MiniMax M3 and DeepSeek V4-Pro are both Chinese frontier models competing for the same developer audience. Both are open-weight. Both score competitively with GPT-5.5. But they take fundamentally different architectural approaches and excel at different tasks.

DeepSeek is cheaper and scores higher on pure coding benchmarks. M3 has native multimodal, computer use, and faster long-context inference. Here is how to choose.

Quick comparison

*DeepSeek V4-Pro’s SWE-bench Pro score estimated from available data.

Pricing: DeepSeek is 2-3× cheaper

The cost difference is significant:

	MiniMax M3	DeepSeek V4-Pro	Ratio
Input	$0.60/M	$0.435/M	1.4×
Output	$2.40/M	$0.87/M	2.8×
Cache	$0.12/M	$0.004/M	30×
Monthly agent (24/7)	~$360	~$200	1.8×

DeepSeek’s cache pricing ($0.004/M) is essentially free. For agent pipelines with stable system prompts, DeepSeek’s effective cost is dramatically lower.

Where DeepSeek V4-Pro wins

• Pure coding quality — Higher SWE-bench scores (80.6% Verified)
• Cost — 2-3× cheaper on output, 30× cheaper on cache
• Weights available now — Already downloadable and self-hostable
• Larger knowledge base — 1.6T total parameters (vs M3’s estimated 200-400B)
• Mathematical reasoning — 82.1% AIME 2024
• Ecosystem maturity — Larger community, more tooling, better documentation
• MiMo V2.5 Pro compatibility — Same price tier, easy to route between them

Where MiniMax M3 wins

• Native multimodal — Images and video as first-class inputs
• Computer use — Can operate a desktop (click, type, navigate)
• Long-context speed — MSA is 15.6× faster at 1M tokens (no precision loss)
• Visual code generation — 63.7% SVG-Bench (leads all models)
• Browsing — 83.5% BrowseComp (web search accuracy)
• Coding interface — Dedicated code.minimax.io environment
• Video understanding — Process video frames natively

Decision framework

Your workload	Best choice	Why
Pure text coding (budget)	DeepSeek V4-Pro	Cheaper, higher coding scores
Multimodal agent (vision + code)	MiniMax M3	Native image/video + computer use
Long-context analysis (500K+)	MiniMax M3	MSA speed advantage
Maximum cost efficiency	DeepSeek V4-Pro	2-3× cheaper
Web research agents	MiniMax M3	83.5% BrowseComp
Self-hosting today	DeepSeek V4-Pro	Weights available now
Self-hosting in 2 weeks	Either	M3 weights ~June 10
Mathematical reasoning	DeepSeek V4-Pro	82.1% AIME

Using both

Both are available on OpenRouter. Route based on task type:

def choose_model(task):
    if task.has_images or task.has_video or task.needs_browser:
        return "minimax/minimax-m3"
    else:
        return "deepseek/deepseek-v4-pro"  # Cheaper for text-only

For a broader comparison of Chinese model pricing, see Chinese AI Models Are 30× Cheaper Than American.

FAQ

Which is better for coding?

DeepSeek V4-Pro scores higher on coding benchmarks (80.6% SWE-bench Verified) and is cheaper. For pure text-based coding, DeepSeek is the better value. M3’s advantage is when coding involves visual elements (UI screenshots, diagram understanding, visual verification).

Can I switch between them easily?

Yes. Both use OpenAI-compatible APIs. Change the model string and base URL — no other code changes. Both available on OpenRouter with a single key.

Which should I self-host?

DeepSeek if you need it today (weights available). M3 if you can wait ~10 days and need multimodal. Hardware requirements are similar (both need multi-GPU setups for full precision).

How do they compare to Claude Opus 4.8?

Both are significantly cheaper than Opus (8-30×) but score lower on coding. Opus 4.8 (69.2% SWE-bench Pro) leads both. The trade-off is quality vs cost.

Is M3’s multimodal worth the price premium over DeepSeek?

Only if your workload actually uses images/video. If you are doing pure text coding, DeepSeek is strictly better value. If you need vision, computer use, or video understanding, M3 is the only option in this price range.

What about token efficiency?

MiMo V2.5 Pro uses 30-40% fewer tokens per task than most models. If you pair DeepSeek’s low pricing with MiMo’s token efficiency, you get even cheaper effective costs. M3 has not been benchmarked for token efficiency yet — expect community data within weeks of launch.

Which has better long-context performance?

Both support 1M tokens. M3’s MSA architecture is specifically optimized for long-context speed (15.6× faster decoding). DeepSeek’s MLA compresses KV cache, which saves memory but may lose precision at extreme lengths. For workloads that routinely use 500K+ tokens, M3 has the architectural advantage.

How do they compare for production reliability?

DeepSeek V4-Pro has been running in production since April 2026 with 99.5%+ uptime. M3 launched today — no production track record yet. For risk-averse deployments, DeepSeek is the safer choice until M3 proves itself over weeks of production use.

Which is better for a startup building an AI product?

If your product is text-only (chatbot, code assistant, document processor): DeepSeek V4-Pro. It is cheaper, more proven, and has a larger community. If your product involves vision, video, or computer interaction: M3 is the only option in this price range that combines all three with frontier coding quality.