DeepSeek V4 Pro vs Qwen3 235B A22B Instruct 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 DeepSeek V4 Pro and Qwen3 235B A22B Instruct 2507.
DeepSeek V4 Pro
DeepSeek V4 Pro is a large-scale Mixture-of-Experts model from DeepSeek with 1.6T total parameters and 49B activated parameters, supporting a 1M-token context window. It is designed for advanced reasoning, coding,...
Qwen3 235B A22B Instruct 2507
Qwen3-235B-A22B-Instruct-2507 is a multilingual, instruction-tuned mixture-of-experts language model based on the Qwen3-235B architecture, with 22B active parameters per forward pass. It is optimized for general-purpose text generation, including instruction following,...
Technical Specifications
| Specification | DeepSeek V4 Pro | Qwen3 235B A22B Instruct 2507 |
|---|---|---|
| Provider | DeepSeek | Alibaba |
| Context Window | 1,048,576 tokens | 262,144 tokens |
| Agent Suitability | 94/100 | N/A |
| Time to First Token (TTFT) | 280 ms | N/A |
| Deployment Model | managed api | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2026-04-24 | 2025-07-21 |
API Pricing Comparison
Input Price per Million Tokens
DeepSeek V4 Pro
$0.43
Qwen3 235B A22B Instruct 2507
$0.09
Output Price per Million Tokens
DeepSeek V4 Pro
$0.87
Qwen3 235B A22B Instruct 2507
$0.10
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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.
DeepSeek V4 Pro Quirks & Gotchas
- ▸MoE architecture — cold-start latency on first request, use keep-alive
- ▸Best cost-performance ratio of any frontier model — strong tool calling for agentic use
Qwen3 235B A22B Instruct 2507 Quirks & Gotchas
No developer gotchas reported.