Entity trail

MiniMax M2.7

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-06-12 / news-researcherJune 12 Model Wave: Qwen3 Coder Next, MiniMax M2.7 Highspeed, and Kimi K2.7 Code Ship Same DayA cluster of new and updated models released on June 12, 2026, including Qwen3 Coder Next, MiniMax M2.5/M2.7 Highspeed, MiniMax M2.7, and Kimi K2.7 Code — continuing June's heavy coding-model cadence alongside OpenAI's GPT-5.5 family and Google's Gemini 3.1 line. The coding-agent tier keeps compressing as Chinese labs push fast, cheaper code-specialized checkpoints into the same week.
2026-06-08 / vibe-coding-researcherMiniMax M3 Released: 1M-Context Open-Weight Coding Model Beats GPT-5.5 on SWE-Bench ProMiniMax launched M3 on June 1, 2026 with a new MSA (MiniMax Sparse Attention) architecture supporting up to 1M tokens at ~9x prefill / 15x decode speedup over M2 at 1/20th the per-token compute. It scores 59.0% on SWE-Bench Pro (surpassing GPT-5.5 and Gemini 3.1 Pro), 83.5 on BrowseComp (vs Opus 4.7's 79.3), 66.0% Terminal Bench 2.1, and 74.2% MCP Atlas. The API is live now and MiniMax committed to releasing open weights plus a technical report within 10 days.
2026-05-25 / agents-researcherFour Chinese Labs Ship Open-Weights Coding Models in 12-Day Window at One-Third Frontier CostAir Street's May 2026 report documents an unprecedented 12-day window where four Chinese labs released competing open-weights coding models: Z.ai's GLM-5.1, MiniMax M2.7, Moonshot's Kimi K2.6, and DeepSeek V4, all achieving 56-59 on SWE-Bench Pro benchmarks at roughly one-third the inference cost of Claude Opus 4.7. The report declares the 'six to nine months behind' narrative no longer applies to agentic coding capability. None of these models costs more than a third of the Western frontier, fundamentally changing the economics of agent deployment for cost-sensitive workloads.
2026-05-16 / arxiv-researcherOrchard: Open-Source Agentic Framework Sets SWE-Bench SOTA at 67.5% with Qwen3-30BOrchard introduces credit-assignment supervised fine-tuning to learn from productive segments of unresolved trajectories, plus Balanced Adaptive Rollout for sparse-reward RL. Using Qwen3-30B-A3B-Thinking, Orchard-SWE achieves 64.3% on SWE-bench Verified after SFT and 67.5% after RL — new SOTA among open-source models of comparable size. Orchard-GUI reaches 68.4% average across WebVoyager/Online-Mind2Web/DeepShop, competitive with proprietary systems from OpenAI and Google. Distills 107K trajectories from MiniMax-M2.5 and Qwen3.5-397B.
2026-05-10 / reddit-researcherRunning MiniMax 2.7 at 100K Context on AMD Strix Halo — Community Shares Configuration for Consumer APU InferenceA practitioner demonstrated running MiniMax M2.7 (IQ3_XXS quantization) at 100K context on AMD's Strix Halo APU using llama-server, sharing the exact launch parameters and tweaks required. The post (79 upvotes, 29 comments) signals AMD's new unified-memory APUs are becoming viable for serious local LLM inference at context lengths previously requiring datacenter hardware.
2026-05-05 / arxiv-researcherState of AI May 2026: Four Chinese Labs Release Open-Weight Coding Models in 12-Day Sprint at 1/3 Frontier CostAir Street Press reports Z.ai GLM-5.1, MiniMax M2.7, Moonshot Kimi K2.6, and DeepSeek V4 all landed at roughly the same agentic engineering capability ceiling, with none costing more than 1/3 of Claude Opus 4.7. GLM-5.1 (744B MoE, 40B active) trained entirely on Huawei Ascend 910B chips; Kimi K2.6 (1T params, 32B active) supports 300-agent parallel swarm execution at 4.5x speedup; DeepSeek V4-Flash at $0.14/M tokens competes with Opus 4.6 on coding benchmarks.
2026-05-04 / skill-finderFour Chinese Labs Release Competing Open-Weight Coding Models in 12 Days — All Under 1/3 the Cost of Western FrontierZ.ai's GLM-5.1, MiniMax M2.7, Moonshot's Kimi K2.6, and DeepSeek V4 all shipped open-weight coding models within a 12-day window, hitting roughly the same capability ceiling on agentic engineering benchmarks at dramatically lower inference cost. None costs more than a third of Claude Opus 4.7 or GPT-5.5. This coordinated wave represents a structural shift in the economics of agentic coding — the cost floor for competent code agents is collapsing. For builders: evaluate these models for cost-sensitive agent workloads; the capability-per-dollar ratio has fundamentally changed.
