Model Comparison
Side-by-side benchmarks, pricing, and value analysis. See which model costs less per intelligence point.
DeepSeek V3 (Dec '24) (DeepSeek) and Gemini 2.5 Pro (Google) are both large language models available via API. On list price, DeepSeek V3 (Dec '24) is cheaper, while Gemini 2.5 Pro scores higher on benchmarks. When you factor in token efficiency — how many tokens each model needs for the same task — DeepSeek V3 (Dec '24) delivers more intelligence per dollar. List prices can be misleading because different models consume different numbers of tokens for the same work. The effective costs below adjust for this using benchmark data, so you can compare what equivalent work actually costs.
| Metric | DeepSeek V3 (Dec '24) | Gemini 2.5 Pro | Gap |
|---|---|---|---|
| Output tokens/sec | N/A | 159.0 | |
| Time to first token | N/A | 35.38s | |
| Context window | 128,000 | 32,000 | 4.0x |
| Metric | DeepSeek V3 (Dec '24) | Gemini 2.5 Pro | Gap |
|---|---|---|---|
| Input price / 1M tokens | $0.4 | $1.25 | 3.1x |
| Output price / 1M tokens | $0.89 | $10.0 | 11.2x |
| Cache hit price / 1M tokens | $0.028 | $0.3 | 10.7x |
List prices adjusted for token efficiency. Different models use different numbers of tokens for the same task — these prices reflect what equivalent work actually costs.
| Metric | DeepSeek V3 (Dec '24) | Gemini 2.5 Pro | Gap |
|---|---|---|---|
| Input (adjusted) / 1M | $0.0874 | $5.7227 | 65.5x |
| Output (adjusted) / 1M | $2.4246 | $3.6707 | 1.5x |
| Input token ratio | 0.22x | 4.58x | |
| Output token ratio | 2.72x | 0.37x |
Higher is smarter, further left is cheaper. Top-left is best value. Prices adjusted for token efficiency.
Cheaper
DeepSeek V3 (Dec '24)
Higher Benchmarks
Gemini 2.5 Pro
Better Value ($/IQ point)
DeepSeek V3 (Dec '24)
DeepSeek V3 (Dec '24)
$0.15 / IQ point
Gemini 2.5 Pro
$0.27 / IQ point
DeepSeek V3 (Dec '24) is cheaper on list price. DeepSeek V3 (Dec '24) costs $0.4/M input and $0.89/M output tokens. Gemini 2.5 Pro costs $1.25/M input and $10.0/M output tokens. On combined list price, DeepSeek V3 (Dec '24) is 8.7x cheaper than Gemini 2.5 Pro. However, list prices alone can be misleading because different models use different numbers of tokens for the same task. Check the effective cost comparison above, which adjusts for token efficiency using benchmark data.
Gemini 2.5 Pro has a higher Intelligence Index (34.5) compared to DeepSeek V3 (Dec '24) (16.4). The Intelligence Index is a composite score from three industry-standard benchmarks: MMLU-Pro (general knowledge and reasoning), GPQA (graduate-level science), and AIME (mathematical problem solving). A higher score means the model produces more accurate and capable responses across a broad range of tasks. This composite approach is more reliable than any single benchmark because it measures different types of capability.
DeepSeek V3 (Dec '24) offers better value at $0.15 per intelligence point compared to Gemini 2.5 Pro at $0.27 per intelligence point. Cost per intelligence point measures how much you pay for each unit of benchmark performance, calculated as the combined token cost divided by the Intelligence Index score. When token efficiency data is available, this calculation uses effective prices (adjusted for the fact that different models consume different numbers of tokens for the same task) rather than raw list prices. A lower cost per intelligence point means you get more capability per dollar.
DeepSeek V3 (Dec '24) supports 128,000 tokens compared to Gemini 2.5 Pro with 32,000 tokens. The context window determines how much text (including your prompt, conversation history, and documents) the model can process in a single request. A larger context window is important for tasks like document summarization, long-form analysis, and multi-turn conversations with extensive history. If your use case involves processing large inputs, the context window may be a deciding factor.
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