ModelPriceWatch.com
Last scan 2026-08-09 Models tracked 198 Providers 30 Cheapest paid Granite 4.0 Micro $0.017/Mtok in Every price links to its source

MiniMax-M2

by MiniMax

Retired budget open weights cheap tier
Retired on Jul 28, 2026. No longer served on the endpoint we price — the figures below are kept for historical reference only. Replaced by MiniMax-M2.7. Full retirement status & replacements →
Availability: MiniMax's Pay-as-You-Go pricing page (2026-07-28) no longer lists a plain 'MiniMax-M2' row; its active text LLMs are MiniMax-M3 and MiniMax-M2.7 (M2.1/M2.5 sit under Legacy). LiteLLM still carries minimax/MiniMax-M2 at $0.30/$1.20, so the number is a real historical price, but MiniMax-M2 is no longer offered first-party — treat it as superseded by MiniMax-M2.7. This is a lifecycle reclassification; the price itself did not change.

Today's price · per 1M tokens

Input

$0.300

per 1M tokens

Output

$1.20

per 1M tokens

Blended

$0.525

blended $/1M — 3:1 weighted input:output

Cached input

$0.030

10% of input — prompt caching

Source: official MiniMax pricing · read Jul 28, 2026 MODELPRICEWATCH.COM · 2026-08-09

Overview

MiniMax M2 base model. $0.30/$1.20 per 1M; cached $0.03. Superseded by MiniMax-M2.7.

Run it yourself

Deploy this open model on rented GPUs

Open weights mean you can self-host instead of paying per-token API prices. These platforms let you serve it on demand — often cheaper at scale.

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Capabilities

struck through = not supported
Input 1/5
Text ✓ Image Audio Video PDF
Output 1/5
Text ✓ Image Audio Video Embedding
Features 3/9
Prompt caching ✓ Reasoning Coding Fast inference Long context ✓ Open weights ✓ Multimodal Web search Realtime

Benchmark performance

accuracy % · higher is better
Percentile vs all tracked models 13.1th2 independent measurements
Percentile per $/Mtok 25.2

We hold no per-benchmark accuracy scores for this model yet, so it has no accuracy average. That is a gap in our coverage, not a sign the model is untested — independent evaluators often publish a composite index for a new model long before releasing its per-benchmark numbers. The percentile above is its standing across the independent composites below.

Independent composite scores
  • AA-Omniscience Index: -46.5 (−100–100)
  • LMSYS Chatbot Arena: 1346 ELO (Human preference)
How it stacks up
  • Ranks #124 of 150 comparably-measured models by percentile score, across 2 independent measurements
  • Ranks #97 of 150 comparably-tested models by normalized performance per dollar

Source: LMSYS Chatbot Arena (UC Berkeley) · updated Jun 29, 2026 · See full rankings →

Specifications

Provider
MiniMax
Context window
205K tokens
Modality
text
Parameters
Proprietary
Open source
Yes — open weights available
Released
Jan 1, 2025
Status
Retired ended Jul 28, 2026
Last updated
Jul 28, 2026
Tags
open-weightsbudgetcachingretired

Availability verified: Jul 28, 2026 — per MiniMax's own deprecation notice