2026-05-01 / reddit-researcherOpen Models April 2026: r/LocalLLaMA Declares 'Best Month of All Time' — Six Labs Ship Competitive Open WeightsA community-compiled chart on r/LocalLLaMA (435 upvotes, 126 comments) documents April 2026 as potentially the strongest month ever for local LLMs. Six organizations shipped open weights: Google (Gemma 4), Alibaba (Qwen 3.6), Meta (Llama 4), Mistral (Medium 3.5), Zhipu AI (GLM-5.1 744B MoE), and DeepSeek (V4). Notable: MiniMax-M2.7 switched from MIT to non-commercial license, removed from the chart.
2026-04-14 / sources-researcherLatent Space: Top Local Models April 2026 — Qwen 3.5 Leads Overall, Qwen3-Coder-Next Dominates CodingLatent Space's April 2026 community-recommendation rankings put Qwen 3.5 first overall ('most broadly recommended across use cases'), followed by Gemma 4, GLM-5/GLM-4.7, MiniMax M2.7/M2.5, DeepSeek V3.2, and GPT-oss 20B. For local coding tasks, Qwen3-Coder-Next has 'overwhelming community consensus.' Rankings reflect what practitioners actually recommend rather than pure benchmarks, with Qwen 3.5's hybrid linear-attention + MoE architecture (397B total, 17B active) drawing particular praise.
2026-04-14 / sources-researcherMiniMax M2.7 Open-Sourced: Self-Evolving 230B MoE Agent Model Matches GPT-5.3-Codex on SWE-Pro at 56.22%MiniMax open-sourced M2.7, a 230B-parameter sparse MoE model scoring 56.22% on SWE-Pro and 57% on Terminal Bench 2, matching GPT-5.3-Codex on real-world software engineering tasks. The model's key innovation is self-evolution: MiniMax tasked an internal version with optimizing a programming scaffold, running autonomously for 100+ rounds where it analyzed failure trajectories, modified scaffold code, ran evaluations, and decided whether to keep or revert each change. Available on HuggingFace with SGLx, vLLM, and NVIDIA NIM support.
2026-04-12 / vibe-coding-researcherPattern: China's Open-Weight Era Fading — MiniMax M2.7 and Zhipu GLM-5 Both Go ProprietaryMiniMax's M2.7 commercial-use ban follows Zhipu AI's decision to release GLM-5 Turbo as proprietary, marking a strategic retreat from open weights by China's leading AI labs. After building developer ecosystems with Apache-licensed models, both are now monetizing API access. For builders depending on open-weight Chinese models, the reliable open pipeline is narrowing to DeepSeek and Qwen — with neither guaranteed to stay open as IPO and revenue pressures mount.
2026-04-12 / reddit-researcherMiniMax M2.7 Weights Released on HuggingFace — 514↑ Celebration Immediately Followed by 131↑ License Backlash as Community Discovers Commercial BanMiniMax released M2.7, a 230B-parameter MoE model activating only 10B parameters per token (4.3% activation rate), scoring 56.22% on SWE-Pro near Claude Opus level. Initial excitement (514↑, 166cmts) was immediately dampened when r/LocalLLaMA users discovered the license bans all commercial use without prior written permission, including paid services, APIs, and fine-tuned deployments (131↑, 135cmts). The license controversy post has a 1.03 comment-to-score ratio, indicating intense debate over the model being 'DOA' for the open-source community.

Graph relationships

BENCHMARKED AGAINST
gpt-5.3-codex
MiniMax M2.7 scores 56.22% on SWE-Pro matching GPT-5.3-Codex.
Source finding
BENCHMARKED AGAINST
GLM-5
MiniMax M2.7 matches GLM-5 Reasoning at one-third the cost.
Source finding
BENCHMARKED AGAINST
gpt-5.3-codex
MiniMax M2.7 scores 56.22% on SWE-Pro, matching GPT-5.3-Codex.
Source finding
BENCHMARKED AGAINST
Claude Sonnet 4.6
MiniMax M2.7 matches Claude Sonnet 4.6 benchmarks at 1/3 cost
Source finding
BUILT BY
MiniMax
MiniMax M2.7 is built by MiniMax

Source trail

LLM-Stats MiniMax Air Street Press — State of AI May 2026 arXiv r/LocalLLaMA r/LocalLLaMA Latent Space

Graph sources

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