Gemma 4 vs MiniMax M2.7
Gemma 4 vs MiniMax M2.7: reasoning depth vs cost efficiency
Google's Gemma 4 and MiniMax M2.7 offer different value propositions. Gemma leads on math reasoning (89.2% AIME), multimodal, and edge deployment. MiniMax leads on cost efficiency ($0.30/M tokens), speed (100 TPS), and self-evolving training. Here's the full breakdown.
Quick verdict
When to choose each model
Both are excellent. The right choice depends on your primary use case and budget.
Choose Gemma 4 when
Math reasoning, multimodal, edge deployment, or longer context
Gemma 4 excels at mathematical reasoning (89.2% AIME), multimodal understanding (76.9% MMMU Pro), and offers the widest deployment range from 2.3B edge models to 31B flagship. 256K context window and Apache 2.0 license provide maximum flexibility.
Best for: math tutoring, document analysis, on-device AI, multimodal applications, and tasks requiring longer context windows.
Choose MiniMax M2.7 when
Cost efficiency, speed, or self-evolving capabilities
MiniMax M2.7 is #1 on the Artificial Analysis Intelligence Index (score 50/100). At $0.30/M input tokens and ~100 TPS, it's the most cost-efficient high-quality model. Its self-evolving training achieves 30% improvement through model-assisted training.
Best for: high-volume API deployments, cost-sensitive applications, real-time inference, and teams exploring self-evolving AI.
Google DeepMind
Gemma 4 31B Dense
#3 on Arena AI. 89.2% AIME, 80% LiveCodeBench, 76.9% MMMU Pro. Dense architecture with 256K context.
30.7B parameters, all active. Best for maximum quality across reasoning, coding, and multimodal tasks.
Google DeepMind
Gemma 4 26B A4B MoE
Near-31B quality at 4B inference cost. 88.3% AIME, 77.1% LiveCodeBench. 256K context.
25.2B total, 3.8B active per token. 128 experts, 8 active + 1 shared.
MiniMax
MiniMax M2.7
#1 Artificial Analysis Intelligence Index. 230B total, 10B active. Self-evolving training with 30% improvement.
256 local experts, 8 activated per token, 62 layers. $0.30/M input tokens, ~100 TPS throughput.
MiniMax
MiniMax M2.7 Self-Evolution
Model helps train itself. 30% improvement via self-evolution. Pioneering approach to model training.
Self-evolving training loop where the model generates training data and evaluates its own outputs for continuous improvement.
Head to head
Where each model wins
A category-by-category breakdown of strengths and weaknesses.
Math reasoning: Gemma wins
Gemma 4 31B: 89.2% AIME 2026. MiniMax M2.7 focuses on general intelligence rather than math-specific benchmarks. Gemma has a clear reasoning advantage.
Cost efficiency: MiniMax wins
MiniMax M2.7: $0.30/M input tokens. At this price point, MiniMax is one of the most cost-efficient high-quality models available.
Inference speed: MiniMax wins
MiniMax M2.7: ~100 TPS. With only 10B active parameters per token, MiniMax achieves exceptional throughput for real-time applications.
Multimodal: Gemma wins
Gemma 4: 76.9% MMMU Pro with native vision encoder. Gemma's multimodal capabilities are more mature and better benchmarked.
Context window: Gemma wins
Gemma 4: 256K tokens. MiniMax M2.7: 200K tokens. Gemma has a slight edge on maximum context length.
Edge deployment: Gemma wins
Gemma 4 has E2B (2.3B) and E4B (4.5B) edge models with native audio. MiniMax M2.7's 230B total model is server-only.
Architecture comparison
Traditional training vs self-evolving AI
Gemma 4 uses proven training methods at scale. MiniMax M2.7 pioneers self-evolving training where the model helps train itself.
Gemma 4 31B Dense
- 30.7B total parameters, all active per token
- Dense architecture for maximum quality
- 256K context window
- Native multimodal (text + image)
- Apache 2.0 license
MiniMax M2.7
- 230B total parameters, 10B active per token
- 256 local experts, 8 activated per token, 62 layers
- Self-evolving: model helps train itself (30% improvement)
- #1 on Artificial Analysis Intelligence Index (50/100)
- $0.30/M input tokens, ~100 TPS
Benchmarks
Complete benchmark comparison
Head-to-head benchmark results across reasoning, coding, efficiency, and deployment.
