Xiaomi Is Suddenly Competing With the World’s Top AI Labs

Xiaomi's MiMo-V2.6-Pro has climbed to the top of Artificial Analysis' open-weight model rankings, putting it alongside some of the strongest proprietary AI models at present available.

Key points

• Artificial Analysis estimates that an average Intelligence Index task costs regarding $0.13 on MiMo 2.6 Pro compared with $2.73 on Grok 4.7 High.
• The training involved around 750, 000 trajectories throughout MiMo 2.6 Flash and Pro, with Xiaomi saying the Pro model's portion cost roughly $2.62 million.
• Artificial Analysis gives MiMo-V2.6-Pro an Intelligence Index score of 46, the same score as Grok 4.7 High.
• While Grok leads on AA-Briefcase, GDPval, AutomationBench and GDP.pdf, miMo performs better on Terminal-Bench, SciCode, Humanity's Last Exam, CritPt and long-context reasoning.
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Artificial Analysis gives MiMo-V2.6-Pro an Intelligence Index score of 46, the same score as Grok 4.7 High. It at present sits ahead of Kimi K3 Max at 44, GLM-5.3 Max at 45, and GLM 5.3 Flash at 42. Model Intelligence Index Open Weight Context Window. MiMo-V2.6-Pro 46 Yes 1M tokens. Grok 4.7 High 46 No 500K tokens. GLM-5.3 Max 45 Yes 1M tokens. Kimi K3 Max 44 Yes ~1.05M tokens. GLM 5.3 Flash 42 Yes 1M tokens.

Meanwhile, the result is notable since MiMo 2.6 Pro and Grok 4.7 arrived at almost the same time in late September, yet Xiaomi's model reaches the same overall Artificial Analysis score while remaining open-weight. OpenAI Warns 100+ Groups After AI Agents Act Without Authorization. MiMo 2.6 Pro vs Grok 4.7.

In practice, the overall scores are identical, but the individual benchmarks show different strengths. Benchmark MiMo 2.6 Pro Grok 4.7 High. Artificial Analysis Intelligence Index 46 46. AA-Briefcase v1.1 1, 515 1, 632. GDPval-AA v2.1 1, 686 1, 710. Terminal-Bench 4.0 35% 25%. Humanity's Last Exam 49% 42%. GDP.pdf 19% 23%. AA-LCR v1.1 86% 77%.

This means MiMo should not be treated as better than Grok in every area. Instead, the two models show different strengths despite having the same overall score. Much Cheaper Than Grok 4.7. Cost is where MiMo has a much larger advantage. Metric MiMo 2.6 Pro Grok 4.7 High. Input / 1M tokens $0.435 $2.00. Output / 1M tokens $0.87 $6.00. Cached input / 1M tokens $0.0036 $0.50. Average cost per Intelligence Index task $0.13 $2.73. Cost to run full Intelligence Index $207 $3, 881.

That makes MiMo roughly 21 times cheaper per benchmark task in this particular evaluation. MiMo Is Not Faster. Xiaomi's cost advantage does not extend to raw generation speed. Performance MiMo 2.6 Pro Grok 4.7 High. Output speed ~46 tokens/sec ~78 tokens/sec. Time to first token 4.08 sec 33.12 sec. Time to first answer token 47.99 sec 33.12 sec. End-to-end response time 58.97 sec 39.49 sec.

While MiMo starts processing sooner but spends longer reasoning before delivering its final answer, grok produces tokens faster once generation begins.

Xiaomi additionally offers MiMo-V2.6-Pro-UltraSpeed. It is designed to improve output speed for latency-sensitive workloads. MiMo 2.6 Pro vs Kimi K3. MiMo additionally leads Kimi's current flagship on the overall index. Benchmark MiMo 2.6 Pro Kimi K3 Max. Intelligence Index 46 44. Terminal-Bench 4.0 35% 13%. Humanity's Last Exam 49% 47%. AA-LCR v1.1 86% 89%. Cost per task $0.13 $2.00.

Kimi K3 performs better in some long-context testing, but MiMo leads on the overall index and a number of coding, science and reasoning tests. MiMo 2.6 Pro vs GLM-5.3 Max. The gap between MiMo and GLM-5.3 Max is much smaller. Benchmark MiMo 2.6 Pro GLM-5.3 Max. Intelligence Index 46 45. Terminal-Bench 4.0 35% 42%. Humanity's Last Exam 49% 42%. AA-LCR v1.1 86% 80%. Cost per task $0.13 $2.01.

While MiMo scores higher on science, tough reasoning, and long-context tasks, GLM performs better on AutomationBench and Terminal-Bench. OpenAI Adds Virtual Try-On and Favorites to ChatGPT Shopping.

MiMo-V2.6-Pro uses a Mixture-of-Experts architecture with 1 trillion total parameters and 42 billion active parameters during inference.

It supports a 1-million-token context window and up to 128, 000 output tokens.

In practice, the model is published under the MIT license, allowing commercial apply and modification. Built for Agents and Long Tasks.

Xiaomi positions MiMo 2.6 Pro for complex projects, long-running tasks, research, cybersecurity, and agentic workflows.

Meanwhile, the model supports tool calling, web search, structured output, streaming, and context caching.

Xiaomi has additionally employed large-scale reinforcement learning to improve software engineering performance. During recent training, MiMo 2.6 Pro reportedly improved from 58.4 to 72.6 on DeepSWE v1.1.

Notably, the training involved around 750, 000 trajectories throughout MiMo 2.6 Flash and Pro, with Xiaomi saying the Pro model's portion cost roughly $2.62 million. Stay Connected with ProPakistani.

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For now, xiaomi Is Suddenly Competing With the World's Top AI Labs remains the part of the story worth watching, and further updates are likely as more details are confirmed.