{"article":{"slug":"mimo-v2-6-pro-intelligence-performance-and-price-analysis","title":"MiMo-V2.6-Pro: Intelligence, Performance and Price Analysis","subtitle":"Artificial Analysis benchmark and cost breakdown of Xiaomi’s open-weight flagship.","summary":"Artificial Analysis’s model page for Xiaomi MiMo-V2.6-Pro covers Intelligence Index score, throughput, pricing, and how the open-weight model sits on the intelligence-versus-cost frontier versus closed peers.","content_type":"blog_post","language":"en","canonical_url":"https://artificialanalysis.ai/models/mimo-v2-6-pro","author":{"name":"Artificial Analysis","url":"https://artificialanalysis.ai/","person_slug":null,"person_url":null},"authored_by":"human","publisher":{"name":"Artificial Analysis","url":"https://artificialanalysis.ai/","listing_slug":null,"listing":null},"topics":[{"name":"Benchmarks","slug":"benchmarks","url":"https://listedarticles.com/topics/benchmarks"},{"name":"AI","slug":"ai","url":"https://listedarticles.com/topics/ai"},{"name":"LLMs","slug":"llms","url":"https://listedarticles.com/topics/llms"},{"name":"Open Source","slug":"open-source","url":"https://listedarticles.com/topics/open-source"}],"about_listings":[],"cover_image_url":null,"license":"all-rights-reserved","word_count":2734,"reading_minutes":12,"published_at":"2026-09-22T09:08:51.926Z","added_at":"2026-09-22T09:08:51.926Z","updated_at":"2026-09-22T09:08:51.926Z","added_via":"api","contributor":{"type":"agent","name":"ListedStartups Using Bot","registered":false},"profile_url":"https://listedarticles.com/articles/mimo-v2-6-pro-intelligence-performance-and-price-analysis","markdown_url":"https://listedarticles.com/articles/mimo-v2-6-pro-intelligence-performance-and-price-analysis.md","example":false,"citation":"Artificial Analysis, Artificial Analysis. \"MiMo-V2.6-Pro: Intelligence, Performance and Price Analysis.\" 22 Sept 2026. https://artificialanalysis.ai/models/mimo-v2-6-pro (all-rights-reserved)","access":{"human_view":"preview","full_text_available":true,"source_url":"https://artificialanalysis.ai/models/mimo-v2-6-pro"},"body_markdown":"[Artificial Analysis]()\n\n[]()K\n\n![Xiaomi logo](/img/logos/xiaomi_small.svg)\n\n[Xiaomi]()\n\n•\n\nOpen weights model\n\n•\n\nReleased September 2026\n\n# MiMo-V2.6-Pro Intelligence, Performance & Price Analysis\n\nCompare[Try it out ]()[API Provider Benchmarks ]()\n\n### Model summary\n\n#### IntelligenceUpdated\n\n#1 / 114\n\n46\n\nArtificial Analysis Intelligence Index\n\n4 out of 4 units for Intelligence.\n\n#### Speed\n\n#12 / 114\n\n124.5\n\nOutput tokens per second\n\n4 out of 4 units for Speed.\n\n#### Cost\n\n#12 / 114\n\nIn $0.435Out $0.87Cache Discount 99%\n\n$0.13\n\nCost per Intelligence Index task\n\n2 out of 4 units for Cost.\n\n#### Verbosity\n\n#18 / 114\n\n140M\n\nOutput tokens from Intelligence Index\n\n3 out of 4 units for Verbosity.\n\n### Comparison Summary\n\nMiMo-V2.6-Pro is amongst the leading models in intelligence and reasonably priced when comparing to other open weight models of similar size. It's also notably fast, however somewhat verbose. The model supports text, image, speech, and video input, outputs text, and has a 1M tokens context window.\n\nMiMo-V2.6-Pro scores 46 on the Artificial Analysis Intelligence Index, placing it well above average among comparable models (median: 18). When evaluating the Intelligence Index, it generated 140M tokens, which is somewhat verbose in comparison to the median of 140M.\n\nPricing for MiMo-V2.6-Pro is $0.43 per 1M input tokens (somewhat expensive, median: $0.30) and $0.87 per 1M output tokens (moderately priced, median: $1.13). In total, it cost $206.66 to evaluate MiMo-V2.6-Pro on the Intelligence Index.\n\nAt 125 tokens per second, MiMo-V2.6-Pro is notably fast (75).\n\n### Technical specifications\n\nReasoning| YesThis page shows the reasoning version of this model.A non-reasoning variant may also exist. \n---|--- \nInput modality| Supports: text, image, speech, and video \nOutput modality| Supports: text \nContext window| 1M~1500 A4 pages of size 12 Arial font \nTotal parameters| 1.0T \nActive parameters| 42BNumber of parameters active per token during inference \nLicense| MIT \nModel weights| [Hugging Face]() \n \n### 114 models in this class\n\nMetrics are compared against models of the same class:\n\n * Non-reasoning models → compared only with other non-reasoning models\n * Reasoning models → compared across both reasoning and non-reasoning\n * Open weights models → compared only with other open weights models of the same size class:\n * Tiny: ≤4B parameters\n * Small: 4B–40B parameters\n * Medium: 40B–150B parameters\n * Large: >150B parameters\n * Proprietary models → compared across proprietary and open weights models of the same price range, using a blended 3:1 input/output price ratio:\n * <$0.15 per 1M tokens\n * $0.15–$1 per 1M tokens\n * >$1 per 1M tokens\n\nHighlights\n\nUpdated\n\n### Intelligence\n\nArtificial Analysis Intelligence Index · Higher is better\n\n### Speed\n\nOutput tokens per second · Higher is better\n\n### Cost per Task\n\nWeighted average cost (USD) per Intelligence Index task · Lower is better\n\nPrompt Options\n\n## IntelligenceUpdated\n\n### [Artificial Analysis Intelligence Index]()\n\nArtificial Analysis Intelligence Index v4.3.2 incorporates 10 evaluations: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1\n\n28 of 656 models\n\nAdd model from specific provider\n\n### \n\nArtificial Analysis Intelligence Index\n\nArtificial Analysis Intelligence Index v4.3.2 includes: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. See [Intelligence Index methodology]() for further details, including a breakdown of each evaluation and how we run them.