Xiaomi MiMo icon

Xiaomi MiMo

V2.6-Pro

Xiaomi's frontier LLM series — MiMo-V2-Pro offers 1M-token context, 78% SWE-Bench Verified, and agentic orchestration at $1/M input tokens.

Content updated 2 months ago·V2.6-Pro released 24 days ago

Pricing:Free + from $0.40/per 1M input tokens (V2.5 ≤256K)
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Official Xiaomi MiMo model-family page listing current model variants

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Pros & Cons

Pros

  • $1/M input pricing significantly undercuts comparable frontier models at similar benchmark levels
  • 1M-token context window accessible without proprietary indexing or chunking setup
  • SWE-Bench Verified score of 78.0 shows MiMo-V2-Pro is highly competitive on coding tasks, but currently available public benchmark data does not support claiming it exceeds Claude Sonnet 4.6
  • Open-source weights available for V1 (MiMo-7B) and V2-Flash; established GitHub/Hugging Face presence
  • Strong agentic benchmark performance (ClawEval 61.5) with a purpose-built agent orchestration focus

Cons

  • MiMo-V2-Pro's open-weight release status is not yet confirmed; earlier versions have open weights but V2-Pro is currently API-only at launch
  • Benchmarks are primarily Xiaomi-published; independent third-party evaluations are limited at launch
  • MiMo-V2-Pro is presented as a text-first model, while Xiaomi introduces multimodal image, video, and audio capabilities separately under MiMo-V2-Omni
  • The surrounding ecosystem is still developing, with fewer public third-party integrations and deployment resources than more established model platforms
  • Platform tooling and documentation are still evolving at launch, so developers should verify current API capabilities directly on Xiaomi's platform

Overview

Xiaomi MiMo is Xiaomi's in-house foundation model series, evolving from the compact MiMo-7B reasoning model (May 2025) through the V2 generation to its current flagship, MiMo-V2-Pro. Released on March 18, 2026, MiMo-V2-Pro is a 1-trillion-parameter mixture-of-experts model with 42 billion active parameters, a 1-million-token context window, and API pricing that starts at $1 per million input tokens for prompts up to 256K tokens, with 256K–1M input priced at $2 per million.

The model ranks 8th globally and 2nd among Chinese LLMs on the Artificial Analysis Intelligence Index. It scores 78.0 on SWE-Bench Verified (software engineering) and 61.5 on ClawEval (agent capability), placing it in reach of Claude Opus 4.6 on agentic benchmarks while undercutting Claude's API pricing by roughly 15× on input tokens.

Access is available through Xiaomi's AI Studio at aistudio.xiaomimimo.com for interactive testing and the API platform at platform.xiaomimimo.com for programmatic integration.

Key Features

  • 1-Trillion-Parameter Scale — MiMo-V2-Pro's MoE architecture deploys over 1 trillion total parameters with 42 billion active per token, roughly 3× the scale of the smaller MiMo-V2-Flash. The 7:1 hybrid attention ratio (increased from V2-Flash's 5:1) delivers efficient long-context processing without proportional compute overhead.

  • 1M-Token Context Window — Supports context lengths up to 1 million tokens in the $2/M input tier, enabling full-codebase analysis, long document reasoning, and extended AI agent sessions in a single context without chunking.

  • Agentic Workflow Orchestration — Designed as "the brain behind systems and workflows." Strong performance on ClawEval (61.5, approaching Opus 4.6's 66.3) and PinchBench (81.0 average) reflects a focus on multi-step task planning, system design, and tool-use coordination rather than single-turn chat.

  • Software Engineering Capability — 78.0 on SWE-Bench Verified and 57.1 on Terminal-Bench 2.0 position MiMo-V2-Pro ahead of Claude Sonnet 4.6 on coding-specific evaluations according to Xiaomi's published benchmarks.

  • Multi-Token Prediction — Inherits MiMo-V2-Flash's Multi-Token Prediction (MTP) layer for fast token generation, improving throughput without sacrificing model quality.

  • Open-Source Heritage — The V1 MiMo-7B series was fully open-sourced on GitHub and Hugging Face. V2-Flash weights are also publicly available. Check the XiaomiMiMo GitHub organization for the current open-weight status of V2-Pro.

How It Compares

MiMo-V2-Pro Claude Opus 4.6 Claude Sonnet 4.6
ClawEval 61.5 66.3 —
SWE-Bench Verified 78.0 — 79.6%
Context window 1M tokens 1M tokens 1M tokens
Input cost (standard) $1/M $5/M $3/M
Open weights V1 + V2-Flash ✅ ❌ ❌

MiMo-V2-Pro's $1/M input pricing is 3× cheaper than Claude Sonnet 4.6 and 5× cheaper than Claude Opus 4.6 at similar benchmark performance levels. The 1M-token context tier ($2/M input) remains substantially cheaper than both Claude models at comparable context lengths.

Pricing & Plans

MiMo-V2-Pro uses tiered pricing based on context length.

API Pricing

Context Range Input Output Cache Read Cache Write
Up to 256K tokens $1.00/M $3.00/M $0.20/M Free (limited time)
256K–1M tokens $2.00/M $6.00/M $0.40/M Free (limited time)

Developer Access

  • The API platform launched with first-week free access; check platform.xiaomimimo.com for current availability and terms
  • AI Studio at aistudio.xiaomimimo.com provides interactive testing without API setup

All pricing should be confirmed at platform.xiaomimimo.com as rates may update with model availability.

