Intelligence
40
score
MiMo-V2.5 scores 40 (estimated) on the Artificial Analysis Intelligence Index, placing it well …
vs class avg: 25
Xiaomi•Open weights•April 22, 2026
MiMo-V2.5
Intelligence, Performance & Price Analysis
Source:Artificial Analysis scrapeType:Open weights
Speed
84.9
output tokens/sec
MiMo-V2.5 generates output at 84.9 tokens per second (based on Xiaomi's API), which is above …
Latency
2.83s
TTFT
MiMo-V2.5 has a time to first token (TTFT) of 2.83s (based on Xiaomi's API), which is at the higher …
Input Price
$0.14
/ 1M tokens
MiMo-V2.5 costs $0.14 per 1M input tokens (very competitive, median: $0.59) and $0. …
Output Price
$0.28
/ 1M tokens
MiMo-V2.5 costs $0.14 per 1M input tokens (very competitive, median: $0.59) and $0. …
Decision Overview
Ethen's intelligence-driven routing assessment for this model
Overall verdict
Frontier research / complex reasoning
Reserve this model for the hardest requests; simpler or repetitive work should stay on cheaper routes.
Best for
- Complex reasoning
- Research synthesis
- Hard coding tasks
- Multi-step agentic workflows
Weak or unsuitable
- High-volume chat
- Simple classification
- Cost-sensitive pipelines
Routing recommendation
1
Use the fit matrix to compare against cheaper routes when volume rises
Cost pressure
Competitive pricing — $0.14 / M input, $0.28 / M output. Cost pressure is low enough for sustained production use.
Model-fit matrix
Use this model where capability pays for itself; route away where volume, latency, or budget dominate.
| Use case | Fit | Reason | Routing note |
|---|---|---|---|
| Complex reasoning & agentic workflows | Excellent | Intelligence score 40 places this model among top performers. Suitable for multi-step analysis and agentic loops. | Route complex tasks here; reserve simpler queries for cheaper models. |
| High-volume chat & customer-facing | Strong | Output speed 84.9 tokens/sec is adequate for chat. | Suitable for chat; monitor latency under concurrent load. |
| Latency-sensitive applications | Moderate | TTFT 2.83s — latency may be noticeable in interactive use. | Consider routing latency-critical paths to faster models. |
| Cost-sensitive pipelines | Excellent | Output pricing at $0.28 is very competitive for high-volume workloads. | Excellent for budget-constrained pipelines; enable caching to reduce costs further. |
Cost pressure
LowPricing is competitive — input $0.14, output $0.28. Suitable for sustained production use.
Cheaper substitutes
No cheaper substitute is supported by the current price data.
Route away when
- Extreme scale where even low costs matter
Highlights
Key benchmark results at a glance.
Intelligence
Intelligence position
Artificial Analysis Intelligence Index · Higher is better · Evaluation results measured independently by Artificial Analysis
MiMo-V2.5 scores 40 (estimated) on the Artificial Analysis Intelligence Index, placing it well above average among other open weight models of similar size (median: 25).
Speed
Speed position
Output tokens per second · Higher is better · Evaluation results measured independently by Artificial Analysis
MiMo-V2.5 generates output at 84.9 tokens per second (based on Xiaomi's API), which is above average compared to other open weight models of similar size (median: 61.7 t/s).
Context, Cost & Pricing
Price/value position
Weighted average cost (USD) per Intelligence Index task · Lower is better · Evaluation results measured independently by Artificial Analysis
Pricing is competitive — input $0.14, output $0.28. Suitable for sustained production use.
Intelligence & Quality
Benchmark scores and quality indices measuring model capability.
AA-Omniscience Index
AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct. · Evaluation results measured independently by Artificial Analysis
Source and methodology are summarized below
Chart source and provenance are listed in Methodology & sources below.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Artificial Analysis Intelligence Index by Open Weights / Proprietary
Artificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR · Evaluation results measured independently by Artificial Analysis
Source and methodology are summarized below
Chart source and provenance are listed in Methodology & sources below.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Artificial Analysis Intelligence Index
Artificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR · Evaluation results measured independently by Artificial Analysis
Source and methodology are summarized below
Chart source and provenance are listed in Methodology & sources below.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Artificial Analysis Openness Index: Score
Openness Index assesses model openness on a 0 to 100 normalized scale (higher is more open) · Evaluation results measured independently by Artificial Analysis
Source and methodology are summarized below
Chart source and provenance are listed in Methodology & sources below.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Intelligence
Artificial Analysis Intelligence Index · Higher is better · Evaluation results measured independently by Artificial Analysis
Source and methodology are summarized below
Chart source and provenance are listed in Methodology & sources below.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Speed & Throughput
Output tokens per second and generation throughput.
Output Speed
Output tokens per second · Higher is better · Evaluation results measured independently by Artificial Analysis
Source and methodology are summarized below
Chart source and provenance are listed in Methodology & sources below.
Speed should be read together with latency and cost; fast output does not always mean better user-perceived performance.
Speed
Output tokens per second · Higher is better · Evaluation results measured independently by Artificial Analysis
Source and methodology are summarized below
Chart source and provenance are listed in Methodology & sources below.
