Gemma 4 26B A4B vs Llama 4 Maverick
How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for Gemma 4 26B A4B and Llama 4 Maverick.
Gemma 4 26B A4B
Gemma 4 26B A4B IT is an instruction-tuned Mixture-of-Experts (MoE) model from Google DeepMind. Despite 25.2B total parameters, only 3.8B activate per token during inference — delivering near-31B quality at...
Llama 4 Maverick
Meta's next-generation open weights model. Delivers premium agentic capabilities, reasoning, and tool call compliance for local or self-hosted enterprise stacks.
Technical Specifications
| Specification | Gemma 4 26B A4B | Llama 4 Maverick |
|---|---|---|
| Provider | Meta | |
| Context Window | 262,144 tokens | 1,048,576 tokens |
| Agent Suitability | N/A | 89/100 |
| Time to First Token (TTFT) | N/A | 300 ms |
| Deployment Model | managed api | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2026-04-03 | 2026-05-25 |
API Pricing Comparison
Input Price per Million Tokens
Gemma 4 26B A4B
$0.06
Llama 4 Maverick
$0.15
Output Price per Million Tokens
Gemma 4 26B A4B
$0.33
Llama 4 Maverick
$0.60
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Benchmark Performance Metrics
Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.
Gemma 4 26B A4B Quirks & Gotchas
No developer gotchas reported.
Llama 4 Maverick Quirks & Gotchas
- ▸Self-hostable via Ollama/Docker — ideal for on-premise deployments
- ▸Requires specific system prompt for optimal function calling reliability