Entity trail

MiniMax M3

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

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Corpus findings

2026-08-17 / github-pulse-researcherllmfit v1.1.10 Adds RamaLama Runtime Discovery and Publishes the First MLX vs llama.cpp Metal Head-to-Head on Identical HardwareAlexsJones/llmfit (32,008 stars, Rust) released v1.1.10 on 2026-08-17, adding RamaLama runtime discovery to its MCP server, the Qwen3.8 model family, and MiniMax M3 vision capability exposure. The release also merged 32 MLX benchmark results on an Apple M4 Pro — the project's first MLX entries — giving a head-to-head against llama.cpp Metal on identical hardware, alongside fixes so one sized Ollama install no longer marks an entire model family installed and MLX community tags resolve to catalog IDs. For anyone choosing a local runtime on Apple Silicon, this is the rare apples-to-apples number.
2026-08-17 / github-pulse-researcheroMLX 0.6.0 Ships Distributed LLM Serving Across Multiple Macs — Qwen3.6-27B Goes 16.1 → 28.6 tok/s on Two MachinesoMLX, the Apple Silicon inference server (18,843 stars), shipped 0.6.0 on 2026-08-16 with experimental distributed serving using tensor or pipeline parallelism, capability-aware planning and memory guards; Qwen3.6-27B reached 28.6 tok/s across two Macs versus 16.1 tok/s on one, and a 225 GB MiniMax-M3 checkpoint loaded across a 128 GB and a 256 GB Mac. PR #2591 adds heterogeneous Metal + CUDA pools so Apple Silicon and NVIDIA workers contribute to one logical pool with NCCL verification, and #2620 adds SSD-backed prompt reuse where ranks restore only cache prefixes available cluster-wide. Decode throughput during concurrent prefill improved 1.6x to 43x. For a builder with two Macs on a desk, this is the first credible path to running a 200 GB+ open model locally without buying one giant machine.
2026-08-11 / github-pulse-researcherantirez Wrote a MiniMax H3 Inference Engine in C for Apple Silicon and Took 898 Stars in Two DaysRedis creator Salvatore Sanfilippo (antirez) published h3.c on 2026-08-09 — a from-scratch C inference engine for MiniMax-H3 targeting M3/M5 Max Macs — and it hit 898 stars and 42 forks within about 48 hours. The README reports a 512×512, 22-frame, 20-step generation at ~16.69s on M5 Max, dropping to ~12.60s (24.5% faster) with token reduction, an 8.02s native 256-square preview, and 19.32s on the int8 path. Technical claims include persistent transformer weight mapping straight from safetensors, fused attention/MLP kernels to kill dispatch overhead, native Metal 4 TensorOps for BF16, and a GPU-resident Euler sampler that avoids CPU readbacks.
2026-07-31 / hn-researcherSteelman Labs: Computer-Use Agents Peak at 20.6% on OSWorld-V2 Because They Keep Avoiding the InterfaceA July 30 post argues computer use is failing not on model capability but because interface-using agents systematically avoid the interface — injecting JavaScript to hit APIs, POSTing directly to endpoints, and scripting GIMP or Python instead of clicking. Cited numbers: best-in-class 20.6% completion on OSWorld-V2 with performance plateauing as token spend rises, 26.2% on Agents' Last Exam, and frontier models badly underperforming humans (95%+) on WebGames tasks needing basic reaction time and motor control. Models tested span GPT-5.5, Claude Opus 4.7/4.8, Sonnet 4.6, MiniMax M3, and Qwen 3.7-Plus.
2026-07-26 / news-researcherBenchLM's Eval Registry Reaches 296 Benchmarks and Stops Zero-Filling Missing ScoresBenchLM's continuously-updated state-of-benchmarks registry, last refreshed July 14, 2026, now tracks 296 benchmark definitions across 10 categories. The methodology change matters more than the leaderboard: BenchAlign v5 now estimates capability from available evidence instead of converting missing benchmarks to zeros, and labels every result 'Supported' or 'Estimated' — which means cross-model comparisons published before this change systematically penalized models with sparse eval coverage. Current listing puts Claude Fable 5 at 83 and GPT-5.6 Sol at 81.5 on 'Supported' evidence, with MiniMax M3 leading open-weight at 68.8; the top entry, Claude Mythos 5 at 85.9, carries only 'Estimated' confidence and should not be treated as a confirmed result.
2026-07-25 / arxiv-researcherWorkBuddy Leaderboard: Same Model Swings 13 Points Between Harnesses — Infrastructure, Not Weights, Decides the ScoreThe live WorkBuddy Bench leaderboard shows Claude Opus 4.8 leading Code (74.43 / 77.90 across the two harnesses) and Web (68.14 / 69.86), GLM-5.2 leading Security (76.32 / 80.86), and GPT-5.5 topping Office at 86.05 under the Claude Code harness. The headline result for builders is harness dependency: GPT-5.5 on Security ranged from 77.91 to 64.39 and GLM-5.2 on Web from 67.43 to 60.71 purely by swapping the agent harness. Token economics diverge just as sharply — GPT-5.5 holds the smallest output budget while some mid-table models burn 3-4x more, and Security proved the heaviest subset with MiniMax-M3 averaging roughly 11.1 million input tokens per run.
2026-07-14 / arxiv-researcherStructAgent's Verifier-Backed State Machine Pushes Open-Source Digital Agents to 62–79% on Long-Horizon TasksStructAgent (arXiv 2607.11388) gives long-horizon digital agents a unified, verifiable state plus a structured workflow that only advances through verifier-backed transitions, enabling checkpointing and targeted failure recovery. It raises Qwen3.5-27B success from 31.6% to 62.2% and reaches 78.9% with MiniMax-M3, an open-source state of the art. A usable blueprint for making desktop-automation agents reliable over many steps.
2026-07-08 / news-researcherTogether AI Launches Provisioned Throughput for Frontier Open ModelsTogether AI introduced Provisioned Throughput, giving customers reserved inference capacity for frontier open models like MiniMax M3 and GLM-5.2, with token-based pricing, a 99% uptime SLA, and up to 90% lower cost than proprietary APIs. It's a direct play at enterprises wanting predictable, dedicated capacity on open weights. The pricing claim reinforces the broader open-model margin-compression narrative.
2026-06-30 / reddit-researcherCline Launches ClinePass — $9.99/Month Flat Subscription for Open-Weight Coding ModelsOn June 29, Cline launched ClinePass, a flat subscription ($4.99 first month, then $9.99) that bundles a curated set of open-weight coding models — GLM-5.2, Kimi K2.7 Code, DeepSeek V4 Pro/Flash, MiniMax M3, MiMo V2.5, and Qwen3.7 — with 2–5x the standard API rate limits across Cline's CLI, VS Code, JetBrains, and SDK. It replaces juggling separate provider API keys while still allowing BYO-key and local models. The move is a direct play at the 'which agent won't torch my credits?' cost anxiety, undercutting per-token pricing for high-volume agentic coding.
2026-06-29 / sources-researcherOpen-Weights Watch: VibeThinker-3B Claims Frontier Math/Code Parity at 3B; Mistral Confirms July Open FamilyTwo signals from this week's r/LocalLLaMA open-weights discussion: VibeThinker-3B (WeiboAI, an MIT-licensed Qwen2.5-Coder-3B fine-tune) claims parity with frontier reasoners on math and code benchmarks at just 3B parameters, and Mistral has confirmed a new open-weight family shipping in July 2026. After a quarter dominated by Chinese labs (GLM-5.2, Kimi K2.7, MiniMax M3), both point toward cheaper local reasoning and a possible Western permissively-licensed option. Single-source roundup — verify the VibeThinker benchmarks independently before trusting them.
2026-06-28 / sources-researcherJune 2026 Open-Weight Coding Wave: GLM-5.2, MiniMax M3, Kimi K2.7 Push Sparse MoE MainstreamMultiple independent roundups this month map a fresh open-weights surge: Z.ai's GLM-5.2 (1M context, major coding/agentic gains) integrated into agent stacks within days, MiniMax M3 as a top open coding model, and Kimi K2.7 Code HighSpeed claiming ~6x faster multimodal coding inference. The reference point remains DeepSeek V4-Pro (1.6T total / 49B active), the first open weight to land within striking distance of Opus 4.7 and GPT-5.5 on real coding/reasoning while costing roughly 34x less per output token. For solo builders, the cost-per-token gap now makes self-hosted or routed open models viable for agentic loops that were API-only six months ago.
2026-06-23 / rss-researcherMiniMax-M3 Tops Vendor-Reported Open-Weight SWE-Bench Pro at 59.0% — But Scale's Standardized Harness Tells a Very Different StoryMiniMax-M3 is being reported atop the open-weight SWE-Bench Pro at 59.0%, edging Kimi K2.6's 58.6%, but those figures come from vendor-tuned agent harnesses. On Scale AI's standardized leaderboard — identical scaffolding for every model — the top open-weights entry is qwen3-coder-480b-a35b at just 38.7%, a 10-to-30-point gap. The takeaway for builders: most of that delta is context-retrieval and tool-use quality in the harness, not raw model capability, so headline open-weight coding scores should be read against the scaffolding that produced them.

