MiMo V2.5 Thinking
MiMo V2.5 with Xiaomi thinking enabled. It supports deep reasoning, tool calling, structured outputs, and web search with up to 1M context.
- Reasoning
- Vision
- Audio Input
- Video Input
- Tool Calling
- Structured Output
Added Jun 3, 2026
Model weightsPricing
Auto routing · per 1M tokens- Input
- $0.14
- Output
- $0.28
- Cache read
- $0.0028
Specifications
- Context window
- 1M
- Max output
- 131.1K
- Parameters
- 309B / 15B
- Total / active
- Avg output (7d)
- 1.3K tokens
- Longer than 80% of models
Benchmarks
Benchmarks
Sourced from Artificial Analysis.
Intelligence Index
21.8
Coding Index
56.8
Agentic Index
15.8
Agentic work
AutomationBench-AA
Workflow automation with guardrail penalties
18.4%
Better than 39% of models compared
AutomationBench-AA Tasks Completed
Fully completed workflows without guardrail violations
3.3%
Better than 41% of models compared
AA-Briefcase v1.1
Agentic knowledge work (Elo)
754 Elo
Better than 31% of models compared
GDPval-AA v2.1
Economically valuable tasks (Elo)
997 Elo
Better than 44% of models compared
Document reasoning
GDP.pdf
Professional PDF reasoning: all-pass rate
4.0%
Better than 23% of models compared
AA-LCR v1.1
Long context reasoning with updated grading
73.0%
Better than 67% of models compared
Reasoning
HLE
Humanity's Last Exam
27.2%
Better than 73% of models compared
IFBench
Instruction-following benchmark
67.1%
Better than 80% of models compared
CritPt
Research-level physics reasoning
3.7%
Coding
Terminal-Bench v4.0
Practical coding and terminal tasks
0.0%
Better than 16% of models compared
SciCode
Python programming for scientific computing
43.9%
Better than 34% of models compared
Knowledge
AA-Omniscience Accuracy
Proportion of correctly answered questions
16.8%
AA-Omniscience Hallucination Rate
Rate of incorrect answers among non-correct responses
31.9%
Legacy benchmarks
GPQA Diamond (legacy)
Graduate-level scientific reasoning
84.9%
Better than 78% of models compared
Terminal-Bench Hard (legacy)
Agentic coding and terminal use
41.7%
Better than 88% of models compared
T²-Bench Telecom (legacy)
Conversational AI agents in dual-control scenarios
90.6%
Better than 85% of models compared
AA-LCR (unversioned / legacy)
Long context reasoning evaluation
73.0%
Better than 67% of models compared
GDPval-AA (unversioned / legacy)
Economically valuable tasks
24.9%
Last updated Oct 22, 2026
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