Llama 4 Maverick vs Qwen3 235B A22B Thinking 2507
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 Llama 4 Maverick and Qwen3 235B A22B Thinking 2507.
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.
Qwen3 235B A22B Thinking 2507
Qwen3-235B-A22B-Thinking-2507 is a high-performance, open-weight Mixture-of-Experts (MoE) language model optimized for complex reasoning tasks. It activates 22B of its 235B parameters per forward pass and natively supports up to 262,144...
Technical Specifications
| Specification | Llama 4 Maverick | Qwen3 235B A22B Thinking 2507 |
|---|---|---|
| Provider | Meta | Alibaba |
| Context Window | 1,048,576 tokens | 262,144 tokens |
| Agent Suitability | 89/100 | N/A |
| Time to First Token (TTFT) | 300 ms | N/A |
| Deployment Model | self hostable | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2026-05-25 | 2025-07-25 |
API Pricing Comparison
Input Price per Million Tokens
Llama 4 Maverick
$0.15
Qwen3 235B A22B Thinking 2507
$0.15
Output Price per Million Tokens
Llama 4 Maverick
$0.60
Qwen3 235B A22B Thinking 2507
$1.50
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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.
Llama 4 Maverick Quirks & Gotchas
- ▸Self-hostable via Ollama/Docker — ideal for on-premise deployments
- ▸Requires specific system prompt for optimal function calling reliability
Qwen3 235B A22B Thinking 2507 Quirks & Gotchas
No developer gotchas reported.