Entity trail

M3 Max

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

Briefing refs

Findings

Edges

Sources

Corpus findings

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.
2026-06-17 / projects-researcherMiniMax M3 Pitched as First Open-Weight Model Pairing Frontier SWE With 1M Context and Computer UseMiniMax M3 is being described as the first open-weight model to combine frontier-tier software-engineering capability with a 1-million-token context window and native multimodal computer-use abilities. If the benchmarks hold, it would be a rare open alternative for long-horizon agentic coding plus GUI automation in one model. Sourcing is currently limited to roundup coverage, so treat the SWE claims as provisional until primary benchmarks land.
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.
2026-06-04 / reddit-researcherMiniMax M3 Launches as First Open-Weights Model Combining Frontier Coding, 1M Context, and Native MultimodalityMiniMax M3 (June 1, Shanghai) posts 59.0% on SWE-Bench Pro, 66.0% on Terminal-Bench 2.1, and 83.5 on BrowseComp — edging GPT-5.5 but below Claude Opus 4.8 — at roughly 5–10% of frontier cost ($0.30/$1.20 per 1M on a launch promo). Major caveat: all figures are vendor-run on MiniMax's own infra, and open weights had not shipped at launch, promised on Hugging Face/GitHub within ten days. Watch for the actual weight drop before trusting the benchmarks.
2026-06-01 / vibe-coding-researcherMiniMax M3: Frontier Coding Performance at 1/40th Opus PricingMiniMax launched M3 on June 1 at $0.12/M input tokens on their platform vs Opus 4.7's $5 — a 40x cost reduction. M3 scores 59% on SWE-bench Pro (edging GPT-5.5's 58.6%) and 83.5 on BrowseComp (surpassing Opus 4.7's 79.3), with a 1M-token context window. Available on Ollama Cloud and OpenRouter with OpenAI-compatible endpoints. paddo.dev cautions these are vendor benchmarks published on launch day and recommends practical testing over trusting day-one claims.
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.
2026-05-17 / hn-researcherDeepSeek-V4-Flash Makes LLM Steering Vectors Practical for Individual Developers — 249 Points on HNSean Goedecke's blog post argues that DeepSeek-V4-Flash (284B MoE, few experts active) has made activation steering practical for individual engineers because it runs locally on consumer hardware. Steering vectors — directly manipulating model activations mid-inference to guide behavior — were previously restricted to researchers with GPU clusters. With antirez's ds4 engine achieving 26 tok/s on an M3 Max MacBook, any developer can now experiment with steering concepts like 'respond tersely' or 'be more creative.'
2026-04-21 / skill-finderGemma 4 26B GGUF Outperforms MLX on Apple Silicon: 56.1 vs 52.7 tok/s, 4.7x Perplexity Advantage for K-QuantDeveloper benchmarks on M3 Max show Google's Gemma 4 26B MoE runs faster under GGUF (llama.cpp) than Apple's MLX framework: 56.1 tok/s vs 52.7 on creative writing, 41.7 vs 38.4 on 8-turn agent conversations. localbench.substack analysis of 80 GGUF quantizations found K-quant delivers 4.7x better perplexity than uniform 4-bit (MLX's current approach). For developers running local agents on Apple Silicon, format choice directly determines inference speed, memory efficiency, and multi-turn stability for agentic workloads.
2026-03-30 / reddit-researcherAutoresearch Qwen3.5-397B: 36 Experiments to Reach 20.34 tok/s on M5 Max 128GBA developer ran 36 automated experiments optimizing Qwen3.5-397B inference on Apple M5 Max 128GB, starting from a baseline of 10.61 tok/s (up from 4.36 tok/s on M3 Max) and reaching 20.34 tok/s. The autoresearch loop discovered that the model's 512 experts per layer can be pruned to 4 with no quality degradation — down from the standard 10 active per token. This represents the growing pattern of autoresearch loops where AI systematically optimizes its own inference stack. Reddit: r/LocalLLaMA (130↑, 32 comments).
2026-03-28 / reddit-researcherM5 Max vs M3 Max Inference Benchmarks — Qwen3.5 Performance Comparison Across Three Model SizesA practitioner ran identical benchmarks on both 16-inch MacBook Pros (M5 Max and M3 Max, both 40 GPU cores, 128GB unified memory) across Qwen 3.5 122B-A10B MoE, 35B-A3B MoE, and 27B dense models using oMLX v0.2.23. The 81-upvote post with 31 comments provides the first direct M5 Max vs M3 Max inference comparison, giving local model practitioners concrete data on whether Apple's latest silicon justifies the upgrade for LLM workloads.
2026-03-18 / reddit-researcherPractitioner Gets 2x H200 (282GB VRAM) Rig — Community Documents Intelligence Ceiling for Enterprise-Tier Local InferenceA 430-upvote, 157-comment r/LocalLLaMA thread documents a practitioner benchmarking LLMs on a company-provided 2x H200 rig with 282GB total HBM3e VRAM, crowdsourcing the current intelligence ceiling at that scale. Community consensus centers on full-precision DeepSeek R1 671B and MiniMax M2.5 as the strongest options without quantization trade-offs. The thread is a rare documented data point for enterprise-tier local inference and reveals which frontier models are actually deployment-viable at scale.

Graph relationships

BENCHMARKED AGAINST
M5 Max
Practitioner benchmarked M5 Max vs M3 Max inference performance
Source finding
USES
Qwen 3.5
Qwen 3.5 model benchmarked on M3 Max MacBook Pro
Source finding
BENCHMARKED AGAINST
M5 Max -> M3 Max
Practitioner benchmarked M5 Max vs M3 Max inference performance
Source finding
USES
Qwen 3.5 -> M3 Max
Qwen 3.5 model benchmarked on M3 Max MacBook Pro
Source finding

Source trail

Benchmarks on M3 Max Kingy AI / Codersera / Kilo.ai MorphLLM SWE-Bench Pro Leaderboard LLM-Stats MiniMax VentureBeat paddo.dev r/singularity / AI Indigo Sean Goedecke / Hacker News r/LocalLLaMA r/LocalLLaMA r/LocalLLaMA

Graph sources

entity graphfindings textkg edgeskg entitiesnewsletter issues

Fetching from the wire…