Model Comparison
Llama 4 vs MiniMax M2.7 - scale vs radical efficiency
Meta's Llama 4 family offers the longest context window in open models (10M tokens) and 400B-scale MoE architecture. MiniMax M2.7 (230B total, 10B active, 256 experts) achieves frontier-class performance at 1/50th the cost of mainstream flagship models. Two very different approaches to the same goal.
Performance
Head-to-head benchmark comparison
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.
MiniMax M2.7 launched March 2026 as a self-evolving model with 230B total parameters and only 10B active per token (8 of 256 experts). It scores 50 on the Artificial Analysis Intelligence Index and achieves 56.22% on SWE-Pro. Llama 4 Maverick (400B, 17B active) competes on general benchmarks, while Scout's 10M context window remains unmatched.
MiniMax M2.7: 10B active parameters achieving Tier-1 performance
MiniMax M2.7: SWE-Pro 56.22%, 100 tokens/second throughput
MiniMax M2.7: $0.30/M input tokens - 1/50th of flagship model pricing
Maverick: MMLU Pro 80.5%, MMMU 73.4% - strong all-around quality
Scout: 10M token context - 50x longer than M2.7's 200K
Full comparison
Llama 4 family vs MiniMax M2.7
Complete benchmark results across reasoning, coding, and efficiency metrics.
| 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 you need massive context windows, proven multimodal capabilities, or fully open-weight models for self-hosted deployment. Scout's 10M context is 50x longer than M2.7's 200K.
- 10M token context (Scout) - 50x longer than M2.7's 200K
- Fully open-weight for self-hosted deployment
- MMLU Pro 80.5% - strong general knowledge and reasoning
- MMMU 73.4% - proven multimodal understanding
- Broad ecosystem support across all major cloud providers
Choose MiniMax M2.7
When MiniMax M2.7 has the edge
MiniMax M2.7 achieves frontier-class performance with only 10B active parameters - the most efficient ratio in the industry. Its self-evolving architecture and ultra-low pricing make it compelling for cost-sensitive production workloads.
- 10B active parameters - lowest active count among frontier models
- $0.30/M input tokens - 1/50th of mainstream flagship pricing
- SWE-Pro 56.22% - strong agentic coding performance
- 100 tokens/second throughput for fast inference
- Self-evolving architecture that improves over time
Llama 4 Family
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