Xiaomi positions MiMo-V2.6-Pro as a native omnimodal open-source model for agents, coding, vision, and computer use, and reports an Artificial Analysis Intelligence Index score of 46 along with several RL/agent results; these figures remain the vendor's own reporting and cannot replace an independent rerun under the same harness.
Xiaomi MiMo Documentation · Read evidenceMiMo-V2.6-Pro · Reviews and evidence
Which MiMo-V2.6-Pro conclusions hold up?
Browse public evaluations by topic, source identity, and evidence type. Different versions, tiers, and harnesses are not treated as directly comparable.
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Editorial takeaways
Editorial takeaways
This official thread positions MiMo-V2.6-Pro as an openly built native omnimodal agent model focused on coding, computer use, 3D, design, research, and tool workflows; its rankings and examples help identify promising task directions, but they remain vendor-reported and cannot replace an independent rerun under the same harness.
X (Xiaomi MiMo official account); Xiaomi MiMo official release page linked from the X thread · Read evidenceUnder the Xiaomi provider, reasoning variant, and default production workload of 10,000 input tokens, Artificial Analysis measured MiMo-V2.6-Pro at an Intelligence Index of 46.324 (displayed as 46), output speed of 129.75 tokens/s, 17.58 seconds to the first answer token, and 21.44 seconds for an end-to-end 500-token output; pricing is in the low-cost range。
Artificial Analysis · Read evidenceFull reviews and related reading
Selected evidence
Media / benchmarkVendor report
Xiaomi MiMo Official Release: MiMo-V2.6-Pro Benchmark Signals and Native Omnimodal Positioning
Xiaomi positions MiMo-V2.6-Pro as a native omnimodal open-source model for agents, coding, vision, and computer use, and reports an Artificial Analysis Intelligence Index score of 46 along with several RL/agent results; these figures remain the vendor's own reporting and cannot replace an independent rerun under the same harness.
SourceXiaomi MiMo Documentation
Published2026-09-22
Collected2026-09-22
- Source-specific observation
- Xiaomi positions MiMo-V2.6-Pro as a native omnimodal open-source model for agents, coding, vision, and computer use, and reports an Artificial Analysis Intelligence Index score of 46 along with several RL/agent results; these figures remain the vendor's own reporting and cannot replace an independent rerun under the same harness.
- Published conditions
- Deciding whether to ship to production based only on official leaderboards; rigorous comparisons requiring independent statistical significance, complete inputs, random seeds, and per-item logs; or treating case demonstrations and claims about what the model “can achieve” as stable success rates.
CommunityVendor report
MiMo-V2.6-Pro Official X Release Thread: Task Positioning, Public Benchmarks, and Open-Source Entry Points
This official thread positions MiMo-V2.6-Pro as an openly built native omnimodal agent model focused on coding, computer use, 3D, design, research, and tool workflows; its rankings and examples help identify promising task directions, but they remain vendor-reported and cannot replace an independent rerun under the same harness.
SourceX (Xiaomi MiMo official account); Xiaomi MiMo official release page linked from the X thread
Published2026-09-22
Collected2026-09-22
- Source-specific observation
- This official thread positions MiMo-V2.6-Pro as an openly built native omnimodal agent model focused on coding, computer use, 3D, design, research, and tool workflows; its rankings and examples help identify promising task directions, but they remain vendor-reported and cannot replace an independent rerun under the same harness.
- Published conditions
- Production decisions based only on claims of being “comparable” or on price ratios in this thread; rigorous comparisons requiring per-item inputs, complete tool schemas, random seeds, failure logs, and statistical significance; or treating a single official case as a stable success rate.