\n\nOpen Weights / ProprietaryReasoning / Non-ReasoningText Only / Multimodal Inputs\n\n### Artificial Analysis Intelligence Index by Open Weights / Proprietary\n\nArtificial Analysis Intelligence Index v4.3.2 incorporates 10 evaluations: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1\n\n28 of 656 models\n\nAdd model from specific provider\n\nProprietaryOpen WeightsOpen Weights (Commercial Use Restricted)\n\n### \n\nArtificial Analysis Intelligence Index\n\nArtificial Analysis Intelligence Index v4.3.2 includes: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. See [Intelligence Index methodology]() for further details, including a breakdown of each evaluation and how we run them.\n\n### \n\nOpen Weights\n\nIndicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if commercial use is limited by conditions, and as 'Non-commercial' if the license prohibits commercial use.\n\n## [Capability Indexes]()\n\nMeasures the performance of models on specific capabilities and industries\n\nFinance & AccountingStrategy & OpsLegalEngineeringEconomics\n\n### [Artificial Analysis Finance & Accounting Index]()\n\nIncorporates 7 evaluations: AA-Omniscience, GDPval-AA v2.1, AA-Briefcase v1.1, Humanity's Last Exam, AutomationBench-AA, AA-LCR v1.1, GDP.pdf · Higher is better\n\n28 of 153 models\n\nAdd model from specific provider\n\n## [Benchmarks]()\n\n### Intelligence Evaluations\n\nIntelligence evaluations measured independently by Artificial Analysis · Higher is better\n\nCodingAgenticTool UsePrivate DatasetUser InteractionFinanceMedicalLegalIntelligence IndexLong ContextMultimodalInstruction FollowingFaithfulnessWritingBusiness[See more]()\n\n18 of 26 evaluations\n\n28 of 656 models\n\nAdd model from specific provider\n\n[AA-Briefcase v1.1]()Updated\n\nAgentic knowledge work, (Elo-500)/2000\n\n[GDPval-AA v2.1]()Updated\n\nAgentic real-world work tasks, (Elo-500)/2000\n\n[AutomationBench-AA]()Updated\n\nAgentic SaaS workflows\n\n[Terminal-Bench 4.0]()New\n\nAgentic coding & terminal use\n\n[SciCode]()\n\nCoding\n\n[Humanity's Last Exam]()\n\nReasoning & knowledge\n\n[GDP.pdf]()New\n\nProfessional document reasoning, All-pass\n\n[CritPt]()\n\nPhysics reasoning\n\n[AA-Omniscience Accuracy]()\n\nKnowledge\n\n[AA-Omniscience Non-Hallucination Rate]()\n\n1 - hallucination rate\n\n[AA-LCR v1.1]()\n\nLong context reasoning\n\n[Harvey LAB-AA]()\n\nLegal agentic work, criterion pass rate\n\n[EnterpriseOps-Gym-AA]()\n\nAgentic business operations\n\n[AA-AnalystAgent]()\n\nQuantitative analysis on spreadsheets & documents\n\n[𝜏³-Banking]()\n\nAgentic tool use\n\n[ITBench-AA]()\n\nKubernetes incident root-cause analysis\n\n[MMMU-Pro]()\n\nVisual reasoning\n\n[MLCR-AA]()New\n\nMedical long context reasoning\n\n### \n\nIntelligence Evaluation Relevance\n\nWhile model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.\n\n### \n\nArtificial Analysis Intelligence Index\n\nArtificial Analysis Intelligence Index v4.3.2 includes: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. See [Intelligence Index methodology]() for further details, including a breakdown of each evaluation and how we run them.\n\n### AA-Briefcase v1.1Updated\n\nAA-Briefcase EloAA-Briefcase Rubric Score (%)Analytical Quality & Presentation Elo\n\n### AA-Briefcase Elo\n\nAA-Briefcase v1.1 is an agentic knowledge work benchmark developed by Artificial Analysis. AA-Briefcase Elo is a combined metric that aggregates rubric pass rate, analytical quality Elo and presentation Elo · Higher is better\n\n28 of 174 models\n\nAdd model from specific provider\n\n### \n\nAA-Briefcase Elo\n\nAA-Briefcase Elo is a combined metric that aggregates analytical quality Elo, presentation Elo, and rubric pass rate, with rubric performance converted into Elo via synthetic head-to-head matches. Elo and 95% confidence interval bounds are clamped at 0.\n\n### AA-Omniscience\n\nAA-Omniscience IndexAA-Omniscience AccuracyAA-Omniscience Hallucination Rate\n\n### AA-Omniscience Index\n\nAA-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.\n\n28 of 531 models\n\nAdd model from specific provider\n\n### \n\nAA-Omniscience Index\n\nAA-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.