MiniMax-M2.5 Intelligence, Performance & Price Analysis
Model summary
Intelligence
Artificial Analysis Intelligence Index
Speed
Output tokens per second
Input Price
USD per 1M tokens
Output Price
USD per 1M tokens
Verbosity
Output tokens from Intelligence Index
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
| Reasoning | Yes 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 window | 205k ~307 A4 pages of size 12 Arial font |
MiniMax-M2.5 is amongst the leading models in intelligence and reasonably priced when comparing to other open weight models of similar size. It's also faster than average, however somewhat verbose. The model supports text input, outputs text, and has a 205k tokens context window.
MiniMax-M2.5 scores 42 on the Artificial Analysis Intelligence Index, placing it well above average among comparable models (averaging 25). When evaluating the Intelligence Index, it generated 56M tokens, which is somewhat verbose in comparison to the average of 14M.
Pricing for MiniMax-M2.5 is $0.30 per 1M input tokens (moderately priced, average: $0.55) and $1.20 per 1M output tokens (moderately priced, average: $1.68). In total, it cost $124.58 to evaluate MiniMax-M2.5 on the Intelligence Index.
At 81 tokens per second, MiniMax-M2.5 is faster than average (51).
Intelligence
Artificial Analysis Intelligence Index
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.
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Artificial Analysis Intelligence Index by Open Weights / Proprietary
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).
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Intelligence Evaluations
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
Intelligence Index Comparisons
Intelligence vs. Price
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
The number of tokens required to run all evaluations in the Artificial Analysis Intelligence Index (excluding repeats).
Cost to Run Artificial Analysis Intelligence Index
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
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).
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Pricing
Pricing: Input and Output Prices
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)
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-M2.5 API Providers
Speed
Measured by Output Speed (tokens per second)
Output Speed
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).
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Output Speed vs. Price
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
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 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
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.
Comparisons to MiniMax-M2.5
- MiniMax-M2.5
gpt-oss-120B (high)- gpt-oss-20B (high)
- GPT-5.2 (xhigh)
- GPT-5.2 Codex (xhigh)
Llama 4 Maverick
Gemini 3 Pro Preview (high)- Gemini 3 Flash
Claude Opus 4.5- Claude 4.5 Haiku
- Claude 4.5 Sonnet
- Claude Opus 4.6 (Adaptive)
- Claude Opus 4.6
Mistral Large 3
DeepSeek V3.2
Grok 4.1 Fast- Grok 4
Nova 2.0 Pro Preview (medium)- MiniMax-M2.1
NVIDIA Nemotron 3 Nano
Kimi K2.5
K-EXAONE
MiMo-V2-Flash (Feb 2026)
KAT-Coder-Pro V1
K2 Think V2
GLM-4.7- GLM-5
Qwen3 235B A22B 2507
FAQ
Common questions about MiniMax-M2.5
MiniMax-M2.5 was released on February 12, 2026.
MiniMax-M2.5 was created by MiniMax.
MiniMax-M2.5 scores 42 on the Artificial Analysis Intelligence Index, placing it well above average among other open weight models of similar size (median: 25).
MiniMax-M2.5 generates output at 80.8 tokens per second (based on MiniMax's API), which is well above average compared to other open weight models of similar size (median: 51.3 t/s).
MiniMax-M2.5 has a time to first token (TTFT) of 1.52s (based on MiniMax's API), which is at the higher end compared to other open weight models of similar size (median: 1.14s).
MiniMax-M2.5 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-M2.5 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-M2.5 generated 56M output tokens, which is somewhat higher than average compared to other open weight models of similar size (median: 14M).
Yes, MiniMax-M2.5 is a reasoning model. It uses extended thinking or chain-of-thought reasoning to work through complex problems before providing an answer.
MiniMax-M2.5 supports text input.
MiniMax-M2.5 supports text output.
No, MiniMax-M2.5 does not support image input. It can only process text.
No, MiniMax-M2.5 is not multimodal. It only supports text input.
MiniMax-M2.5 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-M2.5 is open weights. The model weights are publicly available and can be downloaded for self-hosting.
MiniMax-M2.5 has 230 billion parameters (10 billion active).
MiniMax-M2.5 is a Mixture of Experts (MoE) model with 230 billion total parameters, but only 10 billion active parameters are used during inference.
MiniMax-M2.5 is released under the MIT license. This license allows commercial use. View license →
MiniMax-M2.5 achieves a score of 42 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.
Yes, MiniMax-M2.5 is available via API through 2 providers. Compare API providers →
MiniMax-M2.5 is available through 2 API providers. Compare providers →