MiniMax M3.1 Flash

MiniMax M3.1 Flash is an open-weight multimodal Mixture of Experts (MoE) model released on October 19, 2026. Originally tested anonymously under the codename "Space Bunny", where it processed more than 200 trillion tokens in preview, the model serves as the direct successor to MiniMax M3. It is engineered specifically for software development workflows, multi-step agentic execution, computer use, and long-context processing. MiniMax designed the model to cut inference latency dramatically, claiming online inference runs nearly three times faster than M3 while dropping compute costs by roughly 70 percent.

The model pairs an undisclosed total and active parameter count with a native 1,000,000-token context window. Unlike traditional dense models that execute fixed computation passes per token, M3.1 Flash integrates an always-on reasoning core that developers can tune dynamically across five distinct effort presets (low, medium, high, xhigh, and max). In practical terms, this allows engineers to throttle back internal reasoning chains for low-latency tool execution or dial it up to maximum depth for multi-file code refactoring and mathematical proofs.

Positioned against frontier agentic models, MiniMax M3.1 Flash targets local workstations and private enterprise clusters that demand high-tier coding and autonomous computer interaction without continuous cloud reliance. With MiniMax committing to an open-weight release for production deployments, the model presents a serious target for practitioners building local agent stacks.

MiniMax built M3.1 Flash on a sparse Mixture of Experts (MoE) multimodal architecture. While the provider keeps the precise parameter total and active parameter routing undisclosed, the underlying mechanics follow sparse activation principles. During forward passes, only a subset of specialized expert networks activate for any given token. This architecture decouples total memory footprint from compute throughput: the full parameter weights must reside in VRAM to avoid offloading penalties, but generation speed scales with the smaller active parameter budget.

The model accepts text, image, and video inputs natively within a unified transformer backbone. Instead of relying on an isolated external vision encoder that downsamples frames into coarse embeddings, the multimodal pipeline processes video and visual UI layouts with high token fidelity. This architectural choice directly underpins its computer-use capabilities, allowing the system to inspect dynamic screen captures alongside terminal logs.

Context handling is one of the model's standout technical traits. At 1,000,000 tokens, the memory required for Key-Value (KV) cache storage becomes a central hardware engineering concern. Without quantization, a full 1M-token context window in 16-bit precision requires dozens of gigabytes solely dedicated to the KV cache, completely separate from model weight storage. MiniMax manages this long-context attention through optimized positional embeddings and sparse attention mechanisms that preserve retrieval accuracy across the entire million-token boundary.

The integrated reasoning mechanism runs continuously. Rather than invoking reasoning via explicit system prompt instructions, the model defaults to max reasoning effort at the architecture layer. Practitioners tuning the model for downstream agent loops can scale down this parameter to minimize reasoning token overhead when executing simple API lookups.

MiniMax M3.1 Flash focuses on deterministic execution, autonomous tool orchestration, and long-form code manipulation:

• Software Engineering and Codebases: In standardized evals, the model posted a 73.7% score on DeepSWE v1.1. It is tuned to ingest entire source repositories within its 1M window, identify broken tests across disparate modules, author targeted patches, and self-validate output across multiple reasoning steps. Practitioners targeting MiniMax M3.1 Flash for coding can deploy it for local test-driven development loops and automated pull request auditing.
• Computer Use and OS Navigation: Scoring 61.7 on OSWorld V2 and 29.6 on Agents' Last Exam (ALE), M3.1 Flash leads Chinese foundation models on autonomous operating system benchmarks. It parses visual desktop environments, maps interface hierarchies, issues precise mouse and keystroke coordinates, and recovers from environment state mismatches across multi-application workflows.
• Agent Automation and Financial Compliance: On BankerToolBench, the model achieved a 76.2 rating, demonstrating robust consistency when handling complex JSON schema validation, multi-system SaaS tool chaining, and state-tracking across fragmented legal or financial documents.
• Multimodal Video and Document Inspection: The native video intake allows engineering teams to feed recording logs, UI bug walkthroughs, or architectural diagrams alongside raw stack traces to diagnose system failures without manual frame pre-processing.

Deploying a model with an undisclosed total parameter size requires practical planning around memory bandwidth and VRAM allocation. While the exact parameter footprint remains proprietary, its sparse MoE architecture dictates specific hardware behaviors during local inference.

When preparing to run MiniMax M3.1 Flash locally, hardware allocation splits between weight residency and the dynamic KV cache:

• Q4_K_M Quantization: Estimated baseline weight storage requires between 32 GB and 48 GB of VRAM. For standard context lengths (8k to 32k tokens), a workstation with 48 GB to 64 GB of addressable memory is sufficient. Running deep into the 1,000,000-token context window requires additional memory for the KV cache, demanding either FP8/Q4 KV cache quantization or unified memory architectures scaling beyond 96 GB.
• Q8_0 / FP16 Precision: For full precision or light quantization, the weight footprint scales significantly, requiring dual or quad GPU nodes (e.g., multiple RTX 4090s, dual RTX 6000 Ada, or data-center class H100/A100 accelerators) to keep weights in fast VRAM.

