MiniMax M3.1 Flash in practice: tests and comparisons with DeepSeek V4.1 Flash

The author ran M3.1 Flash via API in DeepSeek Harness and mcode over long sessions, adding features one by one to build a code-only open-world FPS with no external assets, calling it a big step up from M3 and planning to spend the rest of the month's quota improving it.

Maya ships a dedicated engine for the 428B/23B-active M3.1 Flash, running locally on NVIDIA V100 and up with automatic VRAM planning, half-size long-context cache and lazy vision tower; GGUF quants for 128GB PCs are next.

Running DeepSeek V4.1 Flash, Qwen3.8, GLM-5.3 and MiniMax M3.1 on two AMD Strix Halo boxes plus an RTX PRO 6000, the author traces most gains to config: GPU memory clock stuck in P1 costs ~4.5% bandwidth and 1.65x slower first tokens, and pipelined expert loading cut load time from 109s to 62s.

The author ran MiniMax M3.1 Flash end-to-end on four DGX Spark (GB10) nodes with SGLang TP=4, measuring 59 tok/s single-stream decode, 122.9 tok/s at 6-way concurrency, and a passing needle test at 266k tokens.

A developer built Repro: it records console, network and clicks, has M3.1 Flash on Nebius write the bug report, reproduces in the signed-in browser, proposes a fix and verifies it in Docker; a real store bug reproduced in 2 steps with a 1-file fix.

The post cites M3.1 Flash at $0.15 per 1M input and $0.60 per 1M output tokens, urging B2B SaaS founders to revisit their default-model pricing decision and test which customer requests really need premium tokens.

A deployment writeup runs MiniMax M3.1 Flash on four NVIDIA DGX Spark boxes with vLLM TP4 and DSpark speculative decoding, reporting a 300K context, 1.42M-token KV pool and 84/84 requests passed, with scripts and gotchas published.

The post notes MiniMax M3.1 Flash includes vLLM and SGLang recipes with paged attention and continuous batching out of the box, but its community license bars commercial use without permission, suiting research or teams willing to negotiate.

An SGLang-Omni member analyzes the 428B MoE model's sparse attention, quantization-aware training and speculative decoding, citing OSWorld 61.7 and DeepSWE 73.7%.

A vLLM blog post covers day-0 MiniMax M3.1 Flash support with optimized NVIDIA and AMD recipes and AgentX Pareto curves, described as the most comprehensive Rubin day-0 support yet.

A DFlash 2 speculative decoding drafter trained for MiniMax M3.1 Flash is released under Apache 2.0, making generation up to 3.5x faster while remaining lossless.

inco_ai released an Apache 2.0 DFlash 2 drafter for MiniMax M3.1 Flash that speeds up generation up to 3.5x without loss, with a Hugging Face link.