Speed should be read together with latency and cost; fast output does not always mean better user-perceived performance.
Latency & Response Time
Time to first token and end-to-end response latency.
End-to-End Response Time
Seconds to output 500 tokens, including reasoning model 'thinking' time · Lower is better · Evaluation results measured independently by Artificial Analysis
Source and methodology are summarized below
Chart source and provenance are listed in Methodology & sources below.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Latency: Time To First Answer Token
Seconds to first answer token received · Accounts for reasoning model 'thinking' time · Evaluation results measured independently by Artificial Analysis
Source and methodology are summarized below
Chart source and provenance are listed in Methodology & sources below.
Latency includes thinking / reasoning time when applicable. Lower is better for interactive use cases.
Context, Cost & Pricing
Context window size, cost per task, and token pricing.
Context Window
Context window: tokens limit · Higher is better · Evaluation results measured independently by Artificial Analysis
Source and methodology are summarized below
Chart source and provenance are listed in Methodology & sources below.
Larger context windows enable complex multi-turn tasks but may increase latency and cost for long inputs.
Cost per Intelligence Index Task
Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better · Evaluation results measured independently by Artificial Analysis
Source and methodology are summarized below
Chart source and provenance are listed in Methodology & sources below.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Cost per Task
Weighted average cost (USD) per Intelligence Index task · Lower is better · Evaluation results measured independently by Artificial Analysis
Source and methodology are summarized below
Chart source and provenance are listed in Methodology & sources below.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Cost to Run Artificial Analysis Intelligence Index
Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index · Evaluation results measured independently by Artificial Analysis
Source and methodology are summarized below
Chart source and provenance are listed in Methodology & sources below.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Pricing: Cache Hit, Input, and Output
Price (USD per M Tokens) · Evaluation results measured independently by Artificial Analysis
Source and methodology are summarized below
Chart source and provenance are listed in Methodology & sources below.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Time per Intelligence Index Task
Weighted average decode time (minutes) per task; excludes TTFT and overhead time · Lower is better · Evaluation results measured independently by Artificial Analysis
Source and methodology are summarized below
Chart source and provenance are listed in Methodology & sources below.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Architecture & Scale
Model size, parameters, and architectural details.
Model Size: Total and Active Parameters
Comparison between total model parameters and parameters active during inference · Evaluation results measured independently by Artificial Analysis
Source and methodology are summarized below
Chart source and provenance are listed in Methodology & sources below.
Larger models generally perform better but cost more to serve. Active parameter count matters for inference efficiency.
Specifications
Technical Specifications
MiMo-V2.5 scores 40 (estimated) on the Artificial Analysis Intelligence Index, placing it well above average among other open weight models of similar size (median: 25). MiMo-V2.5 generates output at 84.9 tokens per second (based on Xiaomi's API), which is above average compared to other open weight models of similar size (median: 61.7 t/s). MiMo-V2.5 costs $0.14 per 1M input tokens (very competitive, median: $0.59) and $0.28 per 1M output tokens (very competitive, median: $2.20), based on Xiaomi's API.
MiMo-V2.5
- Model type
- Open weights
- Reasoning
- Yes
- Input modalities
- MiMo-V2.5 supports text and image input.
- Output modalities
- MiMo-V2.5 supports text output.
- Context window
- MiMo-V2.5 has a context window of 1.0M tokens. This determines how much text and conversation history the model can process in a single request.
- Open weights / source
- Yes, MiMo-V2.5 is open weights. The model weights are publicly available and can be downloaded for self-hosting.
- Parameters
- MiMo-V2.5 has 310 billion parameters (15 billion active).
- Active parameters
- MiMo-V2.5 is a Mixture of Experts (MoE) model with 310 billion total parameters, but only 15 billion active parameters are used during inference.
- License
- MiMo-V2.5 is released under the Mit license. This license allows commercial use.
- API availability
- Yes, MiMo-V2.5 is available via API through 5 providers.
Methodology & Provenance
This page is rendered from the normalized profile and page JSON for MiMo-V2.5.
Benchmark values are preserved as normalized; only layout, disclosure ordering, and typography are adjusted for readability.
Source file: md/scrape/artificial_analysis_model_pages/artificialanalysis_ai__models__mimo-v2-5-0424__076d98d475.html · Canonical URL: https://artificialanalysis.ai/models/mimo-v2-5-0424
Normalized at: 2026-07-08T03:24:56+00:00 Technical specs, comparison summary, and FAQs are passed through from the normalized data without rewriting.
Visible FAQ answers and FAQ JSON-LD are emitted from the same normalized FAQ array, so structured data stays aligned with the rendered accordion.
Time per Intelligence Index Task: https://artificialanalysis.ai/models/mimo-v2-5-0424
Frequently Asked Questions
MiMo-V2.5 was released on April 22, 2026.
MiMo-V2.5 was created by Xiaomi.
MiMo-V2.5 scores 40 (estimated) on the Artificial Analysis Intelligence Index, placing it well above average among other open weight models of similar size (median: 25).