Xiaomi’s MiMo-V2-Pro dropped out of nowhere — literally. It spent a week on OpenRouter as an anonymous “stealth model” before Xiaomi revealed it was theirs. Now that the specs and benchmarks are public, the question is: where does it actually fit against Claude, GPT, Gemini, and DeepSeek?
Here’s the honest breakdown.
The full comparison table
| MiMo-V2-Pro | Claude Opus 4.6 | GPT-5.4 | Gemini 3.1 Pro | DeepSeek V3.2 | |
|---|---|---|---|---|---|
| Provider | Xiaomi | Anthropic | OpenAI | DeepSeek | |
| Architecture | MoE (1T/42B active) | Dense | Dense | MoE | MoE |
| Context window | 1M tokens | 1M (beta) | 1M tokens | 1M tokens | 128K tokens |
| Max output | 32K tokens | 128K tokens | 64K tokens | 64K tokens | 16K tokens |
| Input $/1M | $1.00 | $5.00 | $2.50 | $2.00 | $0.28 |
| Output $/1M | $3.00 | $25.00 | $15.00 | $12.00 | $1.10 |
| Vision | ❌ (text only) | ✅ Images | ✅ Images + video | ✅ Images + video | ✅ Images |
| Open source | ❌ (Flash is open) | ❌ | ❌ | ❌ | ✅ |
Pricing for MiMo-V2-Pro is for ≤256K context. Long context (256K–1M) doubles to $2/$6.
Benchmark comparison
Agent-focused benchmarks tell the real story for MiMo-V2-Pro, since that’s what it was built for.
| Benchmark | MiMo-V2-Pro | Claude Opus 4.6 | GPT-5.4 | Gemini 3.1 Pro |
|---|---|---|---|---|
| AA Intelligence Index | 49 (#8) | ~55 (#2) | ~53 (#3) | ~51 (#5) |
| PinchBench (agents) | ~81–84 (#3) | ~85+ (#1) | ~78 | ~75 |
| ClawEval (agents) | 61.5 (#3) | 75.7 (#1) | ~58 | ~52 |
| SWE-bench Verified | Not reported | 80.8% | ~74.9% | ~72% |
Benchmark data from Artificial Analysis, PinchBench, and ClawEval leaderboards. Some scores are approximate based on available reports.
The pattern is clear: MiMo-V2-Pro consistently lands in the #3 spot globally on agent benchmarks, behind Claude Opus 4.6 and roughly neck-and-neck with GPT-5.4. That’s remarkable for a first-generation model from a company that makes phones.
MiMo-V2-Pro vs Claude Opus 4.6
The most interesting comparison. Opus 4.6 is the current king of coding and agentic AI. MiMo-V2-Pro is explicitly trying to compete in the same space.
Where Opus wins:
- Better raw benchmark scores across the board (~10-15% ahead on agent tasks)
- 128K max output vs MiMo’s 32K — huge for code generation
- Proven track record with Claude Code, the most-used AI coding tool
- Multimodal (image understanding)
- Larger ecosystem (Anthropic API, AWS Bedrock, etc.)
Where MiMo wins:
- 8x cheaper on output ($3 vs $25 per million tokens)
- 1M context at base price (Opus charges premium above 200K)
- MoE architecture means lower inference latency for the provider
The verdict: If you need the absolute best agent/coding model and cost isn’t the primary concern, Opus 4.6 is still the pick. But if you’re running high-volume agent workloads where “90% of Opus quality” is acceptable, MiMo-V2-Pro saves you a fortune.
MiMo-V2-Pro vs GPT-5.4
GPT-5.4 is OpenAI’s flagship, released March 5, 2026. It’s the first model to beat human baseline on OSWorld (desktop computer use).
Where GPT-5.4 wins:
- Native computer use (75% on OSWorld — above human baseline)
- Stronger multimodal capabilities (images, video, audio)
- Larger max output (64K vs 32K)
- Massive ecosystem (ChatGPT, API, Azure, plugins)
Where MiMo wins:
- 5x cheaper on output ($3 vs $15 per million tokens)
- Comparable or better on agent benchmarks (ClawEval: 61.5 vs ~58)
- 1M context included at base price
The verdict: GPT-5.4 is the better generalist and the clear winner for computer use tasks. MiMo-V2-Pro is competitive on text-based agent tasks at a much lower price point.
