MiniMax M3 and MiMo V2.5 Pro are both Chinese frontier models targeting developers. Both cost under $3 per million output tokens. Both compete with GPT-5.5. But they optimize for completely different things.

M3 is a multimodal powerhouse — native vision, video, computer use, and the fastest long-context inference via MSA. MiMo is a token efficiency specialist — uses 40-60% fewer tokens per task with optimized tool calling for autonomous agents.

Quick comparison

Pricing: MiMo is cheaper (but the gap narrows with efficiency)

	MiniMax M3	MiMo V2.5 Pro	Ratio
Input	$0.60/M	$0.435/M	1.4×
Output	$2.40/M	$0.87/M	2.8×
Cache	$0.12/M	$0.0036/M	33×

MiMo is cheaper per token. But MiMo also uses 40-60% fewer tokens per task. Combined effect:

100 coding tasks	MiniMax M3	MiMo V2.5 Pro
Avg tokens per task	~3,000 output	~1,800 output
Output cost	$0.72	$0.16
Effective cost ratio	—	4.5× cheaper

When you factor in token efficiency, MiMo is effectively 4-5× cheaper per task, not just 2.8×.

Where MiniMax M3 wins

Multimodal (unique)

M3 handles images, video, and desktop operation. MiMo is text-only. For any workflow involving visual input — UI testing, screenshot analysis, video processing, chart reading, visual code verification — M3 is the only option.

Long-context speed (MSA)

MSA delivers 15.6× faster decoding at 1M tokens. MiMo uses standard attention which slows at long contexts. For workloads that routinely use 500K+ tokens, M3 responds faster.

Browsing agents

83.5% BrowseComp makes M3 excellent for web research, information gathering, and search-heavy agent workflows.

Higher SWE-bench Pro

59.0% on SWE-bench Pro (the harder variant) vs MiMo’s 79.2% on SWE-bench Verified (the easier variant). Direct comparison is difficult, but M3 has proven Pro-level coding capability.

Where MiMo V2.5 Pro wins

Token efficiency (40-60% fewer tokens)

MiMo’s core advantage. Trained specifically to solve problems concisely. A task that takes most models 3,000 tokens takes MiMo ~1,800. This means faster responses, lower costs, and more context available. See our token efficiency analysis.

Tool calling (97.2%, 1000+ calls/session)

MiMo was designed for autonomous agent sessions with 1,000+ tool calls. At 97.2% per-call accuracy, it maintains coherence over very long agent loops. M3’s 74.2% MCP Atlas is good but not at the same level for sustained multi-step execution.

Cache pricing (33× cheaper)

$0.0036/M vs $0.12/M for cached tokens. Agent pipelines that reuse system prompts (most do) hit cache constantly. MiMo’s cache pricing makes repeated context essentially free.

Cost per task (4-5× cheaper)

Lower per-token price × fewer tokens per task = dramatic cost advantage for sustained workloads. A 24/7 agent on MiMo costs ~$150/month vs ~$360/month on M3.

Claude Code integration

MiMo has first-class Claude Code setup via the Anthropic-compatible endpoint. M3 requires Aider or Continue.

Use case recommendations

Workload	Best model	Why
Autonomous coding agent (budget)	MiMo V2.5 Pro	4.5× cheaper per task
Visual/multimodal agent	MiniMax M3	Only option
Long-running agent (1000+ tool calls)	MiMo V2.5 Pro	97.2% tool accuracy
Video processing	MiniMax M3	Native video
Web research agent	MiniMax M3	83.5% BrowseComp
Long-context codebase analysis	MiniMax M3	MSA speed advantage
Maximum cost efficiency	MiMo V2.5 Pro	Token efficiency + low pricing
Computer use / GUI testing	MiniMax M3	Desktop operation
Claude Code user	MiMo V2.5 Pro	Native integration
Self-hosting (today)	MiMo V2.5 Pro	Weights available now
Self-hosting (after June 10)	Either	M3 weights dropping soon

Using both

Route by task type on OpenRouter:

def choose_model(task):
    if task.has_images or task.has_video or task.needs_browser:
        return "minimax/minimax-m3"
    else:
        return "mimo-v2.5-pro"  # Cheaper + more efficient for text tasks

For a broader view of the Chinese AI pricing landscape, see Chinese AI models are 30× cheaper.

FAQ

Which is better for coding?

For pure text coding: MiMo wins on efficiency and cost. For coding with visual elements (UI verification, diagram-to-code): M3 wins on capability. Quality is similar for standard coding tasks.

Can MiMo’s token efficiency make up for the capability gap?

For most tasks, yes. MiMo produces equivalent quality code in fewer tokens. The cases where M3 genuinely beats MiMo are multimodal tasks and complex browsing — things MiMo simply cannot do.

Which is better for our AI Startup Race?

We use MiMo V2.5 Pro for the Xiaomi agent — 5 sessions/day, 456+ sessions total, 371 pages built. Its token efficiency and agentic optimization made it the most productive agent by output. M3 would be interesting for visual verification but was not available when the race started.

How do cache costs compare in practice?

For a typical agent pipeline with a 4K token system prompt reused across 100 calls:

• M3: 100 × 4K × $0.12/M = $0.048
• MiMo: 100 × 4K × $0.0036/M = $0.0014

MiMo’s cache is 34× cheaper. Over thousands of calls per day, this adds up.

Which will be easier to self-host?

MiMo V2.5 Pro (dense, smaller) is likely easier to fit on consumer hardware. M3 (200-400B estimated) needs more RAM. Both are open-weight. See how to run M3 locally.