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Best MiniMax Models in 2026 — Lightning Attention and 4M Context
8 min read ·
MiniMax M2.7 is the best MiniMax model in 2026, ranking first out of 136 models on the Artificial Analysis Intelligence Index with a score of 50, while costing only $0.30 per million input tokens. MiniMax's core technical innovation — lightning attention, a hybrid linear/softmax architecture that reduces computational complexity from quadratic to linear — is what enables the company to offer a 4-million-token inference context window that no other provider matches at this price point.
This is the general MiniMax model review covering architecture, competitive positioning, and pricing. If you are looking for MiniMax models specifically inside OpenClaw: read Best MiniMax Models for OpenClaw, which covers provider configuration, model IDs, and workflow fit.
MiniMax: Company Overview
MiniMax is a Shanghai-based AI company founded in early 2022 by Yan Junjie, a former SenseTime executive. The company develops multimodal AI models spanning text, audio, images, video, and music — including the Hailuo AI video generator that competes directly with OpenAI's Sora.
MiniMax held its IPO on the Hong Kong Stock Exchange in January 2026, raising approximately $538 million at a valuation of around $6.5 billion. The offering attracted 14 cornerstone investors including Abu Dhabi Investment Authority, Alibaba, and Mirae Asset Securities. Before the IPO, MiniMax had raised over $850 million in venture funding since 2023.
What makes MiniMax unusual among Chinese AI companies is its revenue mix. The company draws about two-thirds of its revenue from individual users, with Singapore and the United States as its top markets — not China. This international consumer focus is rare for a company headquartered in Shanghai and funded largely by Chinese investors.
NVIDIA CEO Jensen Huang has publicly described MiniMax as a "world-class" AI company, and MiniMax is classified as one of China's "AI tiger" startups alongside Zhipu AI, Moonshot AI (Kimi), and others.
Technical Architecture: Lightning Attention Explained
Lightning attention is the core technical innovation behind MiniMax's ability to scale context windows to 4 million tokens while keeping inference costs an order of magnitude below competitors. It was introduced in the MiniMax-01 technical report (January 2025) and remains the architectural foundation of all current MiniMax models.
Standard transformer attention has quadratic complexity — doubling the input length quadruples the computation. Lightning attention solves this by replacing most attention layers with a linear variant while keeping a small number of traditional softmax layers for retrieval precision.
The specific structure uses a repeating 8-layer block: 7 lightning attention layers (linear attention) followed by 1 softmax attention layer. The linear layers handle the bulk of token-to-token interaction at near-linear cost, while the periodic softmax layer ensures the model can still perform precise information retrieval from distant positions in the context.
Within each linear attention layer, the computation is split into intra-block (local, using standard attention on nearby tokens) and inter-block (global, using kernel-trick approximations for distant tokens). This avoids the cumulative summation bottleneck that plagues naive linear attention implementations.
The result: MiniMax-Text-01 trains at up to 1 million token context and extrapolates to 4 million tokens at inference time, achieving 100% accuracy on Needle-In-A-Haystack at the full 4-million-token length.
The MiniMax Model Lineup in 2026
MiniMax currently offers three main text model tiers, each built on the lightning attention architecture but optimized for different cost-performance tradeoffs.
| Model | Total Params | Active Params | Context | Release | Key Strength |
|---|---|---|---|---|---|
| MiniMax M2.7 | — | ~10B | 205K | Mar 2026 | Top-ranked intelligence, self-evolving |
| MiniMax M2.5 | — | — | 196K | Feb 2026 | Polyglot coding, budget pricing |
| MiniMax-Text-01 | 456B | 45.9B | 4M (inference) | Jan 2025 | Extreme context, open-source |
MiniMax M2.7 is the current flagship, released March 18, 2026. It ranks #1 on Artificial Analysis across 136 models and introduces what MiniMax calls "self-evolving" capabilities — the model can perform 30-50% of its own reinforcement learning research workflow, according to VentureBeat's coverage. It scores 56.2% on SWE-Pro and 57.0% on Terminal Bench 2.
