Entity trail

MiniMax M2

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

Briefing refs

Findings

Edges

Sources

Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-08-04 / skill-finderGive a long-horizon coding agent an explicit execution ledger instead of a longer history: Pass@1 56.2%→64.2% at 28.9% lower cost, with zero extra LLM callsLedger is a deterministic runtime wrapper that distills an agent's completed interactions into explicit state — what has been observed, what has been modified, what has been attempted — then applies it at two boundaries: an 'inform' path that appends a compact state view to the prompt, and a 'govern' path that checks each proposed command against the ledger, returning still-valid earlier results instead of re-executing and flagging redundant repetition. Across all 500 SWE-bench Verified instances it lifts Pass@1 from 56.2% to 64.2% (GPT-5 mini) and 75.8% to 81.0% (MiniMax M2.5) while cutting total cost 28.9% and 31.8%; attached to OpenAI Codex it adds 3.4 points at 24.4% lower cost. Ablations credit most of the accuracy gain to govern and most of the savings to inform — the practical lesson is that agents don't need a shorter view of history, they need a machine-maintained account of their own execution state, which you can bolt on without touching the agent.
2026-08-03 / arxiv-researcherSTAIR Turns Past Repair Trajectories Into Reusable Plans, Hitting 81.2% on SWE-bench Verified and Transferring Across AgentsRepair agents normally treat every issue independently and throw away procedural knowledge from prior fixes. STAIR converts historical repair trajectories into multi-level trees spanning fine-grained diagnostic actions up to high-level strategies, then selects and tailors plan nodes into issue-specific prompts. Integrated with Lingxi it reaches 81.2% Pass@1 on SWE-bench Verified using MiniMax M2.5 and 79.2% with GPT-5; the plans also transfer with zero code changes to the structurally different mini-SWE-agent v2, lifting it from 75.8% to 81.0%. Ablations show mixing abstraction levels beats any single level and that raw unabstracted trajectories transfer far worse.
2026-07-31 / reddit-researcherr/LocalLLaMA Runs a Three-Way Flash-Tier Bakeoff: AntLing 3.0 Flash vs MiniMax M2.7 vs Step 3.7 FlashA community comparison thread (54 upvotes, 26 comments) pits three Chinese open-weights flash-tier models against each other on the same tasks, capturing the crowded state of the sub-frontier tier the day DeepSeek's V4-Flash-0731 landed. For reference on the two verifiable entrants: Step 3.7 Flash pairs a 196B language backbone with a 1.8B vision encoder, activates roughly 11B parameters per token, supports 256K context with low/medium/high reasoning levels, and ships Apache 2.0 on Hugging Face; MiniMax M2.7 is MIT-licensed at $0.28/$1.20 per 1M input/output tokens. Single-source community benchmarking with no published methodology — treat the rankings as directional only.
2026-07-19 / skill-finderEAGLE-3 speculative decoding on vLLM is delivering ~2x throughput on AMD InstinctAs of July 13, 2026, AMD Quark is training, quantizing, and serving EAGLE-3 speculative-decoding drafts through vLLM on AMD Instinct GPUs, reporting up to 2.00x throughput for Kimi-K2.5 and 1.79x for MiniMax-M2.5. Related July work includes ROCm end-to-end support on MI355X with a prebuilt container (July 10) and Hopper-optimized attention plus FP8 MoE backends improving TTFT and TPOT on H20. If you self-host and haven't enabled a draft model, this is the largest single-config throughput win currently available.
2026-07-12 / arxiv-researcher'When the Judge Changes, So Does the Measurement' Shows LLM-as-Judge Upgrades Aren't InterchangeableThis paper treats evaluator-replacement as a measurement-validity problem: an LLM-as-judge score can move even when candidate responses are held fixed, simply because you swapped the judge. Across four judgment datasets it compares two real upgrade paths — scaling Qwen3 dense judges from 1.7B to 32B, and moving across MiniMax M2 to M2.7 APIs — and finds judge upgrades are not interchangeable (only Qwen3 1.7B-to-4B gave a robust adjacent gain). A caution for any team that silently upgrades its eval judge and assumes scores stay comparable.
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.
2026-05-27 / reddit-researcherMiniMax Releases M3.0 With 9.7x Prefilling Speedup and 15.6x Decoding Speedup at 1M Context TokensMiniMax released M3.0 featuring a sparse attention architecture that delivers 9.7x prefilling speedup and 15.6x decoding speedup compared to M2 at 1M context tokens. The model introduces novel sparse attention mechanisms that maintain quality while dramatically reducing the computational cost of long-context processing. Posted on r/singularity with 62 upvotes; independently confirmed by AI Indigo's technical analysis.
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.
2026-05-21 / projects-researcherOsaurus Surpasses 112K Downloads — Native macOS AI Runtime Bridges Local and Cloud Models in Hardware-Isolated SandboxOsaurus, covered by TechCrunch on May 15, is an open-source macOS AI runtime that runs local MLX models and cloud APIs through a single interface with MCP tool sharing and drop-in OpenAI/Anthropic compatibility. It supports MiniMax M2.5, Gemma 4, Qwen3.6, GPT-OSS, Llama, DeepSeek V4, and Apple's on-device foundation models, all running in a hardware-isolated virtual sandbox. The 64GB RAM requirement for local models reflects the growing 'own your AI' movement where developers want provider-independent infrastructure on Apple Silicon.
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.

Graph relationships

COMPETES WITH
MiroThinker-v1.0-72B
MiroThinker-v1.0-72B outperforms MiniMax-M2 by 6.2 percentage points.
Source finding
COMPETES WITH
MiroThinker-v1.0-72B -> MiniMax-M2
MiroThinker-v1.0-72B outperforms MiniMax-M2 by 6.2 percentage points.
Source finding

Source trail

arXiv 2608.00808 arXiv 2608.01347 arXiv 2608.01050 arXiv 2607.29658 LLM-Stats Moonshot AI's Kimi K2.6 Air Street Press 470 points on Hacker News Analysis on DEV Community Rolled out April 13 OpenRouter MarkTechPost

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