MiMo-V2-Pro vs Gemini 3.1 Pro
Google’s latest flagship, strong on reasoning and the best value among Western models.
Where Gemini wins:
- Better reasoning scores (77.1% on ARC-AGI-2)
- Full multimodal (text, images, video)
- Deep Google ecosystem integration (Vertex AI, Workspace)
- More mature API and tooling
Where MiMo wins:
- Better agent benchmark scores (PinchBench: ~81 vs ~75)
- 2x cheaper on input, 4x cheaper on output
- Designed specifically for agentic workloads
The verdict: Gemini 3.1 Pro is the better all-rounder. MiMo-V2-Pro is the better agent model. If you’re building autonomous AI systems, MiMo has the edge. For everything else, Gemini’s ecosystem and multimodal capabilities win.
MiMo-V2-Pro vs DeepSeek V3.2
The comparison everyone’s making, given the Luo Fuli connection and the initial DeepSeek V4 speculation.
Where DeepSeek wins:
- Even cheaper ($0.28/$1.10 per million tokens)
- Open source (full weights available)
- Proven in production at massive scale
- Vision capabilities
Where MiMo wins:
- Significantly better agent benchmarks
- 1M context window (vs DeepSeek’s 128K)
- Larger active parameter count (42B vs ~37B)
- Better at complex multi-step reasoning
The verdict: Different tiers. DeepSeek V3.2 is the budget king for general tasks. MiMo-V2-Pro is a step up in capability, especially for agent workloads, at a moderate price premium. If you’re choosing between them for an agent pipeline, MiMo is worth the extra cost.
Where MiMo-V2-Pro fits in the AI landscape
Here’s how I’d map the current model landscape by capability tier and price:
Tier 1 — Frontier (best quality, highest price)
- Claude Opus 4.6 ($5/$25) — Best for coding and agents
- GPT-5.4 ($2.50/$15) — Best for computer use and generalist tasks
Tier 1.5 — Near-frontier (90% quality, much cheaper)
- MiMo-V2-Pro ($1/$3) — Best price-to-performance for agents ← new entry
- Claude Sonnet 4.6 ($3/$15) — Best value for coding
- Gemini 3.1 Pro ($2/$12) — Best value for reasoning
Tier 2 — Strong and cheap
- DeepSeek V3.2 ($0.28/$1.10) — Budget king, open source
- MiMo-V2-Flash (open source) — Top open-source coding model
Tier 3 — Ultra-budget
- Gemini 3.1 Flash-Lite ($0.25/$1.50) — Cheapest frontier-adjacent
- GPT-4o Mini ($0.15/$0.60) — Cheapest OpenAI option
MiMo-V2-Pro carves out a new “Tier 1.5” position: near-frontier agent performance at mid-tier pricing. It’s not quite Opus 4.6, but it’s close enough that the 8x price difference matters for production workloads.
When to use MiMo-V2-Pro
Good fit:
- High-volume agent pipelines where cost matters
- Long-context processing (1M tokens at $1/$3)
- Multi-step automated workflows
- Research and analysis tasks
- Batch processing where you need “good enough” at scale
Not the best fit:
- Mission-critical coding (Opus 4.6 is still more reliable)
- Multimodal tasks (MiMo-V2-Pro is text-only; use Omni for multimodal)
- Tasks requiring very long outputs (32K max vs Opus’s 128K)
- If you need a proven, battle-tested ecosystem
The bigger picture
MiMo-V2-Pro’s real significance isn’t that it’s the best model — it isn’t. It’s that a consumer electronics company built a near-frontier agent model and priced it at a fraction of the competition. That’s the trend to watch.
The AI model market is splitting into two races: a quality race at the top (Opus, GPT-5.4) and a price race in the middle (MiMo, DeepSeek, Gemini Flash). For developers building production systems, the smart play is increasingly to use frontier models for the hard stuff and route everything else to cheaper alternatives.
MiMo-V2-Pro just became one of the best “everything else” options available.
Want to understand the backstory? Read AI Dev Weekly Extra: Xiaomi’s Hunter Alpha Was Never DeepSeek V4.
Related: What Is MiMo-V2-Pro? Xiaomi’s AI Model Explained
Related: AI Model Comparison 2026: Claude vs ChatGPT vs Gemini
Related: Best AI Coding Tools in 2026