API cost decision in 10 seconds

R1 0528 vs MiniMax M2.1

Pick MiniMax M2.1 when budget and context both matter.

Page updated:  Data confirmed:  Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick MiniMax M2.1 when budget and context both matter.

On the standard 1M input plus 500K output workload, MiniMax M2.1 is estimated at $0.76 vs $1.57 for R1 0528, saving $0.81 (51.4% lower).

Cost-first pickMiniMax M2.1
Context-first pickMiniMax M2.1
Sample savings$0.8151.4%
10x traffic gap$8.1

MiniMax M2.1 is cheaper on the standard workload and also has the larger context window. At 10x that traffic, the same price gap is about $8.1. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

MiniMax M2.1 stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shapeToken mixBetter pickR1 0528MiniMax M2.1
Input-heavy / RAG5M input + 500K outputMiniMax M2.1$3.58$1.92
Balanced workload1M input + 1M outputMiniMax M2.1$2.65$1.24
Output-heavy chatbot1M input + 5M outputMiniMax M2.1$11.25$5.04
Cheaper input MiniMax M2.1 $0.5 vs $0.29 / 1M

MiniMax M2.1 is $0.21 cheaper per 1M input tokens (42% lower; 1.72x difference).

Cheaper output MiniMax M2.1 $2.15 vs $0.95 / 1M

MiniMax M2.1 is $1.2 cheaper per 1M output tokens (55.8% lower; 2.26x difference).

Larger context MiniMax M2.1 163.84K vs 204.8K

MiniMax M2.1 has 40.96K more context (1.25x larger).

Sample workload MiniMax M2.1 $1.57 vs $0.76

MiniMax M2.1 is $0.81 cheaper on the standard workload (51.4% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.
R1 0528 Calculating… Estimated API cost
MiniMax M2.1 Calculating… Estimated API cost
Cheaper for this workload Calculating… Difference: calculating…

This estimate uses normalized public API pricing per 1M tokens. It is a planning aid, not a billing quote. Verify provider pricing, limits, and terms before production use.

Quick Decision

Verdict

MiniMax M2.1 has the lower input price; MiniMax M2.1 has the lower output price; MiniMax M2.1 offers the larger context window. For the 1M input plus 500K output sample, MiniMax M2.1 is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $1.57 for R1 0528 and $0.76 for MiniMax M2.1.

Best Fit

Choose R1 0528 when its provider, model quality, latency, or availability is more important than the numeric price/context winner.

Choose MiniMax M2.1 when you care most about lower input-token price, lower output-token price, and larger context window.

Decision Notes
  • On the standard 1M input plus 500K output workload, MiniMax M2.1 is estimated at $0.76 vs $1.57 for R1 0528, saving $0.81 (51.4% lower).
  • MiniMax M2.1 is $0.81 cheaper on the standard workload (51.4% lower).
  • MiniMax M2.1 is $0.21 cheaper per 1M input tokens (42% lower; 1.72x difference).
  • MiniMax M2.1 is $1.2 cheaper per 1M output tokens (55.8% lower; 2.26x difference).
  • MiniMax M2.1 has 40.96K more context (1.25x larger).
Head-to-Head Specs
FeatureR1 0528
(DeepSeek)
MiniMax M2.1
(MiniMax)
Input Price
prompt tokens per 1M
$0.5$0.29
Completion Price
per 1M tokens
$2.15$0.95
Sample Workload Cost
1M input + 500K output
$1.57$0.76
Context Window163.84K204.8K
Release Date
Popularity#106#118

Use-Case Decision Matrix

Use caseBetter pickWhy
Budget-constrained productionMiniMax M2.1On the standard 1M input plus 500K output workload, MiniMax M2.1 is estimated at $0.76 vs $1.57 for R1 0528, saving $0.81 (51.4% lower).
High-volume input processingMiniMax M2.1Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbotsMiniMax M2.1Lower output-token price matters most when assistants generate many completion tokens.
RAG or long-document workMiniMax M2.1A larger context window leaves more room for retrieved passages, conversation history, or source files.

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Larger context alternatives

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Provider catalogs

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DeepSeek catalog

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MiniMax catalog

Check other MiniMax models with comparable pricing, context, or release timing.

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R1 0528

May 28th update to the [original DeepSeek R1](/deepseek/deepseek-r1) Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active...

MiniMax M2.1

MiniMax-M2.1 is a lightweight, state-of-the-art large language model optimized for coding, agentic workflows, and modern application development. With only 10 billion activated parameters, it delivers a major jump in real-world...