MiniMax AI Models 2026: M2, M3 & H3 Complete Guide
Introduction to MiniMax AI
MiniMax has established itself as one of the most ambitious Chinese AI labs, pioneering ultra-long-context Mixture-of-Experts (MoE) architectures. The MiniMax M-series introduced the industry's first widely deployed 1-million-token context window, a breakthrough that made repository-scale code understanding, book-length document analysis, and multi-hour video reasoning practical for the first time.
MiniMax models are a favorite among developers who need enormous context without sacrificing speed — their hybrid sparse attention layers deliver high-throughput prefill and decode even at extreme context lengths, and quantized GGUF builds are popular for local deployment.
The MiniMax Model Family at a Glance
| Model | Total Params | Active Params | Context | Focus |
|---|---|---|---|---|
| MiniMax M3 | 428B | 23B | 1M tokens | Flagship, native multimodal |
| MiniMax M2.7 | ~356B | ~10B | 1M tokens | Reasoning & agents |
| MiniMax M2.5 | ~356B | ~10B | 1M tokens | Balanced upgrade |
| MiniMax M2.1 | 356B | 10B | 1M tokens | Open-weight base |
| MiniMax M2 | 356B | 10B | 200K (1M beta) | Original M-series |
| MiniMax H3 | ~465B | ~30B | 128K | High-performance MoE |
MiniMax M2: The 1M-Context Pioneer
MiniMax M2 (356B total / 10B active) was the model that made million-token context a mainstream feature in open-weight AI. Its Mixture-of-Experts design activates just 10B parameters per token, making it dramatically more efficient than dense models of similar capability. The hybrid sparse-attention architecture keeps prefill and decode fast even at extreme context lengths, and it delivers strong coding and agentic performance for its size.
Why the 10B active count matters for local use: a Q4 quantization of M2's active weights is small enough to run on a single consumer GPU, while the 1M context window means you can load entire codebases or book collections into the context — something no dense local model could attempt.
MiniMax M2.1, M2.5 and M2.7: Iterative Refinement
MiniMax quickly iterated on M2, releasing M2.1 (open-weight base), M2.5 (balanced upgrade with improved reasoning and instruction following), and M2.7 (agent-focused with stronger tool use and long-horizon planning). Each iteration kept the 1M-token context while improving benchmark scores across reasoning, coding, and multilingual tasks. The M2.x family remains the sweet spot for local 1M-context deployments.
MiniMax M3: The Multimodal Flagship
MiniMax M3 scales the family to 428B total parameters (~23B active) and adds native multimodal input — image and video understanding alongside text. Its specialized sparse attention layers sustain 1M-token context with high throughput, and its benchmark results place it among the very best open-weight models in both reasoning and coding.
Key Benchmark Results:
- GPQA: 92.9% — among the highest science-reasoning scores for open models
- HLE (Humanity's Last Exam): 37.1%
- Artificial Analysis Intelligence Index: 44.4
- Artificial Analysis Coding Index: 58.6
- SciCode: 45.4%
MiniMax H3: High-Performance MoE
MiniMax H3 (roughly 465B total / ~30B active) targets the high-performance end of the spectrum with a denser active-parameter budget than the M-series. It trades some context length (128K) for stronger raw capability, making it a strong choice for complex reasoning and generation workloads where massive context is less critical.
Running MiniMax Models Locally
- 16–32GB RAM: Q4_K_M quantizations of MiniMax M2 / M2.1 / M2.5 — 1M context, runnable on consumer hardware
- 32–64GB RAM: M2.7 and M3 Q4/Q5 builds, or GPU offload for faster generation
- GPU (16GB+ VRAM): fast local inference with partial offload; ideal for 1M-context agent workflows
You can find GGUF quantizations of MiniMax M2, M2.1, M2.5, M2.7, M3, and H3 in the model repository.
Conclusion
MiniMax has carved out a unique position with its 1-million-token context MoE family. For developers who need to feed entire codebases, document archives, or long media into a single context window — while keeping active-parameter counts low enough for local inference — the M-series is arguably the most practical choice in open-weight AI today. And with M3 pushing GPQA past 92%, the family's raw intelligence keeps pace with its headline context-window advantage.