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MiniMax has launched a newer model, MiniMax-M2.5, we suggest considering this model instead.

For more information, see Comparison of MiniMax-M2.5 to other models and API provider benchmarks for MiniMax-M2.5.

MiniMax-M2.1 logo

MiniMax-M2.1 Intelligence, Performance & Price Analysis

MiniMax

Open weights model

Released December 2025

Model summary

Intelligence

Artificial Analysis Intelligence Index

39
4 out of 4 units for Intelligence.

Speed

Output tokens per second

46.3
2 out of 4 units for Speed.

Input Price

USD per 1M tokens

$0.30
2 out of 4 units for Input Price.

Output Price

USD per 1M tokens

$1.20
2 out of 4 units for Output Price.

Verbosity

Output tokens from Intelligence Index

58M
3 out of 4 units for Verbosity.

Metrics are compared against models of the same class:

  • Non-reasoning models → compared only with other non-reasoning models
  • Reasoning models → compared across both reasoning and non-reasoning
  • Open weights models → compared only with other open weights models of the same size class:
    • Tiny: ≤4B parameters
    • Small: 4B–40B parameters
    • Medium: 40B–150B parameters
    • Large: >150B parameters
  • Proprietary models → compared across proprietary and open weights models of the same price range, using a blended 3:1 input/output price ratio:
    • <$0.15 per 1M tokens
    • $0.15–$1 per 1M tokens
    • >$1 per 1M tokens
Technical specifications
ReasoningYes

This page shows the reasoning version of this model.

A non-reasoning variant may also exist.

Input modality

Supports: text

Output modality

Supports: text

Context window205k
~307 A4 pages of size 12 Arial font
Total parameters230B
Active parameters10B
Number of parameters active per token during inference
LicenseMIT
Model weightsHugging Face

MiniMax-M2.1 is amongst the leading models in intelligence and reasonably priced when comparing to other open weight models of similar size. It's also slower than average and somewhat verbose. The model supports text input, outputs text, and has a 205k tokens context window.

MiniMax-M2.1 scores 39 on the Artificial Analysis Intelligence Index, placing it well above average among comparable models (averaging 27). When evaluating the Intelligence Index, it generated 58M tokens, which is somewhat verbose in comparison to the average of 17M.

Pricing for MiniMax-M2.1 is $0.30 per 1M input tokens (moderately priced, average: $0.55) and $1.20 per 1M output tokens (moderately priced, average: $1.70). In total, it cost $114.46 to evaluate MiniMax-M2.1 on the Intelligence Index.

At 46 tokens per second, MiniMax-M2.1 is slower than average (50).

Navigation

IntelligenceOpennessIntelligence Index ComparisonsIntelligence Index Token Use & CostContext WindowPricingPerformance SummarySpeedLatencyEnd-to-End Response TimeModel Size (Open Weights Models Only)Comparisons

Intelligence

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.0 incorporates 10 evaluations: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt
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Reasoning models are indicated by a lightbulb icon.

Artificial Analysis Intelligence Index v4.0 includes: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Artificial Analysis Intelligence Index v4.0 incorporates 10 evaluations: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt
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Proprietary
Open Weights
Reasoning models are indicated by a lightbulb icon.

Artificial Analysis Intelligence Index v4.0 includes: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if the weights are available but commercial use is limited (typically requires obtaining a paid license).

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis; Higher is better
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Results claimed by AI Lab (not yet independently verified)
GDPval-AA (Agentic Real-World Work Tasks, (ELO-500)/2000)
Terminal-Bench Hard (Agentic Coding & Terminal Use)
𝜏²-Bench Telecom (Agentic Tool Use)
AA-LCR (Long Context Reasoning)
AA-Omniscience Accuracy (Knowledge)
AA-Omniscience Non-Hallucination Rate (1 - Hallucination Rate)
Humanity's Last Exam (Reasoning & Knowledge)
GPQA Diamond (Scientific Reasoning)
SciCode (Coding)
IFBench (Instruction Following)
CritPt (Physics Reasoning)
MMMU Pro (Visual Reasoning)
Reasoning models are indicated by a lightbulb icon.

While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.

Artificial Analysis Intelligence Index v4.0 includes: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Openness

Artificial Analysis Openness Index: Results

Openness Index assesses model openness on a 0 to 100 normalized scale (higher is more open)
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Intelligence Index Comparisons

Intelligence vs. Price

Artificial Analysis Intelligence Index; Price: USD per 1M Tokens
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Most attractive quadrant
Alibaba
Anthropic
DeepSeek
Google
Kimi
MiniMax
NVIDIA
OpenAI
StepFun
xAI
Xiaomi
Z AI
Reasoning models are indicated by a lightbulb icon.

While higher intelligence models are typically more expensive, they do not all follow the same price-quality curve.

Artificial Analysis Intelligence Index v4.0 includes: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Price per token, represented as USD per million Tokens. Price is a blend of Input & Output token prices (3:1 ratio).

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Intelligence Index Token Use & Cost

Output Tokens Used to Run Artificial Analysis Intelligence Index

Tokens used to run all evaluations in the Artificial Analysis Intelligence Index
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Reasoning Tokens
Answer Tokens
Reasoning models are indicated by a lightbulb icon.

The number of tokens required to run all evaluations in the Artificial Analysis Intelligence Index (excluding repeats).

Cost to Run Artificial Analysis Intelligence Index

Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index
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Input Cost
Reasoning Cost
Output Cost
Reasoning models are indicated by a lightbulb icon.

The cost to run the evaluations in the Artificial Analysis Intelligence Index, calculated using the model's input and output token pricing and the number of tokens used across evaluations (excluding repeats).

Context Window

Context Window

Context Window: Tokens Limit; Higher is better
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Reasoning models are indicated by a lightbulb icon.

