Model profile
Xiaomi
New in 2026

Xiaomi: MiMo-V2-Pro

Xiaomi: MiMo-V2-Pro is a budget-priced text-first model from Xiaomi with partial runtime data, large context posture, and the clearest fit around long-context research / agent workflows.

Best for: Long-context research / Agent workflowsN/A latencyLarge contextBudget pricing
Intelligence
49.2

Benchmark blend

Coding
41.4

Dev workflow signal

Context
1049K Tokens

Large

Input Price
$0.00

Budget tier

Decision snapshot
65

Xiaomi: MiMo-V2-Pro currently reads as a budget text-first option with large context and a partially published runtime profile.

Overall profile
Selective fit
Best for
Long-context research / Agent workflows
Latency tier
N/A
Price tier
Budget
Source coverage
OpenRouterArtificial Analysis

Decision Strip

Decision rail before the raw tables

Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.

Intelligence
49.2
49

General reasoning and benchmark headroom.

Situational
Speed
N/A
N/A

Latency data is partial.

Unavailable
Context
1049K Tokens
100

How much prompt and task state can stay in view.

Above average
Price
$0.00
86

$0.00 output / 1M

Efficient

Editorial Profile

Xiaomi: MiMo-V2-Pro in one narrative

Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.

Selective fitCoding score 41Math score N/A

MiMo-V2-Pro is Xiaomi's flagship foundation model, featuring over 1T total parameters and a 1M context length, deeply optimized for agentic scenarios. It is highly adaptable to general agent frameworks like OpenClaw. It ranks among the global top tier in the standard PinchBench and ClawBench benchmarks, with perceived performance approaching that of Opus 4.6. MiMo-V2-Pro is designed to serve as the brain of agent systems, orchestrating complex workflows, driving production engineering tasks, and delivering results reliably.

Identity

Xiaomi text-first profile

Positioning

Long-context research / Agent workflows with large context and partially published runtime.

Cost posture

Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.

Strengths
  • Large context headroom supports repo-wide prompts and long research sessions.

Tradeoffs
  • Budget-friendly input pricing is a strength, but raw capability may vary by workload.

  • Latency data is incomplete, so interactive responsiveness is harder to rank confidently.

  • Current metadata points to a text-first profile rather than a broad multimodal one.

Best fit
  • Long-context summarization, repo analysis, and policy or document review.

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Intelligence
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Context
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Input Price
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Intelligence
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Context
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Input Price
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Intelligence
30.4
Context
262K Tokens
Input Price
$0.10

Benchmarks

Grouped by job-to-be-done

Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.

General intelligence

Broad reasoning, knowledge depth, and flagship benchmark posture.

Intelligence Index
49.2
GPQA
87.0%
HLE
28.3%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
41.4
SciCode
42.5%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
68.8%
TAU2
95.0%
TerminalBench Hard
40.9%
LCR
60.7%

Specs & Pricing

Technical snapshot and cost posture

Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.

Technical snapshot
Context Window
1049K Tokens
Vision
Text-first
Modalities
text
Tokenizer
Other
Max Completion
131072
Moderation
No
Supported Parameters
frequency_penaltyinclude_reasoningmax_tokenspresence_penaltyreasoningresponse_formatstoptemperaturetool_choicetoolstop_p
Input Modalities
text
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.00
Output
per 1M output tokens
$0.00
Blended
AA 3:1 mix
$0.00

This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.

OR Cache Read
$0.00

Metadata

Raw source tables at the end

Verification details remain available, but the page no longer forces them ahead of the editorial read.