Model Comparison
Llama 4 vs MiniMax M2.7 - open weight scale versus radical parameter efficiency
The Llama 4 vs MiniMax M2.7 comparison reveals two radically different philosophies for building frontier AI. Meta's Llama 4 family pushes scale with a 400B parameter Maverick model and Scout's unprecedented 10M token context window, backed by a mature open weight ecosystem. MiniMax M2.7 takes the opposite approach, achieving frontier class benchmark scores with just 10B active parameters out of 230B total, routing through 256 experts at a cost of only $0.30 per million input tokens. That makes MiniMax M2.7 roughly 50x cheaper than mainstream flagship models while delivering competitive quality. For teams evaluating Llama 4 vs MiniMax M2.7, this is a choice between proven open weight infrastructure and a new generation of ultra efficient architecture.
Performance
Llama 4 vs MiniMax M2.7 benchmark breakdown
MiniMax M2.7 achieves remarkable benchmark scores with only 10B active parameters, while Llama 4 offers unmatched context length and proven open weight ecosystem support. The efficiency gap between these two architectures creates very different deployment economics.
MiniMax M2.7 launched in March 2026 as a self evolving model with 230B total parameters and only 10B active per token, selecting 8 out of 256 experts per forward pass. It scores 50 on the Artificial Analysis Intelligence Index and achieves 56.22% on SWE Pro, placing it firmly in frontier territory despite its lean active footprint. The model generates 100 tokens per second and costs just $0.30 per million input tokens. On the Llama 4 side, Maverick brings 400B total parameters with 17B active and scores 80.5% on MMLU Pro, while Scout extends the context window to an industry leading 10M tokens. For production teams weighing Llama 4 vs MiniMax M2.7, the decision often hinges on whether you prioritize raw context capacity and ecosystem maturity or maximum cost efficiency with competitive quality.
MiniMax M2.7: only 10B active parameters achieving Tier 1 frontier performance across major benchmarks
MiniMax M2.7: SWE Pro 56.22% and 100 tokens per second throughput for fast, capable inference
MiniMax M2.7: $0.30 per million input tokens, roughly 50x cheaper than mainstream flagship model pricing
Maverick: MMLU Pro 80.5% and MMMU 73.4% for strong general reasoning and multimodal understanding
Scout: 10M token context window, 50x longer than MiniMax M2.7's 200K limit
MiniMax M2.7 uses 256 experts with 8 selected per token, the highest expert count in any production MoE model
Full comparison
Llama 4 family vs MiniMax M2.7
Complete benchmark results across reasoning, coding, and efficiency metrics for the full Llama 4 vs MiniMax M2.7 comparison.
| Benchmark | Llama 4 Maverick 400B / 17B active Open Weight | Llama 4 Scout 109B / 17B active Long Context | MiniMax M2.7 230B / 10B active Efficient |
|---|---|---|---|
MMLU Pro Knowledge & reasoning | 80.5% | 74.3% | - |
MMMU Multimodal | 73.4% | 69.4% | - |
SWE-Pro Agentic coding | - | - | 56.22% |
Intelligence Index Artificial Analysis | - | - | 50 |
Context Window Max tokens | 1M | 10M | 200K |
Total Parameters Model size | 400B | 109B | 230B |
Active Parameters Per token | 17B | 17B | 10B |
Number of Experts MoE routing | 128 | 16 | 256 (8 selected) |
Throughput Tokens per second | - | - | 100 TPS |
API Input Cost Per million tokens | Varies | Varies | $0.30 |
Data from Meta's official model card, MiniMax's technical report, and independent evaluations.
Choose Llama 4
When to choose Llama 4 over MiniMax M2.7
Llama 4 is the better choice when your workload demands massive context windows, proven multimodal capabilities, or the security of a fully open weight model with broad ecosystem support. Scout's 10M token context is 50x longer than MiniMax M2.7's 200K limit, making it essential for applications that need to process entire codebases, legal document sets, or extended conversation histories in a single pass. Maverick's 80.5% on MMLU Pro and 73.4% on MMMU demonstrate consistently strong performance across both text and visual tasks. The Llama 4 ecosystem also benefits from years of community investment in fine tuning tools, quantization methods, and production deployment guides.
- 10M token context with Scout is 50x longer than MiniMax M2.7's 200K window, essential for full codebase analysis and long document processing
- Fully open weight model with downloadable weights for complete control over deployment, fine tuning, and data privacy
- MMLU Pro 80.5% on Maverick places it among the top open weight models for complex reasoning and knowledge tasks
- MMMU 73.4% demonstrates proven multimodal understanding across images, charts, diagrams, and visual content
- Available on all major cloud providers including AWS, Azure, Google Cloud, and dozens of inference platforms worldwide
- Mature open weight community with extensive fine tuning guides, quantization tools, and battle tested production recipes
Choose MiniMax M2.7
When MiniMax M2.7 wins the comparison against Llama 4
MiniMax M2.7 achieves frontier class performance with only 10B active parameters, making it the most parameter efficient model in its quality tier. Its self evolving architecture continuously improves through deployment feedback, and the $0.30 per million input token pricing makes it roughly 50x cheaper than mainstream flagship models. For teams that need strong AI capabilities without massive GPU budgets, MiniMax M2.7 represents a fundamentally new approach to the cost versus quality tradeoff. The 256 expert MoE design routes each token through just 8 specialists, keeping compute requirements minimal while maintaining broad task coverage.
