MiniMax M2.7
MiniMaxhuggingface.co/MiniMaxAI/MiniMax-M2.7
MiniMax-M2.7 is a next-generation large language model designed for autonomous, real-world productivity and continuous improvement. Built to actively participate in its own evolution, M2.7 integrates advanced agentic capabilities through multi-agent...
Updated 5 h ago · first seen 11 Sept 2026
model_01M294WW2MV89PBXXQQ9MTXTB7
Overview
Identity
Identity block not returned by the API for this entity.
Openness
Openness not classified yet — no sourced evidence to place this model in the ontology.
Key facts
- Release date
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 7 h agomedium
- Status
Source:Artificial AnalysisT2observed 10 h agomedium
- Model card
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium
- Openrouter id
Source:OpenRouter public model & pricing listingT2observed 12 h agomedium
Architecture
- Architecture
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 11 h agomedium
- Model type
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 11 h agomedium
- Parameters
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium
- Weights dtype
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 11 h agomedium
- File size
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 11 h agomedium
- Library name
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 11 h agomedium
- Pipeline tag
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium
- Hugging Face repo
Source:OpenRouter public model & pricing listingT2observed 12 h agomedium
Capabilities
Modalities
- Modalities
- text
- Input
- text
- Output
- text
Capabilities
Tool calling
Yes
OpenRouter public model & pricing listing · T2
Structured output
Yes
OpenRouter public model & pricing listing · T2
Reasoning
Yes
OpenRouter public model & pricing listing · T2
Vision
Unavailable
Audio
Unavailable
Fine-tuning available
Unavailable
- Context window
Source:OpenRouter public model & pricing listingT2observed 12 h agomedium
- Max output
Source:OpenRouter public model & pricing listingT2observed 12 h agomedium
Benchmarks9
Compare with another model →Comparable same task and conditions · Partially comparable same task, conditions differ (effort, temperature, judge) · Not comparable different variant or metric
No benchmark results recorded
Providers & Pricing2
All offers in the price terminal →USD per 1M tokens as published by each provider (USD). Rows are append-only: every change is kept in the history below.
Price history
Output price · USD / 1M tokens 2 providers
- MiniMax API
- Together AI
- Together AIfirst observed $1.211 Sept 2026
- MiniMax APIfirst observed $1.211 Sept 2026
Input price · USD / 1M tokens 2 providers
- MiniMax API
- Together AI
- Together AIfirst observed $0.3011 Sept 2026
- MiniMax APIfirst observed $0.3011 Sept 2026
Hardware fit37
Assumptions (6)
- Estimated, not measured: weights = parameters × bytes/param × 1.15 runtime overhead.
- bytes/param: 4bit = 0.5, 8bit = 1.0, fp16 = 2.0 (uniform quantization, no per-layer exceptions).
- KV cache approximated at 0.5 GB per 8 192 tokens of context, independent of architecture (GQA/MLA models need less).
- A model 'fits' when the estimate is at most the device memory minus 2 GB reserved for the OS and framework.
- Mixture-of-experts models are estimated on total parameters (all experts must be resident); active parameters are ignored.
- Device memory uses the largest configuration when several are listed (e.g. Apple silicon tiers).
Timeline14
Full timeline →MiniMax M2.7 scores 39.39% on Terminal-Bench
artificial_analysisMiniMax M2.7 scores 55.43% on Terminal-Bench
artificial_analysisMiniMax M2.7 scores 29.61% on Humanity's Last Exam
artificial_analysisMiniMax M2.7 scores 23.22 on Artificial Analysis Intelligence Index
artificial_analysisTogether AI lists MiniMax M2.7 at $0.3 in / $1.2 out per 1M tokens
together_pricingMiniMax M2.7: release date changed from 2026-04-09 to 2026-03-18
Release date9 Apr 2026→18 Mar 2026openrouterMiniMax M2.7: release date changed from 2026-03-18 to 2026-04-09
Release date18 Mar 2026→9 Apr 2026huggingfaceMiniMax API lists MiniMax M2.7 at $0.3 in / $1.2 out per 1M tokens
openrouter
Change history41
Viewing AI Atlas as of 12 Sept 2026 — attributes exactly as the atlas knew them on that day; later corrections are not shown.
Back to today →Attributes as of 12 Sept 2026 44 claims in force
- Release date
- 9 Apr 2026
- Status
- deprecated
- Openness
- open-weights
- License
- Other
- Architecture
- MiniMaxM2ForCausalLM
- Parameters
- 228.7B
- Context window
- 204.8K tokens
- Max output
- 131.1K tokens
- Modalities
- text
- Input modalities
- text
- Output modalities
- text
- Quantization format
- fp8
- File size
- 230.1 GB
- Hugging Face repo
- MiniMaxAI/MiniMax-M2.7
- Pipeline tag
- text-generation
- Model card
- Downloads
- 1,323,648
- Likes
- 1,247
- Aa context window
- 204,800
- Aa deprecated
- Yes
- Aa openness
- open-weights
- Gated
- No
- Groq model id
- minimaxai/minimax-m2.7
- Groq pricing note
- Contact Sales
- Groq status
- preview
- Hf inference providers
- deepinfra, featherless-ai, novita
- Is quantized
- Yes
- Last modified
- 2026-04-20T04:28:14+00:00
- Library name
- transformers
- License name
- other
- License url
- https://github.com/MiniMax-AI/MiniMax-M2.7/blob/main/LICENSE
- Aa median output tokens per second
- 54.5
- Downloads all time
- 6,947,056
- Model type
- minimax_m2
- Openrouter id
- minimax/minimax-m2.7
- Openrouter listed at
- 18 Mar 2026
- Reasoning
- Yes
- Structured output
- Yes
- Supported parameters
- frequency_penalty, include_reasoning, logit_bias, logprobs, max_tokens, min_p, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_logprobs, top_p
- Tags
- transformers, safetensors, minimax_m2, text-generation, custom_code, fp8
- Tool calling
- Yes
- Weights available
- Yes
- Weights dtype
- BF16, F32, F8_E4M3
Release daterelease_date3
Statusstatus1
Opennessopenness1
Licenselicense1
Architecturearchitecture1
Parametersparameter_count1
Context windowcontext_length1
Max outputmax_output_tokens1
Modalitiesmodalities1
Input modalitiesmodalities_input1
Output modalitiesmodalities_output1
Quantization formatquant_format1
File sizefile_size_gb1
Hugging Face repohf_repo1
Pipeline tagpipeline_tag1
Model cardmodel_card_url1
Downloadsmetric.downloads1
Likesmetric.likes1
Descriptiondescription1
Gatedgated1
Groq model idgroq_model_id1
Groq pricing notegroq_pricing_note1
Groq statusgroq_status1
Hf inference providershf_inference_providers1
Is quantizedis_quantized1
Last modifiedlast_modified1
Library namelibrary_name1
License namelicense_name1
License urllicense_url1
Aa median output tokens per secondmetric.aa_median_output_tokens_per_second1
Downloads all timemetric.downloads_all_time1
Model typemodel_type1
Openrouter idopenrouter_id1
Reasoningreasoning1
Structured outputstructured_output1
Supported parameterssupported_parameters1
Tool callingtool_calling1
Weights dtypeweights_dtype1
Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →
Provenance
Attributed facts
40
Source tiers
T1T23 / 37
Freshest observation
5 h ago
Conflicts
None
Source documents 7
Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.
Data quality (74/100) measures how well AI Atlas knows this entity — completeness, primary-source ratio, freshness, conflicts — never how good the model is. Methodology →