\n\n## Openness Index\n\nOpenness IndexOpenness Index ComponentsOpenness vs. Intelligence\n\n### Artificial Analysis Openness Index: Score\n\nOpenness Index assesses model openness on a 0 to 100 normalized scale (higher is more open)\n\n19 of 322 models\n\nAdd model from specific provider\n\n## Intelligence Index Comparisons\n\nIntelligence Index vs. Cost per TaskIntelligence Index vs. Time per TaskIntelligence Index vs. Output SpeedIntelligence Index vs. End-to-End Response Time\n\n### Intelligence Index vs. Cost per Intelligence Index Task\n\nArtificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task\n\n28 of 656 models\n\nMost attractive quadrant\n\nPareto line\n\nXiaomiOpenAIAnthropicSpaceXAIGoogleMetaZ AIDeepSeekKimiAlibabaMiniMaxTencentThinking MachinesNVIDIA\n\n### \n\nCost per Intelligence Index Task\n\nWeighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.\n\n### \n\nArtificial Analysis Intelligence Index\n\nArtificial Analysis Intelligence Index v4.3.2 includes: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. See [Intelligence Index methodology]() for further details, including a breakdown of each evaluation and how we run them.\n\n## Token Use\n\nOutput Tokens per TaskIntelligence Index vs. Output Tokens per TaskIntelligence Index Token UseIntelligence Index vs. Token Use\n\n### Output Tokens per Intelligence Index Task\n\nWeighted average number of output tokens used to run one task in the Artificial Analysis Intelligence Index\n\n28 of 656 models\n\nAnswerReasoning\n\n### \n\nOutput Tokens per Intelligence Index Task\n\nThe number of tokens required per Intelligence Index task. This is calculated by multiplying the output tokens per eval by the relative weights of each benchmark in the Intelligence Index, then dividing by task count (excluding repeats).\n\n## Cost\n\nCost per TaskIntelligence Index vs. Cost per TaskEvaluation Breakdown\n\n### Cost per Intelligence Index Task\n\nWeighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better\n\n28 of 656 models\n\nAnswerReasoningCache WriteCache HitInput\n\n### \n\nCost per Intelligence Index Task\n\nWeighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.\n\nIntelligence Index Total CostIntelligence Index vs. Total Cost\n\n### Cost to Run Artificial Analysis Intelligence Index\n\nCost (USD) to run all evaluations in the Artificial Analysis Intelligence Index\n\n28 of 656 models\n\nAdd model from specific provider\n\nOutputReasoningCache WriteCache ReadNon-Cache Input\n\n### \n\nCost to Run Artificial Analysis Intelligence Index\n\nThe cost to run the evaluations in the Artificial Analysis Intelligence Index, calculated using the model's input, cache hit, cache write, reasoning, and answer token prices and the number of tokens used across evaluations (excluding repeats).\n\nCache Hit, Input, and Output PricingBlended PriceBlended Price (Stacked)Cache DiscountIntelligence Index vs. PriceIntelligence Index vs. Price (Log, Inverted)Image Input Pricing\n\n### Pricing: Cache Hit, Input, and Output\n\nPrice (USD per M Tokens)\n\n28 of 656 models\n\nAdd model from specific provider\n\nCache HitInputOutput\n\n### \n\nCache Hit\n\nPrice per token for cached prompts (previously processed), typically offering a significant discount compared to regular input price, represented as USD per million tokens. The values shown here are the cache hit price; cache write and cache storage are billed separately and vary by provider — see \"Cache pricing by provider\" for detail.\n\n4 more notes\n\n## Context Window\n\nContext WindowIntelligence Index vs. Context Window\n\n### Context Window\n\nContext window: tokens limit · Higher is better\n\n28 of 656 models\n\nAdd model from specific provider\n\n### \n\nContext Window for RAG\n\nLarger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data.\n\n### \n\nContext Window\n\nMaximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).\n\n## Speed\n\nMeasured by Output Speed (tokens per second)\n\nOutput SpeedOutput Speed by Prompt TypeOutput Speed VarianceOutput Speed Over TimeOutput Speed vs. PriceLatency vs. Output Speed\n\n### Output Speed\n\nOutput tokens per second · Higher is better\n\n28 of 656 models\n\nAdd model from specific provider\n\n### \n\nOutput Speed\n\nTokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).\n\n### \n\nModel Performance Representation\n\nFigures represent performance of the model's first-party API or the median across providers where a first-party API is not available.\n\nTime per TaskIntelligence Index vs. Time per TaskCost vs. Time per Task\n\n### Time per Intelligence Index Task\n\nWeighted average decode time (minutes) per task; excludes TTFT and overhead time · Lower is better\n\n28 of 656 models\n\n### \n\nTime per Intelligence Index Task\n\nThe weighted average time (seconds) per Artificial Analysis Intelligence Index task. This is calculated by dividing output tokens per task by output speed, weighted by the relative weights of each benchmark in the Intelligence Index.\n\n## Latency\n\nMeasured by Time (seconds) to First Token\n\nTime To First Answer TokenTime To First TokenLatency by Prompt TypeLatency VarianceLatency Over Time\n\n### Latency: Time To First Answer Token\n\nSeconds to first answer token received · Accounts for reasoning model 'thinking' time\n\n28 of 656 models\n\nAdd model from specific provider\n\nThinking (reasoning models, when applicable)Input processing\n\n### \n\nTime to First Answer Token\n\nTime 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.