Larger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data.

Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).

Pricing

Pricing: Input and Output Prices

Price: USD per 1M Tokens
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Input price
Output price
Reasoning models are indicated by a lightbulb icon.

Price per token included in the request/message sent to the API, represented as USD per million Tokens.

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Intelligence vs. Price (Log Scale)

Artificial Analysis Intelligence Index; Price: USD per 1M Tokens; Inspired by prior analysis by Swyx
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Most attractive quadrant
Alibaba
Anthropic
DeepSeek
Google
Kimi
MiniMax
NVIDIA
OpenAI
StepFun
xAI
Xiaomi
Z AI
Reasoning models are indicated by a lightbulb icon.

While higher intelligence models are typically more expensive, they do not all follow the same price-quality curve.

Artificial Analysis Intelligence Index v4.0 includes: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Price per token, represented as USD per million Tokens. Price is a blend of Input & Output token prices (3:1 ratio).

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

 Pricing Comparison of MiniMax-M2.1 API Providers

Speed

Measured by Output Speed (tokens per second)

Output Speed

Output Tokens per Second; Higher is better
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Reasoning models are indicated by a lightbulb icon.

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Output Speed vs. Price

Output Speed: Output Tokens per Second; Price: USD per 1M Tokens
+ Add model from specific provider
Most attractive quadrant
Alibaba
Anthropic
DeepSeek
Google
Kimi
MiniMax
NVIDIA
OpenAI
StepFun
xAI
Xiaomi
Z AI
Reasoning models are indicated by a lightbulb icon.

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

Price per token, represented as USD per million Tokens. Price is a blend of Input & Output token prices (3:1 ratio).

Latency

Measured by Time (seconds) to First Token

Latency: Time To First Answer Token

Seconds to First Answer Token Received; Accounts for Reasoning Model 'Thinking' time
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Input processing
Thinking (reasoning models, when applicable)
Reasoning models are indicated by a lightbulb icon.

Time to first answer token received, in seconds, after API request sent. For reasoning models, this includes the 'thinking' time of the model before providing an answer. For models which do not support streaming, this represents time to receive the completion.

End-to-End Response Time

Seconds to output 500 Tokens, calculated based on time to first token, 'thinking' time for reasoning models, and output speed

End-to-End Response Time

Seconds to Output 500 Tokens, including reasoning model 'thinking' time; Lower is better
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Input processing time
'Thinking' time (reasoning models)
Outputting time
Reasoning models are indicated by a lightbulb icon.

Seconds to receive a 500 token response. Key components:

  • Input time: Time to receive the first response token
  • Thinking time (only for reasoning models): Time reasoning models spend outputting tokens to reason prior to providing an answer. Amount of tokens based on the average reasoning tokens across a diverse set of 60 prompts (methodology details).
  • Answer time: Time to generate 500 output tokens, based on output speed

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Model Size (Open Weights Models Only)

Model Size: Total and Active Parameters

Comparison between total model parameters and parameters active during inference
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Active Parameters
Passive Parameters
Reasoning models are indicated by a lightbulb icon.

The total number of trainable weights and biases in the model, expressed in billions. These parameters are learned during training and determine the model's ability to process and generate responses.

The number of parameters actually executed during each inference forward pass, expressed in billions. For Mixture of Experts (MoE) models, a routing mechanism selects a subset of experts per token, resulting in fewer active than total parameters. Dense models use all parameters, so active equals total.

Frequently Asked Questions

Common questions about MiniMax-M2.1

MiniMax-M2.1 was released on December 23, 2025.

MiniMax-M2.1 was created by MiniMax.

MiniMax-M2.1 scores 39 on the Artificial Analysis Intelligence Index, placing it well above average among other open weight models of similar size (median: 27).

MiniMax-M2.1 generates output at 46.3 tokens per second (based on MiniMax's API), which is below average compared to other open weight models of similar size (median: 50.3 t/s).

MiniMax-M2.1 has a time to first token (TTFT) of 3.18s (based on MiniMax's API), which is at the higher end compared to other open weight models of similar size (median: 2.31s).

MiniMax-M2.1 costs $0.30 per 1M input tokens (very competitive, median: $0.60) and $1.20 per 1M output tokens (very competitive, median: $2.20), based on MiniMax's API.

MiniMax-M2.1 costs $0.30 per 1M input tokens and $1.20 per 1M output tokens (based on MiniMax's API). For a blended rate (3:1 input to output ratio), this is $0.53 per 1M tokens. Pricing may vary by provider. Compare provider pricing

When evaluated on the Intelligence Index, MiniMax-M2.1 generated 58M output tokens, which is somewhat higher than average compared to other open weight models of similar size (median: 17M).

Yes, MiniMax-M2.1 is a reasoning model. It uses extended thinking or chain-of-thought reasoning to work through complex problems before providing an answer.

MiniMax-M2.1 supports text input.

MiniMax-M2.1 supports text output.

No, MiniMax-M2.1 does not support image input. It can only process text.

No, MiniMax-M2.1 is not multimodal. It only supports text input.

MiniMax-M2.1 has a context window of 200k tokens. This determines how much text and conversation history the model can process in a single request.

Yes, MiniMax-M2.1 is open weights. The model weights are publicly available and can be downloaded for self-hosting.

MiniMax-M2.1 has 230 billion parameters (10 billion active).

MiniMax-M2.1 is a Mixture of Experts (MoE) model with 230 billion total parameters, but only 10 billion active parameters are used during inference.

MiniMax-M2.1 is released under the MIT license. This license allows commercial use. View license

MiniMax-M2.1 achieves a score of 39 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.

Yes, MiniMax-M2.1 is available via API through 7 providers. Compare API providers

MiniMax-M2.1 is available through 7 API providers. Compare providers