MiniMax-M2.7
Intelligence benchmarks
Artificial Analysis indexes - compared with the best open and proprietary models
Intelligence
49.6
AA Index
Coding
41.9
AA Index
Agentic
61.5
AA Index
Intelligence Index - MiniMax-M2.7 vs. the field
Best open-weight models (you can run locally) and leading proprietary models for context.
Coding Index comparison
Agentic Index comparison
Benchmark data from Artificial Analysis · updated 2026-06-07.
Standard benchmarks
Performance across standard evaluations
| Benchmark | Score |
|---|---|
| GPQA | 87.4 |
Will it run on your hardware?
Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest
Need an exact figure for your context length? Use the VRAM calculator.
Run it locally
Copy-paste - running in under a minute
vllm serve MiniMaxAI/MiniMax-M2.7New to this? Start with Ollama · serve to many users with vLLM.
Deep dive
Notes, sources, and the full write-up
MiniMax-M2.7
MiniMax-M2.7 is a 228.7B-parameter other model from MiniMaxAI. It scores 49.6 on the Artificial Analysis Intelligence Index (coding 41.9). At Q4_K_M it needs roughly 133 GB of VRAM, placing it in the 48gb+ hardware tier.
Specifications
| Spec | Value |
|---|---|
| Parameters | 228.7B |
| Context length | 205K tokens |
| License | other |
| Modalities | text |
| Released | 2026-04-09 |
| Weights | MiniMaxAI/MiniMax-M2.7 |
Benchmarks
Artificial Analysis Intelligence Index - MiniMax-M2.7 vs. leading closed models:
| Model | Intelligence | Coding | GPQA |
|---|---|---|---|
| MiniMax-M2.7 | 49.6 | 41.9 | 87.4 |
| GPT-5.5 (xhigh) | 60.2 | 59.1 | 93.5 |
| Claude Opus 4.8 (max) | 61.4 | 56.7 | 92 |
| Gemini 3.1 Pro Preview | 57.2 | 55.5 | 94.1 |
| Grok 4.3 (high) | 53.2 | 41 | 90.1 |
Source: Artificial Analysis (2026-06-04).
VRAM requirements
| Quant | VRAM | Runs on |
|---|---|---|
| Q4_K_M | ~133 GB | multi-GPU / datacenter |
| Q5_K_M | ~162 GB | multi-GPU / datacenter |
| Q8_0 | ~245 GB | multi-GPU / datacenter |
| FP16 | ~457 GB | multi-GPU / datacenter |
VRAM is estimated from parameter count; MoE models still need all weights resident.
How to run
vLLM:
vllm serve MiniMaxAI/MiniMax-M2.7Popularity
MiniMax-M2.7 has 2,357,142 downloads in the last month on HuggingFace and 1,185 likes.
Frequently asked
Quick answers to common questions
How much VRAM does MiniMax-M2.7 need?
MiniMax-M2.7 with 228.7B parameters needs approximately 133 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is MiniMax-M2.7 better than other MiniMaxAI models?
MiniMax-M2.7 has 228.7B parameters with 204,800 context - a strong choice for general use.
What license is MiniMax-M2.7 under?
MiniMax-M2.7 is released under the other license, making it suitable for most commercial and personal projects.
What hardware runs MiniMax-M2.7 well?
With 228.7B parameters, MiniMax-M2.7 requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.
What is the best quantization for MiniMax-M2.7?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~162 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.
How long can MiniMax-M2.7's context window handle?
MiniMax-M2.7 supports a 204,800-token context window - enough for very long documents, codebases, or multi-turn conversations. Real-world usable context may vary by implementation.
What models compete with MiniMax-M2.7?
MiniMax-M2.7 competes with other 114B–343B. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
Nearby options
Similar models and compatible hardware by spec
Comments coming soon
Configure NEXT_PUBLIC_GISCUS_REPO_ID and NEXT_PUBLIC_GISCUS_CATEGORY_ID at giscus.app to enable.