Model
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MiMo-V2.5-Pro

OPEN
Xiaomi · MiMo family · released Apr 22, 2026

Xiaomi's flagship coding/agentic model, 1.02T total / 42B active MoE, 1M context, fully open-sourced under MIT. Optimized for very long agentic trajectories (1,000+ tool calls); unlike sibling MiMo-V2.5, it is text-only (no native image/video/audio encoders).

ReasoningCodingVisionFunction callingTool useAgentic
2161.7
Elo · rank #76
Parameters
1020B
Active params
42B (MoE)
Context
1M tokens
Architecture
Sparse MoE (384 routed experts, 8/token), 70 layers (1 dense + 69 MoE), hybrid Sliding-Window+Global attention 6:1 with learnable attention-sink bias, 3x MTP modules; text-only (no native vision/audio encoders)
License
MIT
Languages
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API price (in/out)
$0.435 / $0.87
Modalities
text
Benchmark results
Bar shows position within the tracked field; marker = field best
Arena EloHuman preference1466#27
best: Claude Fable 5 · 1504
GDPval-AAAgents1581#16
best: Claude Fable 5 · 1932
GPQA DiamondReasoning86.6%#58
best: GPT-6 Astra · 96.0%
Humanity's Last ExamReasoning33.8%#49
best: Claude Opus 5.5 · 67.7%
IFBenchReasoning79.9%#8
best: MiniMax M3 · 83.0%
LiveCodeBenchCoding81.3%#48
best: DeepSeek-V4-Pro (Think Max) · 93.5%
MMLU-ProKnowledge84.6%#31
best: Claude Fable 5 · 91.5%
PinchBenchAgents87.5%#4
best: Trinity-Large-Thinking · 91.9%
SWE-bench ProCoding57.2%#29
best: Claude Opus 5.5 · 89.9%
SWE-bench VerifiedCoding74.0%#60
best: Claude Opus 5 · 97.0%
τ²-Bench TelecomAgents94.2%#15
best: Claude Opus 4.6 · 99.3%
τ³-BankingAgents8.7%#8
best: Step 5 Preview · 42.5%
Terminal-Bench 2.0Coding57.3%#52
best: DeepSeek-V4.1-Flash · 90.6%
Run it locally
VRAM @ Q4
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VRAM @ FP16
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Fits on (Q4)
Multi-node cluster required
Throughput data unavailable.
Quantizations
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Fine-tune it
Permissive
QLoRA693.6 GB4× B200 192GB
LoRA2172.6 GBbeyond 8× B200
Full fine-tune16371.0 GBbeyond 8× B200
QLoRA SFT on ~10k samples ≈ $15.02 (4× B200 192GB)
API price $0.435/$0.87 · each benchmark row carries its own source badge (see methodology)