Compare · ModelsLive · 2 picked · head to head
DeepSeek V3.2 Speciale vs Gemini 2.5 Pro
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
DeepSeek V3.2 Speciale wins on 7/9 benchmarks
DeepSeek V3.2 Speciale wins 7 of 9 shared benchmarks. Leads in speed · math · knowledge.
Category leads
speed·DeepSeek V3.2 Specialemath·DeepSeek V3.2 Specialeknowledge·DeepSeek V3.2 Specialelanguage·DeepSeek V3.2 Specialecoding·DeepSeek V3.2 Speciale
Hype vs Reality
Attention vs performance
DeepSeek V3.2 Speciale
#6 by perf·no signal
Gemini 2.5 Pro
#116 by perf·no signal
Best value
DeepSeek V3.2 Speciale
10.8x better value than Gemini 2.5 Pro
DeepSeek V3.2 Speciale
97.8 pts/$
$0.80/M
Gemini 2.5 Pro
9.0 pts/$
$5.63/M
Vendor risk
Mixed exposure
One or more vendors flagged
DeepSeek
$3.4B·Tier 1
Google DeepMind
$4.20T·Tier 1
Head to head
9 benchmarks · 2 models
DeepSeek V3.2 SpecialeGemini 2.5 Pro
Artificial Analysis · Agentic Index
Gemini 2.5 Pro leads by +32.7
Artificial Analysis Agentic Index · a composite score measuring how well a model performs in agentic workflows · multi-step tool use, planning, error recovery, and autonomous task completion. Aggregates results from multiple agentic benchmarks including SWE-bench, tool-use tests, and planning evaluations. The canonical single-number metric for "how good is this model as an agent?"
DeepSeek V3.2 Speciale
0.0
Gemini 2.5 Pro
32.7
Artificial Analysis · Coding Index
DeepSeek V3.2 Speciale leads by +5.9
Artificial Analysis Coding Index · a composite score that aggregates performance across multiple coding benchmarks into a single index. Tracks code generation quality, debugging ability, multi-language competence, and real-world software engineering tasks. Used by Artificial Analysis to rank model coding capability in a normalized, comparable format. Useful for developers choosing between models for coding-heavy workloads.
DeepSeek V3.2 Speciale
37.9
Gemini 2.5 Pro
31.9
Artificial Analysis · Quality Index
DeepSeek V3.2 Speciale leads by +2.4
DeepSeek V3.2 Speciale
29.4
Gemini 2.5 Pro
27.0
OpenCompass · AIME2025
DeepSeek V3.2 Speciale leads by +7.3
DeepSeek V3.2 Speciale
96.0
Gemini 2.5 Pro
88.7
OpenCompass · GPQA-Diamond
DeepSeek V3.2 Speciale leads by +2.0
DeepSeek V3.2 Speciale
86.7
Gemini 2.5 Pro
84.7
OpenCompass · HLE
DeepSeek V3.2 Speciale leads by +7.5
DeepSeek V3.2 Speciale
28.6
Gemini 2.5 Pro
21.1
OpenCompass · IFEval
DeepSeek V3.2 Speciale leads by +1.7
DeepSeek V3.2 Speciale
91.7
Gemini 2.5 Pro
90.0
OpenCompass · LiveCodeBenchV6
DeepSeek V3.2 Speciale leads by +9.6
DeepSeek V3.2 Speciale
80.9
Gemini 2.5 Pro
71.3
OpenCompass · MMLU-Pro
Gemini 2.5 Pro leads by +0.3
DeepSeek V3.2 Speciale
85.5
Gemini 2.5 Pro
85.8
Full benchmark table
| Benchmark | DeepSeek V3.2 Speciale | Gemini 2.5 Pro |
|---|---|---|
Artificial Analysis · Agentic Index Artificial Analysis Agentic Index · a composite score measuring how well a model performs in agentic workflows · multi-step tool use, planning, error recovery, and autonomous task completion. Aggregates results from multiple agentic benchmarks including SWE-bench, tool-use tests, and planning evaluations. The canonical single-number metric for "how good is this model as an agent?" | 0.0 | 32.7 |
Artificial Analysis · Coding Index Artificial Analysis Coding Index · a composite score that aggregates performance across multiple coding benchmarks into a single index. Tracks code generation quality, debugging ability, multi-language competence, and real-world software engineering tasks. Used by Artificial Analysis to rank model coding capability in a normalized, comparable format. Useful for developers choosing between models for coding-heavy workloads. | 37.9 | 31.9 |
Artificial Analysis · Quality Index | 29.4 | 27.0 |
OpenCompass · AIME2025 | 96.0 | 88.7 |
OpenCompass · GPQA-Diamond | 86.7 | 84.7 |
OpenCompass · HLE | 28.6 | 21.1 |
OpenCompass · IFEval | 91.7 | 90.0 |
OpenCompass · LiveCodeBenchV6 | 80.9 | 71.3 |
OpenCompass · MMLU-Pro | 85.5 | 85.8 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $0.40 | $1.20 | 164K tokens (~82 books) | $6.00 | |
| $1.25 | $10.00 | 1.0M tokens (~524 books) | $34.38 |