MiniMax-M2
MiniMax · moe · 229B parameters · 196,608 context
Parameters
229B
Context Window
192K tokens
Architecture
MoE
Best GPU
B200 SXM
Cheapest API
$1.10/M
Intelligence Brief
MiniMax-M2 is a 229B parameter Mixture-of-Experts (256 experts, 8 active) model from MiniMax, featuring Grouped Query Attention (GQA) with 62 layers and 3,072 hidden dimensions. With a 196,608 token context window, it supports tools, structured output, code, math, multilingual, reasoning. The most cost-effective API deployment is via minimax at $1.10/M output tokens. For self-hosted inference, B200 SXM delivers optimal throughput at $8522/month.
Provider pricing
1 provider · canonical: minimax| Provider | Input $/M | Output $/M ▲ | Notes |
|---|---|---|---|
| minimaxcanonical | $0.300 | $1.10 | cheapest input · cheapest output |
Prices update via the nightly pricing cron + admin approvals at /admin/ingest-queue. The leaderboard's Input/Output cells show the canonical rate above; this table shows the full spread.
Recent changes
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Picks: same family first, then same vendor within ±2× params, then top tag-overlap matches. Price shown is the cheapest Output $/M across providers — the row's page shows the canonical anchor.
Architecture Details
Memory Requirements
BF16 Weights
458.0 GB
FP8 Weights
229.0 GB
INT4 Weights
114.5 GB
Fits on (single GPU) — most practical first
Fits on (multi-GPU with Tensor Parallelism)
Multi-GPU configurations use Tensor Parallelism (TP) to split model layers across GPUs. Requires NVLink or NVSwitch interconnect for optimal performance.
GPU Compatibility Matrix
MiniMax-M2 is compatible with 8% of GPU configurations across 41 GPUs at 3 precision levels.
GPU Recommendations
FP8 · 2 GPUs · tensorrt-llm
100/100
score
Throughput
280.0 tok/s
Latency (ITL)
3.6ms
Est. TTFT
1ms
Cost/Month
$8522
Cost/M Tokens
$11.58
FP8 · 2 GPUs · tensorrt-llm
100/100
score
Throughput
280.0 tok/s
Latency (ITL)
3.6ms
Est. TTFT
1ms
Cost/Month
$8541
Cost/M Tokens
$11.61
FP8 · 2 GPUs · tensorrt-llm
100/100
score
Throughput
280.0 tok/s
Latency (ITL)
3.6ms
Est. TTFT
1ms
Cost/Month
$12337
Cost/M Tokens
$16.77
Deployment Options
API Deployment
minimax
$1.10/M
output tokens
Single GPU
B200 NVL (pair)
$9965/mo
Min VRAM: 229 GB
Multi-GPU
B200 SXM x2
280.0 tok/s
TP· $8522/mo
API Pricing Comparison
| Provider | Input $/M | Output $/M | Badges |
|---|---|---|---|
| minimax | $0.30 | $1.10 | Cheapest |
Cost Analysis
| Provider | Input $/M | Output $/M | ~Monthly Cost |
|---|---|---|---|
| minimaxBest Value | $0.30 | $1.10 | $7 |
Cost per 1,000 Requests
Short (500 tok)
$0.37
via minimax
Medium (2K tok)
$1.48
via minimax
Long (8K tok)
$4.60
via minimax
Performance Estimates
Throughput by GPU
VRAM Breakdown (B200 SXM, FP8)
Capabilities
Features
Supported Frameworks
Supported Precisions
Where to Deploy MiniMax-M2
Self-Hosted Infrastructure
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Frequently Asked Questions
How much VRAM does MiniMax-M2 need for inference?
MiniMax-M2 requires approximately 458.0 GB of VRAM at BF16 precision, 229.0 GB at FP8, or 114.5 GB at INT4 quantization. Additional VRAM is needed for KV-cache (253952 bytes per token) and activations (~0.00 GB).
What is the best GPU for MiniMax-M2?
The top recommended GPU for MiniMax-M2 is the B200 SXM (x2) using FP8 precision. It achieves approximately 280.0 tokens/sec at an estimated cost of $8522/month ($11.58/M tokens). Score: 100/100.
How much does MiniMax-M2 inference cost?
MiniMax-M2 API inference starts from $0.30/M input tokens and $1.10/M output tokens. Self-hosted inference costs depend on your GPU configuration — use our ROI calculator for a detailed breakdown.