Media / benchmarkIndependent measurement
Artificial Analysis: MiMo-V2.6-Pro Intelligence Index, Speed, Pricing, and Latency
Under the Xiaomi provider, reasoning variant, and default production workload of 10,000 input tokens, Artificial Analysis measured MiMo-V2.6-Pro at an Intelligence Index of 46.324 (displayed as 46), output speed of 129.75 tokens/s, 17.58 seconds to the first answer token, and 21.44 seconds for an end-to-end 500-token output; pricing is in the low-cost range。
SourceArtificial Analysis
Published2026-09-22
Collected2026-09-22
- Source-specific observation
- Under the Xiaomi provider, reasoning variant, and default production workload of 10,000 input tokens, Artificial Analysis measured MiMo-V2.6-Pro at an Intelligence Index of 46.324 (displayed as 46), output speed of 129.75 tokens/s, 17.58 seconds to the first answer token, and 21.44 seconds for an end-to-end 500-token output; pricing is in the low-cost range。
- Published conditions
- Interactive short-form Q&A where time to first response is highly sensitive; rigorous cross-provider, cross-reasoning-level, or cross-random-seed comparisons; this page has only one Xiaomi provider and cannot represent other deployments.
Media / benchmarkVendor report
MiMo-V2.6-Pro Official Technical Report: Architecture, Scaled RL, and Evaluation Conditions
The official report defines MiMo-V2.6-Pro as a native multimodal sparse MoE with 1.02T total parameters and approximately 42B active parameters, and reports strong agent benchmark results from large-batch, multi-environment RL training with multiple harnesses and groupwise graders; however, most figures in the tables are vendor-reported。
SourceHugging Face (XiaomiMiMo official model card and technical report)
Published2026-09-22
Collected2026-09-22
- Source-specific observation
- The official report defines MiMo-V2.6-Pro as a native multimodal sparse MoE with 1.02T total parameters and approximately 42B active parameters, and reports strong agent benchmark results from large-batch, multi-environment RL training with multiple harnesses and groupwise graders; however, most figures in the tables are vendor-reported。
- Published conditions
- Judging production success rate, cost efficiency, or statistical significance from the official tables alone; treating internal benchmarks, training curves, or demo cases as independent blind tests; or equating Pro-RL weight results directly with the API's mimo-v2.6-pro-ultraspeed.
All sources
All sources
Media / benchmarkVendor report
Xiaomi MiMo Official Release: MiMo-V2.6-Pro Benchmark Signals and Native Omnimodal Positioning
Xiaomi positions MiMo-V2.6-Pro as a native omnimodal open-source model for agents, coding, vision, and computer use, and reports an Artificial Analysis Intelligence Index score of 46 along with several RL/agent results; these figures remain the vendor's own reporting and cannot replace an independent rerun under the same harness.
SourceXiaomi MiMo Documentation
Published2026-09-22
Collected2026-09-22
- Source-specific observation
- Xiaomi positions MiMo-V2.6-Pro as a native omnimodal open-source model for agents, coding, vision, and computer use, and reports an Artificial Analysis Intelligence Index score of 46 along with several RL/agent results; these figures remain the vendor's own reporting and cannot replace an independent rerun under the same harness.
- Published conditions
- Deciding whether to ship to production based only on official leaderboards; rigorous comparisons requiring independent statistical significance, complete inputs, random seeds, and per-item logs; or treating case demonstrations and claims about what the model “can achieve” as stable success rates.
CommunityVendor report
MiMo-V2.6-Pro Official X Release Thread: Task Positioning, Public Benchmarks, and Open-Source Entry Points
This official thread positions MiMo-V2.6-Pro as an openly built native omnimodal agent model focused on coding, computer use, 3D, design, research, and tool workflows; its rankings and examples help identify promising task directions, but they remain vendor-reported and cannot replace an independent rerun under the same harness.
SourceX (Xiaomi MiMo official account); Xiaomi MiMo official release page linked from the X thread
Published2026-09-22
Collected2026-09-22
- Source-specific observation
- This official thread positions MiMo-V2.6-Pro as an openly built native omnimodal agent model focused on coding, computer use, 3D, design, research, and tool workflows; its rankings and examples help identify promising task directions, but they remain vendor-reported and cannot replace an independent rerun under the same harness.
- Published conditions
- Production decisions based only on claims of being “comparable” or on price ratios in this thread; rigorous comparisons requiring per-item inputs, complete tool schemas, random seeds, failure logs, and statistical significance; or treating a single official case as a stable success rate.