Best For

  • API-cost-conscious developers building AI agent pipelines who need frontier-adjacent performance at $1/M input
  • Engineering teams requiring 1M-token context for full-codebase analysis or large document workflows
  • Researchers comparing Chinese frontier models such as GLM-5 alongside Western alternatives
  • Developers wanting to benchmark against Claude Sonnet 4.6 on SWE-Bench coding tasks with a cost-competitive alternative
  • Teams exploring AI chatbots and frontier model options beyond the standard Anthropic/OpenAI stack

FAQ

What is Xiaomi MiMo?

Xiaomi MiMo is Xiaomi's in-house foundation model family. It started with the open-source MiMo-7B reasoning model in May 2025, progressed through MiMo-V2-Flash (December 2025), and reached its current flagship with MiMo-V2-Pro in March 2026. All MiMo text models focus on reasoning, coding, and agentic task execution.

How does MiMo-V2-Pro compare to Claude Opus 4.6?

MiMo-V2-Pro scores 61.5 on ClawEval versus Claude Opus 4.6's 66.3 — close but not at parity. On coding (SWE-Bench Verified), MiMo-V2-Pro reaches 78.0 against Opus 4.6's position on that leaderboard. The key difference is cost: MiMo-V2-Pro's standard tier is $1/M input versus $5/M for Claude Opus 4.6, making it substantially more accessible for high-volume agentic workloads, especially when cost efficiency matters more than absolute benchmark leadership.

Is MiMo-V2-Pro open source?

V1 (MiMo-7B) and V2-Flash weights are publicly available on GitHub (XiaomiMiMo) and Hugging Face. V2-Pro is currently available via API only; check the XiaomiMiMo GitHub organization for any open-weight release update.

What context length does MiMo-V2-Pro support?

Up to 1 million tokens. Inputs up to 256K tokens are priced at $1/M input; inputs from 256K to 1M tokens are priced at $2/M input. This makes long-context use cases (full codebases, extended research documents) more affordable than on comparable Western frontier models.

Is there a free way to test MiMo-V2-Pro?

Yes. AI Studio at aistudio.xiaomimimo.com provides free interactive access. AI Studio offers free trial access, and the API platform launched with first-week free access; check platform.xiaomimimo.com for current terms.

Version History

V2.6-Pro

Current Version

Released on September 22, 2026

View Update
+What's new
3 updates
  • •Run 1M-context coding, research, cybersecurity, and multimodal agents on Xiaomi's native text, image, video, and audio flagship
  • •Improve long-horizon execution through scaled mixed-task RL, with Xiaomi reporting DeepSWE v1.1 rising from 58.4 to 72.6 during training
  • •Self-host MIT-licensed weights or use the standard API at $0.435 cache-miss input and $0.87 output; UltraSpeed costs $4.35 cache-miss input and $8.70 output

V2.5

Released on April 23, 2026

View Update
+What's new
3 updates
  • •Build long-running coding agents with V2.5-Pro's 1M context in public beta; Xiaomi reports nearly 1,000 tool-call rounds in internal tests using a suitable agent framework.
  • •Process images, audio, and video with V2.5's 1M context; Xiaomi reports better ClawEval results than V2-Pro at roughly 50% lower API cost, and 50% fewer tokens than Muse Spark at the same score.
  • •Access V2.5-Pro from $1/M input and $3/M output (≤256K) or $2/$6 for 256K–1M, with V2.5 at $0.40/$2 input/output, V2.5-TTS free during beta, and open-source weights planned for V2.5 and V2.5-Pro

V2-Pro

Released on March 18, 2026

View Update
+What's new
3 updates
  • •Deploy over 1 trillion parameters with 42B active, a 7:1 hybrid attention ratio, and a 1M-token context window built for production agentic workloads and complex system orchestration
  • •Reach 78.0 on SWE-Bench Verified, 61.5 on ClawEval, and 81.0 on PinchBench — ranking 8th globally on the Artificial Analysis Intelligence Index at $1/M input tokens (up to 256K)
  • •Access via Xiaomi's AI Studio for interactive testing or the API platform at tiered pricing: $1/M input for up to 256K tokens, $2/M for 256K–1M token prompts

V2-Flash

Released on December 16, 2025

+What's new
3 updates
  • •Run a 309B MoE model with 15B active parameters using a 5:1 hybrid attention architecture and Multi-Token Prediction layer for high-speed reasoning and agentic task execution
  • •Self-host reasoning and coding workloads with weights from Hugging Face and deployment guidance on GitHub; Xiaomi recommends SGLang for MiMo-V2-Flash.
  • •Switch between extended reasoning and instant answers, then run coding assistants and multi-step tool workflows with a 256K-token context window.

V1 (MiMo-7B)

Released on May 7, 2025

+What's new
3 updates
  • •Solve math and coding tasks with the original 7B MiMo model trained from scratch; Xiaomi reports 68.2 on AIME 2024 and 57.8 on LiveCodeBench v5 for MiMo-7B-RL.
  • •Download the base, SFT, RL-Zero, and RL checkpoints from XiaomiMiMo on Hugging Face or ModelScope, and use the official GitHub repository for deployment guidance.
  • •Demonstrate cost-efficient reinforcement learning for reasoning: MiMo-7B-RL achieves top-tier math and coding performance with a training approach designed for reproducibility

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