Gemma leads on reasoning, multimodal, and edge deployment. MiniMax leads on cost efficiency and inference speed. The choice depends on your priorities.
Math: Gemma 4 31B (89.2% AIME) - clear reasoning leader
Cost: MiniMax M2.7 ($0.30/M input) - extreme cost efficiency
Speed: MiniMax M2.7 (~100 TPS) - fastest inference among comparable models
Intelligence Index: MiniMax M2.7 #1 on Artificial Analysis (50/100)
Head to head
Gemma 4 vs MiniMax M2.7 on key benchmarks
Direct comparison across the most important evaluation benchmarks.
| Benchmark | Gemma 4 31B Dense 31B | Gemma 4 26B MoE 4B active 26B | MiniMax M2.7 MoE 10B active 230B | M2.7 Self-Evolved +30% improvement Evo |
|---|---|---|---|---|
MMLU Pro Knowledge & reasoning | 85.2% | 82.6% | 80.5% | 82.0% |
AIME 2026 Mathematics | 89.2% | 88.3% | 72.0% | 76.0% |
LiveCodeBench v6 Code generation | 80.0% | 77.1% | 74.0% | 77.0% |
SWE-Bench Pro Agentic coding | - | - | 56.22% | - |
MMMU Pro Multimodal | 76.9% | 73.8% | 68.0% | 71.0% |
Arena AI ELO Human preference | 1452 | 1441 | - | - |
Intelligence Index Artificial Analysis | - | - | 50/100 (#1) | - |
Inference Speed Tokens per second | - | - | ~100 TPS | ~100 TPS |
API Cost Per million input tokens | - | - | $0.30 | $0.30 |
Context Window Max tokens | 256K | 256K | 200K | 200K |
Active params Per token | 30.7B | 3.8B | 10B | 10B |
License Commercial use | Apache 2.0 | Apache 2.0 | Open Weights | Open Weights |
Data from official model cards and independent evaluations. Scores may vary by evaluation methodology.
Self-Evolution
Self-evolving AI: MiniMax M2.7's breakthrough approach
MiniMax M2.7 pioneers self-evolving training where the model helps generate its own training data and evaluates outputs. This achieves a 30% improvement over base training, pointing toward a future where models continuously improve themselves.
- Self-evolving training: model helps train itself for 30% improvement
- #1 on Artificial Analysis Intelligence Index (score 50/100)
- 256 local experts, 8 activated per token, 62 layers
Reasoning & Vision
Math reasoning and multimodal: Gemma 4's strongest areas
Gemma 4's 89.2% on AIME 2026 and 76.9% on MMMU Pro represent best-in-class performance. For tasks requiring deep mathematical reasoning or visual understanding, Gemma 4 is the stronger choice.
- AIME 2026: Gemma 4 89.2% - top-tier math reasoning
- MMMU Pro: Gemma 4 76.9% - native multimodal vision
- Edge models: E2B (2.3B) and E4B (4.5B) with native audio
Cost & Speed
Extreme cost efficiency: MiniMax M2.7 at $0.30/M tokens
MiniMax M2.7's $0.30/M input tokens and ~100 TPS throughput make it the most cost-efficient high-quality model. For high-volume deployments where cost matters, MiniMax offers exceptional value.
- MiniMax M2.7: $0.30/M input tokens - extreme cost efficiency
- ~100 TPS throughput with only 10B active parameters
- Gemma 4: Apache 2.0 for self-hosted deployments at zero API cost
Try both
Test the models yourself
The best comparison is hands-on experience.
Gemma 4 resources
Get started with Gemma 4
Everything you need to start building with Gemma 4.
MiniMax M2.7 resources
Learn more about MiniMax M2.7
Official MiniMax M2.7 resources and documentation.
Open model landscape
The best open models of 2026
Gemma 4 and MiniMax M2.7 represent different priorities in open AI, but they're not the only options.
Try Gemma 4
Experience Gemma 4's strengths firsthand
Try Gemma 4 for free and see how it performs on your specific tasks. Math reasoning, multimodal understanding, and edge deployment are where it shines brightest.