Xiaomi Proprietary Released Mar 18, 2026 Updated
MiMo-V2-Pro
ToolsGuide Scores, rankings, test results and price.
Model summary
Summary
MiMo-V2-Pro is Xiaomi's model, released Mar 18, 2026. It's ranked in 2 categories and does best at text: #81 of 416, with a ToolsGuide Score of 411 against a median of 235.
It also ranks #83 of 147 for coding (299).
Its text score uses votes only, because it has published fewer than two of the tests used for that category.
It has published results on 7 tests.
Specifications
- Creator
- Xiaomi
- Licence
- Proprietary
- Released
- Mar 18, 2026
- Context window
- –
- Input price
- –
- Output price
- –
- Tests reported
- 7
- Settings ranked
- mimo-v2-pro, MiMo-V2-Pro
Model comparison
Against the ten best text models.
Text score
ToolsGuide Score out of 1,000 · Higher is better
Coding score
ToolsGuide Score out of 1,000 · Higher is better
Input price
USD per 1M input tokens · Lower is better
Leaderboards
Where MiMo-V2-Pro sits among the top 20 in each category.
Text
#81 of 416 · ToolsGuide Score out of 1,000, where 1,000 is a flawless model
Coding
#83 of 147 · ToolsGuide Score out of 1,000, where 1,000 is a flawless model
By task
Scored from votes on each kind of prompt alone, on the same 1,000-point scale.
Against its nearest rivals
Text prompts by kind · ToolsGuide Score from votes · hover for values
- MiMo-V2-Pro
-
gpt-5.3-chat-latest - gpt-5.5-instant
Text
mimo-v2-pro · hover a bar for its rank
Coding
MiMo-V2-Pro · hover a bar for its rank
Test results
Every test MiMo-V2-Pro has a published result on, out of 100%, with its rank among the models that took it.
SWE-Bench Verified
#22 / 120Fixing real bugs in real GitHub projects, checked by the projects' own tests.
78.0%
Self-reported
Terminal-Bench 2.0
#25 / 54Getting real work done in a command-line terminal.
57.1%
Self-reported
SWE-bench Multilingual
#23 / 49A multilingual benchmark for issue resolving in software engineering that covers Java, TypeScript, JavaScript, Go, Rust, C, and C++. Contains 1,632 high-quality instances carefully annotated from 2,456 candidates by 68 expert annotators, designed to evaluate Large Language Models across diverse software ecosystems beyond Python.
71.7%
Self-reported
Tau2 Telecom
#8 / 36τ²-Bench telecom domain evaluates conversational agents in a dual-control environment modeled as a Dec-POMDP, where both agent and user use tools in shared telecommunications troubleshooting scenarios that test coordination and communication capabilities.
96.8%
Self-reported
Claw-Eval
#10 / 14Claw-Eval tests real-world agentic task completion across complex multi-step scenarios, evaluating a model's ability to use tools, navigate environments, and complete end-to-end tasks autonomously.
61.5%
Self-reported
DeepSearchQA
#6 / 10DeepSearchQA is a benchmark for evaluating deep search and question-answering capabilities, testing models' ability to perform multi-hop reasoning and information retrieval across complex knowledge domains.
86.7%
Self-reported
PinchBench
#5 / 7PinchBench evaluates coding agents on real-world agentic coding tasks, measuring both best-case and average performance across complex software engineering scenarios.
81.0%
Self-reported