MiniMax M2.5 launched in February 2026 with a focus on polyglot coding and multilingual performance. At $0.12/M input tokens, it performs on par with Claude Opus 4.5 at one-tenth to one-twentieth the cost — making it arguably the best cost-performance ratio in the current market.
Cost Optimizer
Cost Optimizer is the easiest first purchase when you want lower model spend without rebuilding your workflow stack.
MiniMax-Text-01 remains the foundation model with 456B total parameters (45.9B active) and the headline 4-million-token inference context. It is open-source and available on Hugging Face. MiniMax also offers MiniMax-VL-01, a vision-language variant of the same architecture.
Benchmark Comparison vs DeepSeek and Qwen
MiniMax M2.7 currently holds the top composite intelligence score across all 136 models tracked by Artificial Analysis, but the picture is more nuanced when broken down by task category.
| Benchmark | MiniMax M2.7 | DeepSeek V3.2 | Qwen3-235B |
|---|---|---|---|
| SWE-Pro | 56.2% | ~50% | ~48% |
| Terminal Bench 2 | 57.0% | — | — |
| AIME 2025 | — | 89.3% | 85.7% |
| ArenaHard | — | ~91 | 95.6 |
| Intelligence Index | 50 (#1) | ~44 | ~46 |
The competitive dynamics are clear. MiniMax M2.7 leads on software engineering and agent-style benchmarks. DeepSeek V3.2 is strongest on mathematical reasoning. Qwen3-235B is the most versatile generalist with the highest ArenaHard score. None of the three dominates every category.
Where MiniMax stands apart from both competitors is on cost-adjusted performance. At $0.30/M input tokens, M2.7 delivers frontier-level intelligence at a fraction of what DeepSeek or Qwen charge for their flagship API tiers. M2.5 extends this even further at $0.12/M input, performing comparably to Claude Opus 4.5 at roughly 5% of the cost.
MiniMax M2.7's throughput is a weak spot: 45.7 tokens per second, which is below average for models of its class according to Artificial Analysis. For latency-sensitive applications, this matters.
Pricing and Cost Efficiency
MiniMax's pricing is the most aggressive of any frontier-tier Chinese AI provider in 2026. The gap between MiniMax and Western frontier models is particularly striking.
| Model | Input (per 1M tokens) | Output (per 1M tokens) |
|---|---|---|
| MiniMax M2.7 | $0.30 | $1.20 |
| MiniMax M2.5 | $0.12 | $0.95 |
| MiniMax-Text-01 | $0.20 | $1.10 |
| DeepSeek V3.2 | ~$0.27 | ~$1.10 |
| Claude Sonnet 4.6 | $3.00 | $15.00 |
MiniMax-Text-01 at $0.20/M input is especially interesting because you get the 4-million-token context at that price — a combination no other provider offers. For document-heavy workflows where context length is the binding constraint, the economics are hard to match.
MiniMax provides both OpenAI-compatible and Anthropic-compatible API endpoints, which simplifies integration for teams already using either API format. The API is accessible through OpenRouter and other aggregators as well as directly through MiniMax's own platform.
Limitations and Tradeoffs
MiniMax's strengths come with clear tradeoffs that matter for production decisions.
Throughput is below average. M2.7 generates output at 45.7 tokens per second, which is meaningfully slower than competitors at similar model sizes. For real-time applications or high-throughput batch processing, this bottleneck can be significant.
M2.7 is text-only. Despite MiniMax's multimodal capabilities in video (Hailuo) and music, the M2.7 text model does not accept image or audio inputs. If you need multimodal text understanding, you need MiniMax-VL-01 or a different provider.
The 4M context comes from MiniMax-Text-01, not M2.7. M2.7's context window is 205K tokens — large but not the headline 4-million-token figure. The 4M context is only available through the older MiniMax-Text-01 foundation model, which is less capable on benchmarks.
Mathematical reasoning is not a strength. MiniMax's models are optimized for coding and agent workflows. If your workload is math-heavy, DeepSeek V3.2 or Qwen3-235B are better choices.
