reasoning control earns nothing, because thinking is always on with no documented way to turn it off or budget it. Context earns 10 for 196,608 tokens with no position scaling.",
        "checklist": [
          "What a model and a developer need to talk to it. Speech, image and other models with no chat interface read the tool-calling and structured-output lines for what the model takes and returns (voices, timestamps, streaming, typed outputs), and the note says how.",
          "- 0 to 20, the chat template. 20 for a template in the repository (Jinja, in the tokenizer config or its own file), 10 when the prompt format is only given as code you run.\n- 0 to 25, the tool-calling format. 25 when the publisher documents how tools are declared and how calls come back, with the parser to use in a named runtime, 10 when only the template or a runtime's own recipe says it.\n- 0 to 20, tool calling the model was trained for. 20 with several calls in one turn, 10 for one call at a time.\n- 0 to 15, structured output. 15 when JSON or JSON Schema output is documented for the model with how to ask for it, 5 when the card claims it without saying how.\n- 10, reasoning control. Thinking that can be turned off or budgeted per request, with where the reasoning text comes back.\n- 0 to 10, context. 10 for 128K tokens or more trained, 5 for 32K to 127K. Context reached only by scaling the position encoding counts one band down."
        ],
        "checklistUrl": "https://www.anchorterminal.com/benchmark/#checklist-interface"
      }
    ],
    "provenance": [
      {
        "label": "Domain age",
        "value": "minimax.io, registered 2021-03-27 (5 years)",
        "points": 11,
        "max": 15
      },
      {
        "label": "Status page",
        "value": "not found",
        "points": 0,
        "max": 10
      },
      {
        "label": "Changelog",
        "value": "not found",
        "points": 0,
        "max": 10
      },
      {
        "label": "security.txt",
        "value": "not found",
        "points": 0,
        "max": 10
      }
    ],
    "unchecked": [
      "Unchecked, whether model@minimax.io takes security reports; it's scored as a general contact, not a security contact, so the security programme is 0",
      "Unchecked, replies on the MiniMax-AI/MiniMax-M2.5 GitHub issues; github.com answered 403 to this session, so responsiveness rests on the Hugging Face discussions alone",
      "Responsiveness was recounted on the 40-discussion window, 2 of 40 with a MiniMax reply, where the earlier research counted 9 of all 51",
      "Unchecked, whether Transformers 5.20.0 loads the FP8 checkpoint without trust_remote_code",
      "Builds read as one precision (10) for MiniMax's native FP8 release, as for the other native FP8 and MXFP4 releases; a stricter reading would give 0",
      "Licence risk takes off both the terms MiniMax can change or revoke (termination for any breach, and the replacement after release) and the change without a dated note with a broken link; a reader who sees the replacement as one fault would give 5",
      "Whether the licence replacement of 6 March 2026 is also a negative event (a misleading claim, −2 to −3) is still the founder's call; none is applied",
      "No sameModel set; MiniMax's API still serves M2.5 to existing users until 18 November 2026, after which hosted rests on Novita and Featherless AI",
      "Confidence follows Capability, which the script computes; medium is carried from the dossier until that result is in",
      "The model doesn't fit the 107.4 GB reference machine at any publisher or runtime-library build (the smallest, LM Studio's MLX 4-bit, needs about 154 GB), so our own runs won't add speed or robustness"
    ],
    "weaknesses": [
      "230 GB in FP8, and about 155 GB for the smallest 4-bit library build, more than a 128 GB machine",
      "Thinking is always on, with no documented way to turn it off or budget it",
      "The card says modified MIT and links to a deleted file, while the licence in the repository adds a Prohibited Uses Policy and termination for breach",
      "No safety evaluation, agent guidance or security contact",
      "No MiniMax reply in discussions since April 2026, and the card leaves out limitations, architecture, parameters and a cut-off"
    ],
    "agentNotes": [
      "Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think",
      "In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code",
      "MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache",
      "On a single large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.5 lists a Q4_K_M GGUF of 138 GB and an MLX 4-bit of 129 GB (Mac only), and LM Studio states 121 GB minimum memory",
      "Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends",
      "Budget output tokens for thinking on every turn, since the template always opens a think block",
      "Read LICENSE-MODEL, not the card's licence link, before shipping, and keep the required Notice text in every copy",
      "Without the hardware, call it through Novita or Featherless AI; MiniMax's own API retires M2.5 on 18 November 2026"
    ],
    "requests": [
      {
        "text": "fix the card's licence metadata",
        "reviews": 1
      },
      {
        "text": "add a successor note",
        "reviews": 1
      },
      {
        "text": "a dated licence change note",
        "reviews": 1
      },
      {
        "text": "a way to turn thinking off",
        "reviews": 1
      },
      {
        "text": "native runtime support without remote code",
        "reviews": 1
      },
      {
        "text": "a dated licence history",
        "reviews": 1
      }
    ],
    "recheck": "https://www.anchorterminal.com/builders/#disputes"
  },
  "meta": {
    "attribution": "Anchor Terminal (https://www.anchorterminal.com)",
    "attributionRequired": true,
    "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)",
    "docs": "https://www.anchorterminal.com/docs/",
    "generatedAt": "2026-10-21",
    "license": "CC-BY-4.0",
    "method": "https://www.anchorterminal.com/benchmark/",
    "methodology": "0.6",
    "openapi": "https://www.anchorterminal.com/openapi.json",
    "preview": false,
    "run": "2026-10-01",
    "runLabel": "October 2026 research run",
    "terms": "https://www.anchorterminal.com/terms/#data"
  }
}