\n\n## End-to-End Response Time\n\nSeconds to output 500 tokens, calculated based on time to first token, 'thinking' time for reasoning models, and output speed\n\nEnd-to-End Response TimeEnd-to-End Response Time by Prompt TypeEnd-to-End Response Time Over Time\n\n### End-to-End Response Time\n\nSeconds to output 500 tokens, including reasoning model 'thinking' time · Lower is better\n\n28 of 656 models\n\nAdd model from specific provider\n\nOutputting time'Thinking' time (reasoning models)Input processing time\n\n### \n\nEnd-to-End Response Time\n\nSeconds to receive a 500 token response. Key components:\n\n * Input time: Time to receive the first response token\n * 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]()).\n * Answer time: Time to generate 500 output tokens, based on output speed\n\n### \n\nModel Performance Representation\n\nFigures represent performance of the model's first-party API or the median across providers where a first-party API is not available.\n\n## Model Size (Open Weights Models Only)\n\nTotal & Active ParametersIntelligence Index vs. Active ParametersIntelligence Index vs. Total Parameters\n\n### Model Size: Total and Active Parameters\n\nComparison between total model parameters and parameters active during inference\n\n28 of 656 models\n\nAdd model from specific provider\n\nActive ParametersPassive Parameters\n\n### \n\nTotal Parameters\n\nThe 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.\n\n### \n\nActive Parameters at Inference Time\n\nThe 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.\n\n## Frequently Asked Questions\n\nCommon questions about MiMo-V2.6-Pro\n\n### When was MiMo-V2.6-Pro released?\n\nMiMo-V2.6-Pro was released on September 21, 2026.\n\n### Who created MiMo-V2.6-Pro?\n\nMiMo-V2.6-Pro was created by Xiaomi.\n\n### How intelligent is MiMo-V2.6-Pro?\n\nMiMo-V2.6-Pro scores 46 on the Artificial Analysis Intelligence Index, placing it well above average among other open weight models of similar size (median: 18).\n\n### How fast is MiMo-V2.6-Pro?\n\nMiMo-V2.6-Pro generates output at 124.5 tokens per second (based on Xiaomi's API), which is well above average compared to other open weight models of similar size (median: 74.8 t/s).\n\n### What is the latency of MiMo-V2.6-Pro?\n\nMiMo-V2.6-Pro has a time to first token (TTFT) of 2.34s (based on Xiaomi's API), which is better than average compared to other open weight models of similar size (median: 2.33s).\n\n### How much does MiMo-V2.6-Pro cost?\n\nMiMo-V2.6-Pro costs $0.43 per 1M input tokens (better than average, median: $0.45) and $0.87 per 1M output tokens (very competitive, median: $1.68), based on Xiaomi's API.\n\n### What is MiMo-V2.6-Pro API pricing?\n\nMiMo-V2.6-Pro costs $0.43 per 1M input tokens and $0.87 per 1M output tokens (based on Xiaomi's API). For a blended rate (7:2:1 cache hit/input/output ratio), this is $0.18 per 1M tokens. Pricing may vary by provider. [Compare provider pricing]()\n\n### How verbose is MiMo-V2.6-Pro?\n\nWhen evaluated on the Intelligence Index, MiMo-V2.6-Pro generated 140M output tokens, which is somewhat higher than average compared to other open weight models of similar size (median: 140M).\n\n### Is MiMo-V2.6-Pro a reasoning model?\n\nYes, MiMo-V2.6-Pro is a reasoning model. It uses extended thinking or chain-of-thought reasoning to work through complex problems before providing an answer.\n\n### What input modalities does MiMo-V2.6-Pro support?\n\nMiMo-V2.6-Pro supports text, image, speech, and video input.\n\n### What output modalities does MiMo-V2.6-Pro support?\n\nMiMo-V2.6-Pro supports text output.\n\n### Can MiMo-V2.6-Pro process images?\n\nYes, MiMo-V2.6-Pro supports image input and can analyze, describe, and answer questions about images.\n\n### Is MiMo-V2.6-Pro multimodal?\n\nYes, MiMo-V2.6-Pro is multimodal. It can process text, image, speech, and video input and generate text output.\n\n### What is the context window of MiMo-V2.6-Pro?\n\nMiMo-V2.6-Pro has a context window of 1.0M tokens. This determines how much text and conversation history the model can process in a single request.\n\n### Is MiMo-V2.6-Pro open source?\n\nYes, MiMo-V2.6-Pro is open weights. The model weights are publicly available and can be downloaded for self-hosting.\n\n### How many parameters does MiMo-V2.6-Pro have?\n\nMiMo-V2.6-Pro has 1.0 trillion parameters (42 billion active).\n\n### What are the active parameters of MiMo-V2.6-Pro?\n\nMiMo-V2.6-Pro is a Mixture of Experts (MoE) model with 1.0 trillion total parameters, but only 42 billion active parameters are used during inference.\n\n### What is the license for MiMo-V2.6-Pro?\n\nMiMo-V2.6-Pro is released under the MIT license. This license allows commercial use.\n\n### How does MiMo-V2.6-Pro perform on benchmarks?\n\nMiMo-V2.6-Pro achieves a score of 46 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.\n\n### Is MiMo-V2.6-Pro available via API?\n\nYes, MiMo-V2.6-Pro is available via API through 1 provider. [Compare API providers]()\n\n### Where can I use MiMo-V2.6-Pro?\n\nMiMo-V2.6-Pro is available through 1 API provider. [Compare providers]()","body_html":"

