DeepSeek V3.2vsGemini 2.5 Pro Preview 06-05
DeepSeek V3.2 is cheaper than Gemini 2.5 Pro Preview 06-05 at $0.28/M vs $1.25/M input tokens.
| Spec | DeepSeek V3.2 | Gemini 2.5 Pro Preview 06-05 |
|---|---|---|
| Input price | $0.28/M tokens | $1.25/M tokens |
| Output price | $0.42/M tokens | $10/M tokens |
| Context window | 131K tokens | 1.0M tokens |
| Weights | Open | Closed |
| Free API (OpenRouter) | No | No |
| Released | Dec 2025 | Jun 2025 |
| SWE-bench Verified | 73.1% | 67.2% |
Our Verdict
Gemini 2.5 Pro Preview 06-05
DeepSeek V3.2Runner-up
Not enough votes to call it. On the specs, Gemini 2.5 Pro Preview 06-05 has the edge: bigger model tier, bigger context window.
DeepSeek V3.2 costs 24x less per token.
Too close to call
API pricing
Cost per 1M tokens
DeepSeek V3.2
Input
$0.28
4.5× cheaper
Output
$0.42
24× cheaper
Gemini 2.5 Pro Preview 06-05
Input
$1.25
Output
$10.00
DeepSeek V3.2 is cheaper on both: 4.5× input, 24× output.
Where to run it
17 hosts, cheapest first
DeepSeek V3.215 hosts
GMI Cloudfp8$0.21 in$0.31 out164k99.8% upStreamLakefp8$0.21 in$0.32 out128k99.6% upDigitalOcean$0.25 in$0.80 out164k97.8% upSiliconFlowfp8$0.26 in$0.42 out164k98.1% upAtlasCloudfp8$0.26 in$0.38 out164k99.7% upDeepInfrafp4$0.26 in$0.38 out164k99.5% up
9 more hostsFewer hosts
Novitafp8$0.27 in$0.40 out164k100% upBaidu Qianfanfp8$0.28 in$0.42 out131k100% upVenice$0.33 in$0.48 out160k99.4% upAlibaba Cloudfp8$0.37 in$1.11 out131k99.3% upFriendli$0.50 in$1.50 out164k99.9% upGoogle Vertex AI$0.56 in$1.68 out164k99.2% upPhala$1.00 in$1.00 out164k98.9% upMara$3.00 in$4.50 out33k89.3% upSambaNova$3.00 in$4.50 out33k96.5% up
Gemini 2.5 Pro Preview 06-052 hosts
Per million tokens. Prices and uptime via OpenRouter, checked 9 Sep 2026.
Writing DNA
Style Comparison
Similarity
53%
DeepSeek V3.2 uses 2.4x more transitions
DeepSeek V3.2
Gemini 2.5 Pro Preview 06-05
54%Vocabulary51%
29wSentence Length15w
0.58Hedging0.31
5.9Bold4.8
4.8Lists4.1
0.00Emoji0.00
0.74Headings0.98
0.12Transitions0.05
Based on 28 + 20 text responses
Research
What we learned reading every model
FAQ
Common questions
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