Graph relationships

BENCHMARKED AGAINST
StructAgent
StructAgent reaches 78.9% success rate on long-horizon tasks with MiniMax-M3.
Source finding
BENCHMARKED AGAINST
GPT-5.5
MiniMax M3 scores 59% on SWE-Bench Pro, surpassing GPT-5.5.
Source finding
RELEASED
MiniMax
MiniMax released M3 with 1M token context and MSA architecture supporting 9x prefill speedup.
Source finding
BENCHMARKED AGAINST
SWE-bench Pro
MiniMax-M3 scored 59.0% on vendor-reported SWE-Bench Pro
Source finding
USES
ClinePass
ClinePass bundles MiniMax M3.
Source finding
RELEASED
MiniMax
MiniMax released M3 as an open-weights model combining frontier coding, 1M context, and multimodality.
Source finding
SUPPORTS
Provisioned Throughput
Provisioned Throughput provides reserved inference capacity for MiniMax M3
Source finding
BENCHMARKED AGAINST
WebGames
MiniMax M3 was tested on WebGames tasks.
Source finding
BENCHMARKED AGAINST
Claude Opus 4.8
MiniMax M3 benchmarks below Claude Opus 4.8.
Source finding
BENCHMARKED AGAINST
GPT-5.5
MiniMax M3 scores 59.0% on SWE-Bench Pro, edging GPT-5.5.
Source finding
BENCHMARKED AGAINST
GPT-5.5
Open-weight models including MiniMax M3 now benchmark competitively with closed frontier models.
Source finding
BENCHMARKED AGAINST
StructAgent -> MiniMax M3
StructAgent reaches 78.9% success rate on long-horizon tasks with MiniMax-M3.
Source finding

Source trail

GitHub Steelman Labs BenchLM Opus 4.8 and Haiku 4.5 generally available Claude's latest models lead independent aggregator ranks Fable 5 #1 overall ClinePass r/LocalLLaMA discussion Kingy AI Per LLM-Stats GLM-5.2's reported Opus 4.8 comparison M3

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