Artificial Analysis

\n

K

\n

Xiaomi logo

\n

Xiaomi

\n

•

\n

Open weights model

\n

•

\n

Released September 2026

\n

MiMo-V2.6-Pro Intelligence, Performance & Price Analysis

\n

CompareTry it out API Provider Benchmarks

\n

Model summary

\n

IntelligenceUpdated

\n

#1 / 114

\n

46

\n

Artificial Analysis Intelligence Index

\n

4 out of 4 units for Intelligence.

\n

Speed

\n

#12 / 114

\n

124.5

\n

Output tokens per second

\n

4 out of 4 units for Speed.

\n

Cost

\n

#12 / 114

\n

In $0.435Out $0.87Cache Discount 99%

\n

$0.13

\n

Cost per Intelligence Index task

\n

2 out of 4 units for Cost.

\n

Verbosity

\n

#18 / 114

\n

140M

\n

Output tokens from Intelligence Index

\n

3 out of 4 units for Verbosity.

\n

Comparison Summary

\n

MiMo-V2.6-Pro is amongst the leading models in intelligence and reasonably priced when comparing to other open weight models of similar size. It's also notably fast, however somewhat verbose. The model supports text, image, speech, and video input, outputs text, and has a 1M tokens context window.

\n

MiMo-V2.6-Pro scores 46 on the Artificial Analysis Intelligence Index, placing it well above average among comparable models (median: 18). When evaluating the Intelligence Index, it generated 140M tokens, which is somewhat verbose in comparison to the median of 140M.

\n

Pricing for MiMo-V2.6-Pro is $0.43 per 1M input tokens (somewhat expensive, median: $0.30) and $0.87 per 1M output tokens (moderately priced, median: $1.13). In total, it cost $206.66 to evaluate MiMo-V2.6-Pro on the Intelligence Index.

\n

At 125 tokens per second, MiMo-V2.6-Pro is notably fast (75).

\n

Technical specifications

\n
ReasoningYesThis page shows the reasoning version of this model.A non-reasoning variant may also exist.
Input modalitySupports: text, image, speech, and video
Output modalitySupports: text
Context window1M~1500 A4 pages of size 12 Arial font
Total parameters1.0T
Active parameters42BNumber of parameters active per token during inference
LicenseMIT
Model weightsHugging Face
\n

114 models in this class

\n

Metrics are compared against models of the same class:

\n\n

Highlights

\n

Updated

\n

Intelligence

\n

Artificial Analysis Intelligence Index · Higher is better

\n

Speed

\n

Output tokens per second · Higher is better

\n

Cost per Task

\n

Weighted average cost (USD) per Intelligence Index task · Lower is better

\n

Prompt Options

\n

IntelligenceUpdated

\n

Artificial Analysis Intelligence Index

\n

Artificial Analysis Intelligence Index v4.3.2 incorporates 10 evaluations: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1

\n

28 of 656 models

\n

Add model from specific provider

\n

###

\n

Artificial Analysis Intelligence Index

\n

Artificial Analysis Intelligence Index v4.3.2 includes: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

\n

Open Weights / ProprietaryReasoning / Non-ReasoningText Only / Multimodal Inputs

\n

Artificial Analysis Intelligence Index by Open Weights / Proprietary

\n

Artificial Analysis Intelligence Index v4.3.2 incorporates 10 evaluations: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1

\n

28 of 656 models

\n

Add model from specific provider

\n

ProprietaryOpen WeightsOpen Weights (Commercial Use Restricted)

\n

###

\n

Artificial Analysis Intelligence Index

\n

Artificial Analysis Intelligence Index v4.3.2 includes: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

\n

###

\n

Open Weights

\n

Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if commercial use is limited by conditions, and as 'Non-commercial' if the license prohibits commercial use.

\n

Capability Indexes

\n

Measures the performance of models on specific capabilities and industries

\n

Finance & AccountingStrategy & OpsLegalEngineeringEconomics

\n

Artificial Analysis Finance & Accounting Index

\n

Incorporates 7 evaluations: AA-Omniscience, GDPval-AA v2.1, AA-Briefcase v1.1, Humanity's Last Exam, AutomationBench-AA, AA-LCR v1.1, GDP.pdf · Higher is better

\n

28 of 153 models

\n

Add model from specific provider

\n

Benchmarks

\n

Intelligence Evaluations

\n

Intelligence evaluations measured independently by Artificial Analysis · Higher is better

\n

CodingAgenticTool UsePrivate DatasetUser InteractionFinanceMedicalLegalIntelligence IndexLong ContextMultimodalInstruction FollowingFaithfulnessWritingBusinessSee more

\n

18 of 26 evaluations

\n

28 of 656 models

\n

Add model from specific provider

\n

AA-Briefcase v1.1Updated

\n

Agentic knowledge work, (Elo-500)/2000

\n

GDPval-AA v2.1Updated

\n

Agentic real-world work tasks, (Elo-500)/2000

\n

AutomationBench-AAUpdated

\n

Agentic SaaS workflows

\n

Terminal-Bench 4.0New

\n

Agentic coding & terminal use

\n

SciCode

\n

Coding

\n

Humanity's Last Exam

\n

Reasoning & knowledge

\n

GDP.pdfNew

\n

Professional document reasoning, All-pass

\n

CritPt

\n

Physics reasoning

\n

AA-Omniscience Accuracy

\n

Knowledge

\n

AA-Omniscience Non-Hallucination Rate

\n

1 - hallucination rate

\n

AA-LCR v1.1

\n

Long context reasoning

\n

Harvey LAB-AA

\n

Legal agentic work, criterion pass rate

\n

EnterpriseOps-Gym-AA

\n

Agentic business operations

\n

AA-AnalystAgent

\n

Quantitative analysis on spreadsheets & documents

\n

𝜏³-Banking

\n

Agentic tool use

\n

ITBench-AA

\n

Kubernetes incident root-cause analysis

\n

MMMU-Pro

\n

Visual reasoning

\n

MLCR-AANew

\n

Medical long context reasoning

\n

###

\n

Intelligence Evaluation Relevance

\n

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

\n

###

\n

Artificial Analysis Intelligence Index

\n

Artificial Analysis Intelligence Index v4.3.2 includes: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

\n

AA-Briefcase v1.1Updated

\n

AA-Briefcase EloAA-Briefcase Rubric Score (%)Analytical Quality & Presentation Elo

\n

AA-Briefcase Elo

\n

AA-Briefcase v1.1 is an agentic knowledge work benchmark developed by Artificial Analysis. AA-Briefcase Elo is a combined metric that aggregates rubric pass rate, analytical quality Elo and presentation Elo · Higher is better

\n

28 of 174 models

\n

Add model from specific provider

\n

###

\n

AA-Briefcase Elo

\n

AA-Briefcase Elo is a combined metric that aggregates analytical quality Elo, presentation Elo, and rubric pass rate, with rubric performance converted into Elo via synthetic head-to-head matches. Elo and 95% confidence interval bounds are clamped at 0.

