DeepSeek V4 Flash vs Gemini 3.5 Flash
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 Flash and Gemini 3.5 Flash.
DeepSeek V4 Flash
DeepSeek V4 Flash is an efficiency-optimized Mixture-of-Experts model from DeepSeek with 284B total parameters and 13B activated parameters, supporting a 1M-token context window. It is designed for fast inference and...
Gemini 3.5 Flash
Gemini 3.5 Flash is Google's high-efficiency multimodal model, bringing near-Pro level coding and reasoning at Flash-tier cost and speed. It is highly optimized for coding proficiency and parallel agentic execution...
Technical Specifications
| Specification | DeepSeek V4 Flash | Gemini 3.5 Flash |
|---|---|---|
| Provider | DeepSeek | |
| Context Window | 1,048,576 tokens | 1,048,576 tokens |
| Agent Suitability | 86/100 | 88/100 |
| Time to First Token (TTFT) | 120 ms | 200 ms |
| Deployment Model | managed api | managed api |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2026-04-24 | 2026-05-19 |
API Pricing Comparison
Input Price per Million Tokens
DeepSeek V4 Flash
$0.09
Gemini 3.5 Flash
$1.50
Output Price per Million Tokens
DeepSeek V4 Flash
$0.18
Gemini 3.5 Flash
$9.00
Want to test both models live?
Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.
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 Flash Quirks & Gotchas
- ▸Best cost-per-token ratio of any hosted API — ideal for high-throughput pipelines
- ▸Lower agentic performance vs V4 Pro — route complex tool calls accordingly
Gemini 3.5 Flash Quirks & Gotchas
- ▸Excellent multimodal performance — native video understanding
- ▸Tool calling via Google's native function_declarations in Vertex AI