Consumer and workstation hardware setups divide across distinct performance tiers:

• NVIDIA Consumer Hardware: A single GeForce RTX 4090 (24 GB) will struggle to host the full model weights without aggressive quantization or offloading. Dual NVIDIA GeForce RTX 3090 or RTX 4090 GPUs (48 GB total via PCIe) represent the practical entry point for running heavily quantized builds (Q3_K_M or Q4_K_M) at usable speeds for developer tasks.
• Apple Silicon Unified Memory: Mac systems configured with unified memory are exceptionally well-suited for long-context MoE execution. An Apple M3 Max or M4 Max with 64 GB or 128 GB of unified memory, or an M2 Ultra with 192 GB, provides sufficient pooled address space to hold both model weights and expansive KV caches without encountering PCIe bus bottlenecks.
• Enterprise Workstations: Dual NVIDIA RTX 6000 Ada (96 GB total VRAM) or dedicated servers featuring 80 GB A100/H100 cards represent the ideal hardware for production teams running unconstrained context windows with zero layer offload.

The best quantization for MiniMax M3.1 Flash for day-to-day use is Q4_K_M. This 4-bit medium quantization preserves critical reasoning pathways in MoE architectures while slashing the overall memory bandwidth pressure by roughly 70 percent compared to unquantized FP16 weights. Lower tiers like Q3_K_S or IQ2 variants should generally be avoided for this model, as sub-3-bit quantization severely degrades the structured formatting required for JSON function calling and deep code synthesis.

Inference speed heavily depends on MoE efficiency. Because the active parameter count per token is only a fraction of the total footprint, MiniMax M3.1 Flash tokens per second can reach 35 to 55 tokens/second on unified Apple Silicon or multi-GPU configurations under Q4 quantization, provided the memory bus is not saturated by long-context backpropagation. When offloading layers to system RAM over standard DDR5 channels, generation speed will collapse to 2 to 5 tokens per second due to the sheer volume of weights that must be accessed per expert gate.

Once weights are downloaded, the quickest path to local deployment is through Ollama or llama.cpp for local CLI and workstation serving, or vLLM for high-concurrency production endpoints.

For Ollama deployments, configure your Modelfile to pass the appropriate reasoning depth flags:

1FROM ./minimax-m3.1-flash-q4_k_m.gguf
2
3# Set default parameter flags
4PARAMETER temperature 0.2
5PARAMETER top_p 0.95
6PARAMETER num_ctx 32768
7
8# Inject system parameters for thinking effort (low, medium, high, xhigh, max)
9SYSTEM "thinking_effort: medium"

For teams hosting API endpoints across internal networks, vLLM provides tensor-parallel serving with automated PagedAttention management to prevent out-of-memory errors during long context retrieval.

Evaluating MiniMax M3.1 Flash against contemporary open-weight alternatives highlights sharp trade-offs in architecture, memory overhead, and functional specialization.

DeepSeek-V3 is a dominant open-weight MoE architecture featuring 671 billion total parameters with 37 billion active per token.

• Hardware Demands: DeepSeek-V3 requires significant compute clusters (typically multi-node 8xH100 configurations or heavily quantized distributed rigs) to run locally due to its massive 671B footprint. MiniMax M3.1 Flash is designed as a more compact, rapid "Flash" class MoE, allowing it to fit onto substantially smaller multi-GPU workstation footprints.
• Context Capacity: DeepSeek-V3 supports 128,000 tokens natively, whereas M3.1 Flash extends context capacity out to 1,000,000 tokens, giving it an advantage for repository-level debugging and processing extensive video or document logs.
• Reasoning Implementation: DeepSeek decouples its standard chat models from dedicated reasoning models like DeepSeek-R1. MiniMax M3.1 Flash integrates reasoning directly into the core engine with runtime effort flags, unifying general instruction following and reasoning into a single model.

Qwen2.5-Coder-32B is a dense, code-specialized model that has become an industry standard for single-GPU local execution.

• VRAM Footprint: Qwen2.5-Coder-32B easily fits on a single RTX 4090 (24 GB) at Q4 or Q5 quantization, making it significantly easier to deploy for individual developers with standard consumer setups. M3.1 Flash requires a multi-GPU configuration or unified memory Mac to clear weight residency requirements.
• Multimodal and Tool Integration: Qwen2.5-Coder is purely a text-and-code model. M3.1 Flash introduces full visual and video ingestion alongside OS navigation features, making it capable of UI interaction and screen-based debugging that Qwen2.5-Coder cannot perform without auxiliary vision models.
• Agent Workflows: While Qwen excels at immediate syntax generation and function completion, M3.1 Flash demonstrates higher persistence on long-horizon agent tasks, scoring higher on complex multi-step benchmarks like DeepSWE v1.1 and OSWorld V2. Practitioners needing basic autocompletion will prefer Qwen for its lighter hardware footprint, while teams building autonomous agent environments will benefit from the broader tool-chaining and multimodal capabilities of M3.1 Flash.