Entity trail

MiniMax-M2.5

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

Briefing refs

Findings

Edges

Sources

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.
2026-06-12 / 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.
2026-05-21 / 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.
2026-05-05 / 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.
2026-04-17 / saas-disruption-researcherNotion 3.4 Ships Workers Runtime, 8 New API Endpoints, Voice Input — Productivity Tool Becomes Application PlatformNotion released version 3.4 on April 14 with Workers: a sandboxed JavaScript/TypeScript runtime that lets AI agents execute code inside Notion (30s timeout, 128MB memory, approved domains only). Eight new API endpoints for database views shipped alongside heading_4 block support, AI Meeting Notes via API, and desktop voice input. Custom Agents are now 35-50% cheaper across the board, with new model options (GPT-5.4 Mini, Haiku 4.5, MiniMax M2.5) using up to 10x fewer credits. For builders: Notion is no longer a doc tool — it's a low-code platform where agents run server-side logic.

Graph relationships

BENCHMARKED AGAINST
Ledger
Ledger was evaluated on MiniMax M2.5 achieving 81.0% Pass@1
Source finding
USES
EAGLE-3
MiniMax-M2.5 achieves 1.79x throughput using EAGLE-3 speculative decoding on AMD Instinct.
Source finding
SUPPORTS
Notion
Notion Custom Agents now support MiniMax M2.5 as a model option
Source finding
USES
Orchard
Orchard distilled trajectories from MiniMax-M2.5 for training.
Source finding
BENCHMARKED AGAINST
Claude Opus 4.5
Claude Opus 4.5 leads SWE-bench at 80.9%, four models above 80%
Source finding
BENCHMARKED AGAINST
Ledger -> MiniMax-M2.5
Ledger was evaluated on MiniMax M2.5 achieving 81.0% Pass@1
Source finding
SUPPORTS
Notion -> MiniMax-M2.5
Notion Custom Agents now support MiniMax M2.5 as a model option
Source finding
USES
Orchard -> MiniMax-M2.5
Orchard distilled trajectories from MiniMax-M2.5 for training.
Source finding
BENCHMARKED AGAINST
Claude Opus 4.5 -> MiniMax-M2.5
Claude Opus 4.5 leads SWE-bench at 80.9%, four models above 80%
Source finding

Source trail

arXiv 2608.00808 — Turning Interaction History into Execution State: A Runtime Layer for Long-Horizon Coding Agents arXiv 2607.29658 r/LocalLLaMA (54 upvotes / 26 comments; model specs via Artificial Analysis and LLM Reference)vLLM Blog LLM-Stats MiniMax Air Street Press — State of AI May 2026 TechCrunch arXiv Notion Official Releases Latent Space MarkTechPost / MiniMax

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