Media / benchmarkIndependent measurement
Artificial Analysis: MiMo-V2.6-Pro Intelligence Index, Speed, Pricing, and Latency
Under the Xiaomi provider, reasoning variant, and default production workload of 10,000 input tokens, Artificial Analysis measured MiMo-V2.6-Pro at an Intelligence Index of 46.324 (displayed as 46), output speed of 129.75 tokens/s, 17.58 seconds to the first answer token, and 21.44 seconds for an end-to-end 500-token output; pricing is in the low-cost range。
SourceArtificial Analysis
Published2026-09-22
Collected2026-09-22
- Source-specific observation
- Under the Xiaomi provider, reasoning variant, and default production workload of 10,000 input tokens, Artificial Analysis measured MiMo-V2.6-Pro at an Intelligence Index of 46.324 (displayed as 46), output speed of 129.75 tokens/s, 17.58 seconds to the first answer token, and 21.44 seconds for an end-to-end 500-token output; pricing is in the low-cost range。
- Published conditions
- Interactive short-form Q&A where time to first response is highly sensitive; rigorous cross-provider, cross-reasoning-level, or cross-random-seed comparisons; this page has only one Xiaomi provider and cannot represent other deployments.
Media / benchmarkVendor report
MiMo-V2.6-Pro Official Technical Report: Architecture, Scaled RL, and Evaluation Conditions
The official report defines MiMo-V2.6-Pro as a native multimodal sparse MoE with 1.02T total parameters and approximately 42B active parameters, and reports strong agent benchmark results from large-batch, multi-environment RL training with multiple harnesses and groupwise graders; however, most figures in the tables are vendor-reported。
SourceHugging Face (XiaomiMiMo official model card and technical report)
Published2026-09-22
Collected2026-09-22
- Source-specific observation
- The official report defines MiMo-V2.6-Pro as a native multimodal sparse MoE with 1.02T total parameters and approximately 42B active parameters, and reports strong agent benchmark results from large-batch, multi-environment RL training with multiple harnesses and groupwise graders; however, most figures in the tables are vendor-reported。
- Published conditions
- Judging production success rate, cost efficiency, or statistical significance from the official tables alone; treating internal benchmarks, training curves, or demo cases as independent blind tests; or equating Pro-RL weight results directly with the API's mimo-v2.6-pro-ultraspeed.
Media / benchmarkPlatform telemetry
Arena Code Arena: MiMo-V2.6-Pro WebDev AutoEval Record
Arena's Code Arena | WebDev overall leaderboard includes mimo-v2.6-pro with an AutoEval score of 1628 (+18/-18), but it does not publish a vote count or rank, so this only shows that it was included in the WebDev automated evaluation leaderboard; 1628 must not be treated as a blind-test ranking.
SourceArena (official leaderboard)
Published2026-09-12
Collected2026-09-22
- Source-specific observation
- Arena's Code Arena | WebDev overall leaderboard includes mimo-v2.6-pro with an AutoEval score of 1628 (+18/-18), but it does not publish a vote count or rank, so this only shows that it was included in the WebDev automated evaluation leaderboard; 1628 must not be treated as a blind-test ranking.
- Published conditions
- This record cannot establish the model's quality for general text, vision, multimodal tasks, long-form writing, or real-world project delivery; nor can it establish human preference.
CommunityPersonal experience
Reddit Field Test: MiMo-V2.6-Pro Gets Stuck in a grep Infinite Loop During a UI/UX Terminal Modification Task
In a real website UI/UX modification task, the poster said that both MiMo-V2.6-Pro and MiMo-V2.6-Flash triggered a grep-related infinite loop in the terminal and failed to fix the code after approximately 30 minutes; when the same modification was assigned to DeepSeek V4.1 Flash, it continued as expected and output a complete terminal work history.
SourceReddit, r/CommandCode
Published2026-09-22
Collected2026-09-22
- Source-specific observation
- In a real website UI/UX modification task, the poster said that both MiMo-V2.6-Pro and MiMo-V2.6-Flash triggered a grep-related infinite loop in the terminal and failed to fix the code after approximately 30 minutes; when the same modification was assigned to DeepSeek V4.1 Flash, it continued as expected and output a complete terminal work history.
- Published conditions
- It cannot be used to assess benchmark scores, price, speed, code quality, or long-horizon Agent capability, and it cannot replace a controlled retest.
MiMo-V2.6-Pro
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