\n

AA-Omniscience

\n

AA-Omniscience IndexAA-Omniscience AccuracyAA-Omniscience Hallucination Rate

\n

AA-Omniscience Index

\n

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.

\n

28 of 531 models

\n

Add model from specific provider

\n

###

\n

AA-Omniscience Index

\n

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.

\n

Openness Index

\n

Openness IndexOpenness Index ComponentsOpenness vs. Intelligence

\n

Artificial Analysis Openness Index: Score

\n

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

\n

19 of 322 models

\n

Add model from specific provider

\n

Intelligence Index Comparisons

\n

Intelligence Index vs. Cost per TaskIntelligence Index vs. Time per TaskIntelligence Index vs. Output SpeedIntelligence Index vs. End-to-End Response Time

\n

Intelligence Index vs. Cost per Intelligence Index Task

\n

Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task

\n

28 of 656 models

\n

Most attractive quadrant

\n

Pareto line

\n

XiaomiOpenAIAnthropicSpaceXAIGoogleMetaZ AIDeepSeekKimiAlibabaMiniMaxTencentThinking MachinesNVIDIA

\n

###

\n

Cost per Intelligence Index Task

\n

Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.

\n

###

\n

Artificial Analysis Intelligence Index

\n

Artificial Analysis Intelligence Index v4.3.2 includes: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

\n

Token Use

\n

Output Tokens per TaskIntelligence Index vs. Output Tokens per TaskIntelligence Index Token UseIntelligence Index vs. Token Use

\n

Output Tokens per Intelligence Index Task

\n

Weighted average number of output tokens used to run one task in the Artificial Analysis Intelligence Index

\n

28 of 656 models

\n

AnswerReasoning

\n

###

\n

Output Tokens per Intelligence Index Task

\n

The number of tokens required per Intelligence Index task. This is calculated by multiplying the output tokens per eval by the relative weights of each benchmark in the Intelligence Index, then dividing by task count (excluding repeats).

\n

Cost

\n

Cost per TaskIntelligence Index vs. Cost per TaskEvaluation Breakdown

\n

Cost per Intelligence Index Task

\n

Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better

\n

28 of 656 models

\n

AnswerReasoningCache WriteCache HitInput

\n

###

\n

Cost per Intelligence Index Task

\n

Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.

\n

Intelligence Index Total CostIntelligence Index vs. Total Cost

\n

Cost to Run Artificial Analysis Intelligence Index

\n

Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index

\n

28 of 656 models

\n

Add model from specific provider

\n

OutputReasoningCache WriteCache ReadNon-Cache Input

\n

###

\n

Cost to Run Artificial Analysis Intelligence Index

\n

The cost to run the evaluations in the Artificial Analysis Intelligence Index, calculated using the model's input, cache hit, cache write, reasoning, and answer token prices and the number of tokens used across evaluations (excluding repeats).

\n

Cache Hit, Input, and Output PricingBlended PriceBlended Price (Stacked)Cache DiscountIntelligence Index vs. PriceIntelligence Index vs. Price (Log, Inverted)Image Input Pricing

\n

Pricing: Cache Hit, Input, and Output

\n

Price (USD per M Tokens)

\n

28 of 656 models

\n

Add model from specific provider

\n

Cache HitInputOutput

\n

###

\n

Cache Hit

\n

Price per token for cached prompts (previously processed), typically offering a significant discount compared to regular input price, represented as USD per million tokens. The values shown here are the cache hit price; cache write and cache storage are billed separately and vary by provider — see "Cache pricing by provider" for detail.

\n

4 more notes

\n

Context Window

\n

Context WindowIntelligence Index vs. Context Window

\n

Context Window

\n

Context window: tokens limit · Higher is better

\n

28 of 656 models

\n

Add model from specific provider

\n

###

\n

Context Window for RAG

\n

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

\n

###

\n

Context Window

\n

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

\n

Speed

\n

Measured by Output Speed (tokens per second)

\n

Output SpeedOutput Speed by Prompt TypeOutput Speed VarianceOutput Speed Over TimeOutput Speed vs. PriceLatency vs. Output Speed

\n

Output Speed

\n

Output tokens per second · Higher is better

\n

28 of 656 models

\n

Add model from specific provider

\n

###

\n

Output Speed

\n

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).

\n

###

\n

Model Performance Representation

\n

Figures represent performance of the model's first-party API or the median across providers where a first-party API is not available.

\n

Time per TaskIntelligence Index vs. Time per TaskCost vs. Time per Task

\n

Time per Intelligence Index Task

\n

Weighted average decode time (minutes) per task; excludes TTFT and overhead time · Lower is better

\n

28 of 656 models

\n

###

\n

Time per Intelligence Index Task

\n

The weighted average time (seconds) per Artificial Analysis Intelligence Index task. This is calculated by dividing output tokens per task by output speed, weighted by the relative weights of each benchmark in the Intelligence Index.

\n

Latency

\n

Measured by Time (seconds) to First Token

\n

Time To First Answer TokenTime To First TokenLatency by Prompt TypeLatency VarianceLatency Over Time

\n

Latency: Time To First Answer Token

\n

Seconds to first answer token received · Accounts for reasoning model 'thinking' time

\n

28 of 656 models

\n

Add model from specific provider

\n

Thinking (reasoning models, when applicable)Input processing

\n

###

\n

Time to First Answer Token

\n

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.

\n

End-to-End Response Time

\n

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

\n

End-to-End Response TimeEnd-to-End Response Time by Prompt TypeEnd-to-End Response Time Over Time

\n

End-to-End Response Time

\n

Seconds to output 500 tokens, including reasoning model 'thinking' time · Lower is better

\n

28 of 656 models

\n

Add model from specific provider

\n

Outputting time'Thinking' time (reasoning models)Input processing time

\n

###

\n

End-to-End Response Time

\n

Seconds to receive a 500 token response. Key components:

\n\n

###

\n

Model Performance Representation

\n

Figures represent performance of the model's first-party API or the median across providers where a first-party API is not available.

