| Signal | DeepSeek V3.2 | Delta | Gemini 2.5 Pro |
|---|---|---|---|
Capabilities | 67 | -17 | |
Benchmarks | 70 | -15 | |
Pricing | 0 | -10 | |
Context window size | 83 | -13 | |
Recency | 100 | +19 | |
Output Capacity | 20 | -60 | |
| Overall Result | 1 wins | of 6 | 5 wins |
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DeepSeek
DeepSeek V3.2 saves you $580.00/month
That's $6960.00/year compared to Gemini 2.5 Pro at your current usage level of 100K calls/month.
| Metric | DeepSeek V3.2 | Gemini 2.5 Pro | Winner |
|---|---|---|---|
| Overall Score | 74 | 85 | Gemini 2.5 Pro |
| Rank | #113 | #45 | Gemini 2.5 Pro |
| Quality Rank | #113 | #45 | Gemini 2.5 Pro |
| Adoption Rank | #113 | #45 | Gemini 2.5 Pro |
| Parameters | -- | -- | -- |
| Context Window | 164K | 1049K | Gemini 2.5 Pro |
| Pricing | $0.26/$0.38/M | $1.25/$10.00/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 83 | Gemini 2.5 Pro |
| Benchmarks | 70 | 85 | Gemini 2.5 Pro |
| Pricing | 0 | 10 | Gemini 2.5 Pro |
| Context window size | 83 | 96 | Gemini 2.5 Pro |
| Recency | 100 | 82 | DeepSeek V3.2 |
| Output Capacity | 20 | 80 | Gemini 2.5 Pro |
Our composite score (0–100) combines six weighted signals: benchmark performance (25%), pricing efficiency (25%), context window size (15%), model recency (15%), output capacity (10%), and capability versatility (10%). Here's what the scores mean for these two models:
Scores 74/100 (rank #113), placing it in the top 61% of all 290 models tracked.
Scores 85/100 (rank #45), placing it in the top 85% of all 290 models tracked.
Gemini 2.5 Pro has a 11-point advantage, which typically translates to noticeably better performance on complex reasoning, code generation, and multi-step tasks.
DeepSeek V3.2 offers 94% better value per quality point. At 1M tokens/day, you'd spend $9.60/month with DeepSeek V3.2 vs $168.75/month with Gemini 2.5 Pro - a $159.15 monthly difference.
Both models have comparable response speeds. For most applications, the latency difference is negligible.
When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.
Code generation & review
Higher benchmark score (0/100) indicates stronger performance on coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Faster response time (speed score 0/100) is critical for user-facing chat. DeepSeek V3.2 also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (1049K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.38/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (85/100) correlates with better nuance, coherence, and style in long-form content
Image understanding & OCR
Supports vision input - can analyze screenshots, diagrams, photos, and scanned documents directly
Gemini 2.5 Pro clearly outperforms DeepSeek V3.2 with a significant 10.600000000000009-point lead. For most general use cases, Gemini 2.5 Pro is the stronger choice. However, DeepSeek V3.2 may still excel in niche scenarios.
Best for Quality
DeepSeek V3.2
Marginally better benchmark scores; both are excellent
Best for Cost
DeepSeek V3.2
94% lower pricing; better value at scale
Best for Reliability
DeepSeek V3.2
Higher uptime and faster response speeds
Best for Prototyping
DeepSeek V3.2
Stronger community support and better developer experience
Best for Production
DeepSeek V3.2
Wider enterprise adoption and proven at scale
by DeepSeek
| Capability | DeepSeek V3.2 | Gemini 2.5 Pro |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
DeepSeek
DeepSeek V3.2 saves you $13.33/month
That's 94% cheaper than Gemini 2.5 Pro at 1,000 tokens/request and 100 requests/day.
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | DeepSeek V3.2 | Gemini 2.5 Pro |
|---|---|---|
| Context Window | 164K | 1.0M |
| Max Output Tokens | -- | 65,536 |
| Open Source | Yes | No |
| Created | Dec 1, 2025 | Jun 17, 2025 |
Gemini 2.5 Pro scores 85/100 (rank #45) compared to DeepSeek V3.2's 74/100 (rank #113), giving it a 11-point advantage. Gemini 2.5 Pro is the stronger overall choice, though DeepSeek V3.2 may excel in specific areas like cost efficiency.
DeepSeek V3.2 is ranked #113 and Gemini 2.5 Pro is ranked #45 out of 290+ AI models. Rankings use a composite score combining benchmark performance (25%), pricing (25%), context window (15%), recency (15%), output capacity (10%), and versatility (10%). Scores update hourly.
DeepSeek V3.2 is cheaper at $0.38/M output tokens vs Gemini 2.5 Pro's $10.00/M output tokens - 26.3x more expensive. Input token pricing: DeepSeek V3.2 at $0.26/M vs Gemini 2.5 Pro at $1.25/M.
Gemini 2.5 Pro has a larger context window of 1,048,576 tokens compared to DeepSeek V3.2's 163,840 tokens. A larger context window means the model can process longer documents and conversations.