Company track record is shorter. MiniMax was founded in 2022, making it younger than Zhipu (2019) and significantly younger than Alibaba's AI division. The company is now publicly traded, which adds accountability, but the long-term model roadmap is less established than competitors with deeper research histories.
Related Guides
- Best MiniMax Models for OpenClaw
- Best Chinese Models in 2026
- Best Kimi Models in 2026
- Best Ollama Models in 2026
FAQ
What is the best MiniMax model in 2026?
MiniMax M2.7 is the best MiniMax model in 2026, ranking #1 out of 136 models on the Artificial Analysis Intelligence Index with scores of 56.2% on SWE-Pro and 57.0% on Terminal Bench 2. It costs $0.30 per million input tokens.
What is lightning attention and why does it matter?
Lightning attention is MiniMax's hybrid attention mechanism that uses 7 linear attention layers and 1 softmax layer per 8-layer block. It reduces computational complexity from quadratic to near-linear, which is what allows MiniMax-Text-01 to handle a 4-million-token context window at affordable inference costs.
How does MiniMax M2.7 compare to DeepSeek V3?
MiniMax M2.7 leads DeepSeek V3.2 on software engineering benchmarks like SWE-Pro (56.2% vs approximately 50%) and ranks higher on the Artificial Analysis composite index. DeepSeek V3.2 is stronger on mathematical reasoning with an AIME 2025 score of 89.3. Both are priced similarly, with M2.7 at $0.30/M input and DeepSeek V3.2 at approximately $0.27/M input.
Does MiniMax really support 4 million tokens of context?
MiniMax-Text-01 supports 4 million tokens at inference (trained on up to 1 million tokens), achieving 100% accuracy on Needle-In-A-Haystack at 4M tokens. However, the newer M2.7 flagship has a 205K context window, not 4M. The ultra-long context is specific to the MiniMax-Text-01 foundation model.
Is MiniMax a good choice for budget AI workloads?
MiniMax M2.5 at $0.12/M input tokens is one of the strongest budget options available in April 2026, performing comparably to Claude Opus 4.5 at roughly one-tenth to one-twentieth of the cost. For teams optimizing cost-per-quality, it is hard to beat.
Frequently Asked Questions
What is the best MiniMax model in 2026?
MiniMax M2.7 is the best MiniMax model in 2026, ranking #1 out of 136 models on the Artificial Analysis Intelligence Index with scores of 56.2% on SWE-Pro and 57.0% on Terminal Bench 2. It costs $0.30 per million input tokens.
What is lightning attention and why does it matter?
Lightning attention is MiniMax's hybrid attention mechanism that uses 7 linear attention layers and 1 softmax layer per 8-layer block. It reduces computational complexity from quadratic to near-linear, which is what allows MiniMax-Text-01 to handle a 4-million-token context window at affordable inference costs.
How does MiniMax M2.7 compare to DeepSeek V3?
MiniMax M2.7 leads DeepSeek V3.2 on software engineering benchmarks like SWE-Pro (56.2% vs approximately 50%) and ranks higher on the Artificial Analysis composite index. DeepSeek V3.2 is stronger on mathematical reasoning with an AIME 2025 score of 89.3. Both are priced similarly, with M2.7 at $0.30/M input and DeepSeek V3.2 at approximately $0.27/M input.
Does MiniMax really support 4 million tokens of context?
MiniMax-Text-01 supports 4 million tokens at inference (trained on up to 1 million tokens), achieving 100% accuracy on Needle-In-A-Haystack at 4M tokens. However, the newer M2.7 flagship has a 205K context window, not 4M. The ultra-long context is specific to the MiniMax-Text-01 foundation model.
Is MiniMax a good choice for budget AI workloads?
MiniMax M2.5 at $0.12/M input tokens is one of the strongest budget options available in April 2026, performing comparably to Claude Opus 4.5 at roughly one-tenth to one-twentieth of the cost. For teams optimizing cost-per-quality, it is hard to beat.