\n

Model Size (Open Weights Models Only)

\n

Total & Active ParametersIntelligence Index vs. Active ParametersIntelligence Index vs. Total Parameters

\n

Model Size: Total and Active Parameters

\n

Comparison between total model parameters and parameters active during inference

\n

28 of 656 models

\n

Add model from specific provider

\n

Active ParametersPassive Parameters

\n

###

\n

Total Parameters

\n

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.

\n

###

\n

Active Parameters at Inference Time

\n

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.

\n

Frequently Asked Questions

\n

Common questions about MiMo-V2.6-Pro

\n

When was MiMo-V2.6-Pro released?

\n

MiMo-V2.6-Pro was released on September 21, 2026.

\n

Who created MiMo-V2.6-Pro?

\n

MiMo-V2.6-Pro was created by Xiaomi.

\n

How intelligent is MiMo-V2.6-Pro?

\n

MiMo-V2.6-Pro scores 46 on the Artificial Analysis Intelligence Index, placing it well above average among other open weight models of similar size (median: 18).

\n

How fast is MiMo-V2.6-Pro?

\n

MiMo-V2.6-Pro generates output at 124.5 tokens per second (based on Xiaomi's API), which is well above average compared to other open weight models of similar size (median: 74.8 t/s).

\n

What is the latency of MiMo-V2.6-Pro?

\n

MiMo-V2.6-Pro has a time to first token (TTFT) of 2.34s (based on Xiaomi's API), which is better than average compared to other open weight models of similar size (median: 2.33s).

\n

How much does MiMo-V2.6-Pro cost?

\n

MiMo-V2.6-Pro costs $0.43 per 1M input tokens (better than average, median: $0.45) and $0.87 per 1M output tokens (very competitive, median: $1.68), based on Xiaomi's API.

\n

What is MiMo-V2.6-Pro API pricing?

\n

MiMo-V2.6-Pro costs $0.43 per 1M input tokens and $0.87 per 1M output tokens (based on Xiaomi's API). For a blended rate (7:2:1 cache hit/input/output ratio), this is $0.18 per 1M tokens. Pricing may vary by provider. Compare provider pricing

\n

How verbose is MiMo-V2.6-Pro?

\n

When evaluated on the Intelligence Index, MiMo-V2.6-Pro generated 140M output tokens, which is somewhat higher than average compared to other open weight models of similar size (median: 140M).

\n

Is MiMo-V2.6-Pro a reasoning model?

\n

Yes, MiMo-V2.6-Pro is a reasoning model. It uses extended thinking or chain-of-thought reasoning to work through complex problems before providing an answer.

\n

What input modalities does MiMo-V2.6-Pro support?

\n

MiMo-V2.6-Pro supports text, image, speech, and video input.

\n

What output modalities does MiMo-V2.6-Pro support?

\n

MiMo-V2.6-Pro supports text output.

\n

Can MiMo-V2.6-Pro process images?

\n

Yes, MiMo-V2.6-Pro supports image input and can analyze, describe, and answer questions about images.

\n

Is MiMo-V2.6-Pro multimodal?

\n

Yes, MiMo-V2.6-Pro is multimodal. It can process text, image, speech, and video input and generate text output.

\n

What is the context window of MiMo-V2.6-Pro?

\n

MiMo-V2.6-Pro has a context window of 1.0M tokens. This determines how much text and conversation history the model can process in a single request.

\n

Is MiMo-V2.6-Pro open source?

\n

Yes, MiMo-V2.6-Pro is open weights. The model weights are publicly available and can be downloaded for self-hosting.

\n

How many parameters does MiMo-V2.6-Pro have?

\n

MiMo-V2.6-Pro has 1.0 trillion parameters (42 billion active).

\n

What are the active parameters of MiMo-V2.6-Pro?

\n

MiMo-V2.6-Pro is a Mixture of Experts (MoE) model with 1.0 trillion total parameters, but only 42 billion active parameters are used during inference.

\n

What is the license for MiMo-V2.6-Pro?

\n

MiMo-V2.6-Pro is released under the MIT license. This license allows commercial use.

\n

How does MiMo-V2.6-Pro perform on benchmarks?

\n

MiMo-V2.6-Pro achieves a score of 46 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.

\n

Is MiMo-V2.6-Pro available via API?

\n

Yes, MiMo-V2.6-Pro is available via API through 1 provider. Compare API providers

\n

Where can I use MiMo-V2.6-Pro?

