Entity trail
MiniMax M3
Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.
Briefing refs
7
Findings
13
Edges
0
Sources
22
Corpus findings
- 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.
- 2026-06-19 / thought-leaders-researcherSimon Willison Calls Z.ai's GLM-5.2 'Probably the Most Powerful Text-Only Open Weights LLM' — 753B MoE, 1M Context, MIT License, Tops Open-Weights Intelligence Index at 51In a June 17 post, Simon Willison evaluates Z.ai's newly released GLM-5.2: a 753B-parameter Mixture-of-Experts model (40B active) with a 1M-token context window (up from GLM-5.1's 200K), released under an MIT license at roughly $1.40/$4.40 per million input/output tokens on OpenRouter. Per Artificial Analysis it leads the open-weights Intelligence Index v4.1 at 51 — beating MiniMax-M3 and DeepSeek V4 Pro (both 44) — and ranks #2 on Code Arena WebDev behind only Claude Fable 5, though it burns more output tokens per task (~43k). Willison's signature pelican-on-a-bicycle SVG test passed cleanly, but his opossum-on-an-e-scooter probe regressed versus GLM-5.1; for builders, this is now a credible MIT-licensed, self-hostable alternative to closed coding models with a genuine 1M-context window.
- 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.
- 2026-06-10 / projects-researcherNVIDIA RTX Spark Superchip Hits Availability, Running 120B Local Models at 1M-Token ContextThe NVIDIA RTX Spark Superchip reached availability as a CPU+GPU part with up to 128 GB of unified memory, capable of running local models up to 120 billion parameters with 1-million-token context windows. It materially lowers the bar for self-hosting frontier-scale open-weight models like MiniMax M3 on a single machine. For builders, it shifts large-context local inference from datacenter-only to a desktop-class device.
- 2026-06-08 / vibe-coding-researcherPattern: Open-Weight Frontier Coding Models Are Now Benchmark-Competitive With Closed FrontierMiniMax M3 beating GPT-5.5 and Gemini 3.1 Pro on SWE-Bench Pro, alongside Kimi K2.6 and GLM-5.1 being recommended for hard agentic coding and shipping in Ollama, marks a clear convergence: open-weight models now clear the bar for production agentic coding rather than trailing it. The implication for builders is real optionality — local/self-hosted agent stacks and cost-driven model routing are no longer a quality compromise.
- 2026-06-08 / vibe-coding-researcherTip: Route Long-Context and Subtask Work to Cheap Open-Weight Coding ModelsWith MiniMax M3 (1M context, 59.0% SWE-Bench Pro), Kimi K2.6, and GLM-5.1 all now runnable via Ollama or low-cost APIs, builders can keep a frontier closed model for top-level reasoning while routing bulk subtasks — search summarization, boilerplate, long-context grunt work — to a cheaper open-weight model. Heterogeneous routing like this is increasingly viable now that open models clear 70-90% on agentic coding benchmarks with the right harness.
- 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.
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