DeepSeek V4 is the stronger model; MiniMax-M2 is often the smarter deployment. M2's 10B active parameters serve agent loops at a fraction of the latency and cost of V4's 49B, and its tool-use tuning shows on Terminal-Bench. Pick V4 for hard reasoning and long context; pick M2 when you are running thousands of agent steps and the meter is running.
MiniMax-M2 vs DeepSeek at a glance
| MiniMax-M2 | DeepSeek V4 | |
|---|---|---|
| Vendor | MiniMax (China) | DeepSeek (China) |
| Open weights | Yes — downloadable | Yes — downloadable |
| License | MIT — weights free to download, self-host, fine-tune, and use commercially | MIT — weights free to download, self-host, fine-tune, and use commercially |
| Parameters | 230B (10B active) | 1.6T (49B active) |
| Context window | ~200K tokens | 1M tokens |
| Modalities | text | text |
| Pricing | Varies by hosting provider (open weights) | $1.74 in / $3.48 out per 1M tokens |
| Released | 2025-10-27 | 2026-04-24 |
Specs and pricing verified July 2026.
About MiniMax-M2
MiniMax-M2 is the tool-use specialist of the open-weight field: 230B total parameters with just 10B active, MIT-licensed, and tuned specifically for agentic loops — 69.4% SWE-bench Verified, 46.3 Terminal-Bench, 83 LiveCodeBench. Mid-2026 roundups still rank it the best open model for tool-driven agents.
The 10B active footprint is the strategic choice: agent workloads burn tokens on long multi-step loops, and M2 delivers near-frontier agentic quality at interactive speed and low serving cost. For teams building autonomous agents rather than chat products, M2 is frequently the efficiency-optimal pick.
Full specs, benchmarks, and hardware guidance: the MiniMax-M2 page.
About DeepSeek V4
DeepSeek V4 Pro is the flagship of DeepSeek's V4 family, released on April 24, 2026, and the strongest argument yet that open-weight models compete at the frontier. It is a 1.6-trillion-parameter mixture-of-experts model that activates just 49B parameters per token, pairs a 1M-token context window with up to 384K tokens of output, and ships under a plain MIT license — no usage thresholds, no acceptable-use gate.
The headline engineering story is the attention stack. V4 combines Compressed Sparse Attention (CSA), which compresses KV entries 4× along the sequence with softmax-gated pooling, with Heavily Compressed Attention (HCA) at 128× compression. The practical result: at 1M-token context, V4 Pro needs roughly 27% of the single-token inference FLOPs and 10% of the KV cache of DeepSeek-V3.2. Long context stopped being a luxury and became the default across DeepSeek's services.
Full specs, benchmarks, and hardware guidance: the DeepSeek V4 page.
Choosing between them
Choose MiniMax-M2 for:
- Tool-calling agents and terminal/computer-use workloads
- High-throughput agent loops where per-token cost compounds
- Self-hosting on a modest multi-GPU node (MIT license)
Choose DeepSeek V4 for:
- Frontier-level reasoning and coding on an open license
- Very long documents and repositories (1M-token context as the default)
- Teams that need Anthropic/OpenAI API compatibility with open-weight economics
Frequently asked questions
Is MiniMax-M2 better than DeepSeek V4?
DeepSeek V4 is the stronger model; MiniMax-M2 is often the smarter deployment. M2's 10B active parameters serve agent loops at a fraction of the latency and cost of V4's 49B, and its tool-use tuning shows on Terminal-Bench. Pick V4 for hard reasoning and long context; pick M2 when you are running thousands of agent steps and the meter is running.
Which is cheaper: MiniMax-M2 or DeepSeek V4?
MiniMax-M2: Varies by hosting provider (open weights). DeepSeek V4: $1.74 in / $3.48 out per 1M tokens. Both are open-weight, so self-hosting costs depend on your hardware and utilization.
Can I self-host MiniMax-M2 and DeepSeek V4?
MiniMax-M2: yes — weights are published under MIT. DeepSeek V4: yes — weights are published under MIT.