- Only 10B active parameters per token, the lowest active count among any model achieving frontier class benchmark scores
- $0.30 per million input tokens makes MiniMax M2.7 roughly 50x cheaper than mainstream flagship models for API based workloads
- SWE Pro 56.22% demonstrates strong agentic coding performance competitive with much larger models
- 100 tokens per second throughput enables fast, responsive inference even for interactive applications
- Self evolving architecture that continuously improves through deployment feedback without requiring manual retraining
- 256 expert MoE design with 8 selected per token provides the broadest specialist coverage of any production model
FAQ
Frequently asked questions about Llama 4 vs MiniMax M2.7
Answers to the most common questions developers and teams ask when choosing between Llama 4 and MiniMax M2.7 for production workloads and cost efficient deployment.
MiniMax M2.7 uses a 256 expert Mixture of Experts architecture that selects just 8 specialists per token. This means the model has 230B total parameters worth of knowledge but only activates 10B for any given input, keeping compute costs extremely low. The large expert pool allows each token to be routed to highly specialized subnetworks, achieving quality that rivals models with much higher active parameter counts.
MiniMax M2.7 is significantly cheaper for API based workloads at $0.30 per million input tokens, roughly 50x less than mainstream flagship pricing. However, for self hosted deployment, Llama 4 Maverick's 17B active parameters are only moderately larger than MiniMax M2.7's 10B, so the gap narrows when you own the hardware. The biggest cost difference shows up in high volume API usage where MiniMax M2.7's pricing is hard to beat.
Self evolving refers to MiniMax M2.7's ability to improve its performance over time through deployment feedback loops. Unlike traditional models that remain static after training, MiniMax M2.7 incorporates signals from real world usage to refine its expert routing and response quality. This means the model you use today may perform better on your specific tasks next month without requiring you to retrain or fine tune anything.
MiniMax M2.7 covers a broad range of tasks including coding, reasoning, and general conversation. However, Llama 4 Maverick has stronger demonstrated performance on multimodal tasks with 73.4% on MMMU and general knowledge with 80.5% on MMLU Pro. MiniMax M2.7 excels on coding benchmarks with 56.22% on SWE Pro and offers much lower inference costs. The best choice depends on whether your workload is primarily text and code or requires significant visual understanding.
Llama 4 wins decisively on context length. Scout supports 10M tokens, which is 50x longer than MiniMax M2.7's 200K token limit. Even Maverick offers 1M tokens, still 5x more than MiniMax M2.7. If your application needs to process very long documents, maintain extended conversation history, or analyze entire codebases in a single pass, Llama 4 is the clear choice in this comparison.
MiniMax M2.7 provides API access and has released technical details about its architecture, but its weight availability and licensing terms differ from Llama 4's fully open weight approach. Llama 4 models can be downloaded and self hosted under the Llama 3.1 Community License, giving teams complete control over deployment and data privacy. Check MiniMax's latest release notes for the most current information on weight access and licensing.
Both models use Mixture of Experts but at very different scales. Llama 4 Maverick has 128 experts with 17B active parameters out of 400B total. MiniMax M2.7 pushes this further with 256 experts and only 10B active out of 230B total, selecting just 8 experts per token. The higher expert count in MiniMax M2.7 allows for more specialized routing, which helps explain how it achieves strong performance with fewer active parameters.
MiniMax M2.7 is the stronger choice for budget constrained teams. At $0.30 per million input tokens and 100 tokens per second throughput, it delivers frontier class quality at a fraction of typical costs. Llama 4 Scout and Maverick require more substantial GPU infrastructure for self hosting due to their larger active parameter counts. However, if your startup needs long context processing or multimodal capabilities, Llama 4 may justify the higher infrastructure investment.
Llama 4 Family
Explore more Llama 4 comparisons and models
Dive deeper into individual Llama 4 models or see how they stack up against other frontier open weight models. Each comparison page includes full benchmark data, architecture details, and deployment guidance to help you make the right choice.
Llama 4 Scout
The 10M context window specialist with 109B total parameters and 17B active, built for long document processing and extended conversations
ExploreLlama 4 Maverick
Meta's 400B flagship with 128 experts and 17B active parameters, delivering top tier multimodal and reasoning performance
ExploreAll Llama 4 Models
Complete overview of every model in the Llama 4 family including Scout, Maverick, and Behemoth with full specs and benchmarks
ExploreLlama 4 vs Kimi K2.6
Compare Meta's open weight MoE architecture against Moonshot's Kimi K2.6 across reasoning, coding, and multilingual tasks
CompareLlama 4 vs Qwen 3.6
See how Llama 4 measures up against Alibaba's Qwen 3.6 on benchmarks, context length, and deployment flexibility
CompareLlama 4 vs DeepSeek V4
Trillion parameter scale meets long context as Llama 4 faces DeepSeek V4 Pro's 80.6% SWE Bench coding performance
CompareGet started
Try Llama 4 models for free
Start chatting with Llama 4 Maverick or Scout instantly. No setup required. Compare the models yourself and see which one fits your workflow best.