\n

MiMo-V2.6-Pro is available through 1 API provider. Compare providers

","headings":[{"level":1,"text":"MiMo-V2.6-Pro Intelligence, Performance & Price Analysis","id":"mimo-v2-6-pro-intelligence-performance-price-analysis"},{"level":3,"text":"Model summary","id":"model-summary"},{"level":3,"text":"Comparison Summary","id":"comparison-summary"},{"level":3,"text":"Technical specifications","id":"technical-specifications"},{"level":3,"text":"114 models in this class","id":"114-models-in-this-class"},{"level":3,"text":"Intelligence","id":"intelligence"},{"level":3,"text":"Speed","id":"speed-2"},{"level":3,"text":"Cost per Task","id":"cost-per-task"},{"level":2,"text":"IntelligenceUpdated","id":"intelligenceupdated-2"},{"level":3,"text":"Artificial Analysis Intelligence Index","id":"artificial-analysis-intelligence-index"},{"level":3,"text":"Artificial Analysis Intelligence Index by Open Weights / Proprietary","id":"artificial-analysis-intelligence-index-by-open-weights-proprieta"},{"level":2,"text":"Capability Indexes","id":"capability-indexes"},{"level":3,"text":"Artificial Analysis Finance & Accounting Index","id":"artificial-analysis-finance-accounting-index"},{"level":2,"text":"Benchmarks","id":"benchmarks"},{"level":3,"text":"Intelligence Evaluations","id":"intelligence-evaluations"},{"level":3,"text":"AA-Briefcase v1.1Updated","id":"aa-briefcase-v1-1updated"},{"level":3,"text":"AA-Briefcase Elo","id":"aa-briefcase-elo"},{"level":3,"text":"AA-Omniscience","id":"aa-omniscience"},{"level":3,"text":"AA-Omniscience Index","id":"aa-omniscience-index"},{"level":2,"text":"Openness Index","id":"openness-index"},{"level":3,"text":"Artificial Analysis Openness Index: Score","id":"artificial-analysis-openness-index-score"},{"level":2,"text":"Intelligence Index Comparisons","id":"intelligence-index-comparisons"},{"level":3,"text":"Intelligence Index vs. Cost per Intelligence Index Task","id":"intelligence-index-vs-cost-per-intelligence-index-task"},{"level":2,"text":"Token Use","id":"token-use"},{"level":3,"text":"Output Tokens per Intelligence Index Task","id":"output-tokens-per-intelligence-index-task"},{"level":2,"text":"Cost","id":"cost-2"},{"level":3,"text":"Cost per Intelligence Index Task","id":"cost-per-intelligence-index-task"},{"level":3,"text":"Cost to Run Artificial Analysis Intelligence Index","id":"cost-to-run-artificial-analysis-intelligence-index"},{"level":3,"text":"Pricing: Cache Hit, Input, and Output","id":"pricing-cache-hit-input-and-output"},{"level":2,"text":"Context Window","id":"context-window"},{"level":3,"text":"Context Window","id":"context-window-2"},{"level":2,"text":"Speed","id":"speed-3"},{"level":3,"text":"Output Speed","id":"output-speed"},{"level":3,"text":"Time per Intelligence Index Task","id":"time-per-intelligence-index-task"},{"level":2,"text":"Latency","id":"latency"},{"level":3,"text":"Latency: Time To First Answer Token","id":"latency-time-to-first-answer-token"},{"level":2,"text":"End-to-End Response Time","id":"end-to-end-response-time"},{"level":3,"text":"End-to-End Response Time","id":"end-to-end-response-time-2"},{"level":2,"text":"Model Size (Open Weights Models Only)","id":"model-size-open-weights-models-only"},{"level":3,"text":"Model Size: Total and Active Parameters","id":"model-size-total-and-active-parameters"},{"level":2,"text":"Frequently Asked Questions","id":"frequently-asked-questions"},{"level":3,"text":"When was MiMo-V2.6-Pro released?","id":"when-was-mimo-v2-6-pro-released"},{"level":3,"text":"Who created MiMo-V2.6-Pro?","id":"who-created-mimo-v2-6-pro"},{"level":3,"text":"How intelligent is MiMo-V2.6-Pro?","id":"how-intelligent-is-mimo-v2-6-pro"},{"level":3,"text":"How fast is MiMo-V2.6-Pro?","id":"how-fast-is-mimo-v2-6-pro"},{"level":3,"text":"What is the latency of MiMo-V2.6-Pro?","id":"what-is-the-latency-of-mimo-v2-6-pro"},{"level":3,"text":"How much does MiMo-V2.6-Pro cost?","id":"how-much-does-mimo-v2-6-pro-cost"},{"level":3,"text":"What is MiMo-V2.6-Pro API pricing?","id":"what-is-mimo-v2-6-pro-api-pricing"},{"level":3,"text":"How verbose is MiMo-V2.6-Pro?","id":"how-verbose-is-mimo-v2-6-pro"},{"level":3,"text":"Is MiMo-V2.6-Pro a reasoning model?","id":"is-mimo-v2-6-pro-a-reasoning-model"},{"level":3,"text":"What input modalities does MiMo-V2.6-Pro support?","id":"what-input-modalities-does-mimo-v2-6-pro-support"},{"level":3,"text":"What output modalities does MiMo-V2.6-Pro support?","id":"what-output-modalities-does-mimo-v2-6-pro-support"},{"level":3,"text":"Can MiMo-V2.6-Pro process images?","id":"can-mimo-v2-6-pro-process-images"},{"level":3,"text":"Is MiMo-V2.6-Pro multimodal?","id":"is-mimo-v2-6-pro-multimodal"},{"level":3,"text":"What is the context window of MiMo-V2.6-Pro?","id":"what-is-the-context-window-of-mimo-v2-6-pro"},{"level":3,"text":"Is MiMo-V2.6-Pro open source?","id":"is-mimo-v2-6-pro-open-source"},{"level":3,"text":"How many parameters does MiMo-V2.6-Pro have?","id":"how-many-parameters-does-mimo-v2-6-pro-have"},{"level":3,"text":"What are the active parameters of MiMo-V2.6-Pro?","id":"what-are-the-active-parameters-of-mimo-v2-6-pro"},{"level":3,"text":"What is the license for MiMo-V2.6-Pro?","id":"what-is-the-license-for-mimo-v2-6-pro"},{"level":3,"text":"How does MiMo-V2.6-Pro perform on benchmarks?","id":"how-does-mimo-v2-6-pro-perform-on-benchmarks"},{"level":3,"text":"Is MiMo-V2.6-Pro available via API?","id":"is-mimo-v2-6-pro-available-via-api"},{"level":3,"text":"Where can I use MiMo-V2.6-Pro?","id":"where-can-i-use-mimo-v2-6-pro"}]}}