Google (29 models) vs DeepSeek (13 models) - compared across composite scores, pricing, capabilities, and context windows.
| Capability | DeepSeek | Leader | |
|---|---|---|---|
Vision | 25/29 | 0/13 | |
Reasoning | 17/29 | 11/13 | |
Function Calling | 19/29 | 10/13 | |
JSON Mode | 26/29 | 12/13 | |
Web Search | 16/29 | 0/13 | |
Streaming | 27/29 | 13/13 | |
Image Output | 4/29 | 0/13 |
| Metric | DeepSeek | |
|---|---|---|
| Cheapest Input (per 1M tokens) | $0.040 Gemma 3 4B | $0.140 DeepSeek V4 Flash |
| Cheapest Output (per 1M tokens) | $0.080 | $0.280 |
| Most Expensive Input (per 1M tokens) | $2.00 Gemini 3.1 Pro Preview Custom Tools | $0.700 R1 |
| Most Expensive Output (per 1M tokens) | $12.00 | $2.50 |
| Free Models | 4 | 0 |
| Max Context Window | 1.0M | 1.0M |
| Model | Score | Input $/M | Output $/M |
|---|---|---|---|
| Gemini 3 Flash Preview | 88 | $0.500 | $3.00 |
| Gemini 2.5 Pro | 84 | $1.25 | $10.00 |
| Gemini 2.5 Pro Preview 06-05 | 84 | $1.25 | $10.00 |
| Gemini 2.5 Pro Preview 05-06 | 84 | $1.25 | $10.00 |
| Gemini 3.1 Pro Preview Custom Tools | 81 | $2.00 | $12.00 |
| Gemini 3.1 Pro Preview | 81 | $2.00 | $12.00 |
| Gemma 4 31B (free) | 81 | Free | Free |
| Gemma 4 31B | 81 | $0.130 | $0.380 |
| Gemini 3.1 Flash Lite Preview | 80 | $0.250 | $1.50 |
| Gemini 2.5 Flash Lite Preview 09-2025 | 79 | $0.100 | $0.400 |
| Gemini 2.5 Flash Lite | 79 | $0.100 | $0.400 |
| Gemini 2.5 Flash | 79 | $0.300 | $2.50 |
| Gemma 2 27B | 77 | $0.650 | $0.650 |
| Gemma 4 26B A4B (free) | 73 | Free | Free |
| Gemma 4 26B A4B | 73 | $0.060 | $0.330 |
| Gemini 2.0 Flash | 72 | $0.100 | $0.400 |
| Gemini 2.0 Flash Lite | 59 | $0.075 | $0.300 |
| Lyria 3 Pro Preview | 40 | Free | Free |
| Lyria 3 Clip Preview | 40 | Free | Free |
| Gemma 3n 4B | 40 | $0.060 | $0.120 |
| Model | Score | Input $/M | Output $/M |
|---|---|---|---|
| R1 0528 | 79 | $0.500 | $2.15 |
| DeepSeek V4 Pro | 76 | $0.435 | $0.870 |
| R1 | 73 | $0.700 | $2.50 |
| DeepSeek V4 Flash | 72 | $0.140 | $0.280 |
| DeepSeek V3 0324 | 72 | $0.200 | $0.770 |
| DeepSeek V3.2 | 70 | $0.252 | $0.378 |
| DeepSeek V3.2 Exp | 70 | $0.270 | $0.410 |
| DeepSeek V3 | 70 | $0.320 | $0.890 |
| DeepSeek V3.1 Terminus | 69 | $0.270 | $0.950 |
| DeepSeek V3.1 | 69 | $0.150 | $0.750 |
| R1 Distill Llama 70B | 42 | $0.700 | $0.800 |
| DeepSeek V3.2 Speciale | 40 | $0.287 | $0.431 |
| R1 Distill Qwen 32B | 37 | $0.290 | $0.290 |
Compare any two AI providers side-by-side.
Google's free tier strategy includes older and experimental models like PaLM 2 variants and Gemini 1.0 Pro, targeting developers for prototyping and education. DeepSeek focuses exclusively on premium performance with models like V3.2 Exp (46/100 score), pricing their entire portfolio between $0.290-$2.50 per 1M tokens to position as a cost-effective alternative to GPT-4 class models without subsidizing usage.
Google's 79% vision coverage across models like Gemini 2.5 Flash Lite Preview makes them essential for multimodal applications, while DeepSeek's complete absence of vision capabilities limits them to pure text workflows. This gap particularly matters for document processing, UI automation, and visual QA tasks where DeepSeek users would need to integrate separate vision APIs, adding complexity and latency.
Google's ultra-low $0.040/M tier comes from lightweight models optimized for high-volume, simple tasks, while DeepSeek's minimum $0.290/M reflects their focus on competing with GPT-4 performance at 10x lower prices. DeepSeek's cheapest option still delivers reasoning capabilities in 10/11 models compared to Google's 16/34 (47%) reasoning coverage, making DeepSeek more consistent but less flexible for cost optimization.
Google's performance advantage stems from multimodal training and larger compute budgets, with Gemini 2.5 Flash Lite Preview achieving 60/100 while supporting vision, 1M token context, and function calling. DeepSeek's V3.2 Exp prioritizes pure reasoning efficiency at 164K context, trading raw benchmark performance for specialized depth in coding and mathematical tasks where their 91% reasoning coverage (10/11 models) provides more consistent quality than Google's scattered approach.
Google's 34-model portfolio with 9 free options enables rapid prototyping before scaling to paid tiers starting at $0.040/M tokens, while their 1M max context and 50% function calling coverage (16/34) supports diverse production needs. DeepSeek's narrower 11-model lineup with 73% function calling coverage (8/11) offers more predictable performance but requires immediate budget commitment at $0.290/M minimum, making them better for startups with validated use cases than experimental phases.