Compare · ModelsLive · 2 picked · head to head
DeepSeek V3.2 vs Gemini 2.5 Pro
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
DeepSeek V3.2 wins on 13/22 benchmarks
DeepSeek V3.2 wins 13 of 22 shared benchmarks. Leads in speed · coding · agentic.
Category leads
speed·DeepSeek V3.2coding·DeepSeek V3.2agentic·DeepSeek V3.2reasoning·DeepSeek V3.2arena·DeepSeek V3.2knowledge·Gemini 2.5 Promath·DeepSeek V3.2language·Gemini 2.5 Pro
Hype vs Reality
Attention vs performance
DeepSeek V3.2
#108 by perf·no signal
Gemini 2.5 Pro
#100 by perf·no signal
Best value
DeepSeek V3.2
20.4x better value than Gemini 2.5 Pro
DeepSeek V3.2
191.0 pts/$
$0.27/M
Gemini 2.5 Pro
9.4 pts/$
$5.63/M
Vendor risk
Mixed exposure
One or more vendors flagged
DeepSeek
$3.4B·Tier 1
Google DeepMind
$4.00T·Tier 1
Head to head
22 benchmarks · 2 models
DeepSeek V3.2Gemini 2.5 Pro
Artificial Analysis · Agentic Index
DeepSeek V3.2 leads by +20.2
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
52.9
Gemini 2.5 Pro
32.7
Artificial Analysis · Coding Index
DeepSeek V3.2 leads by +4.8
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
36.7
Gemini 2.5 Pro
31.9
Artificial Analysis · Quality Index
DeepSeek V3.2 leads by +14.7
DeepSeek V3.2
41.7
Gemini 2.5 Pro
27.0
Aider polyglot
Gemini 2.5 Pro leads by +8.9
Aider Polyglot · measures how well AI models can edit code across multiple programming languages using the Aider coding assistant framework.
DeepSeek V3.2
74.2
Gemini 2.5 Pro
83.1
APEX-Agents
DeepSeek V3.2 leads by +0.4
APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments.
DeepSeek V3.2
7.0
Gemini 2.5 Pro
6.6
ARC-AGI
DeepSeek V3.2 leads by +16.0
ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization.
DeepSeek V3.2
57.0
Gemini 2.5 Pro
41.0
ARC-AGI-2
Gemini 2.5 Pro leads by +0.8
ARC-AGI-2 · the second iteration of the Abstraction and Reasoning Corpus, testing novel pattern recognition and abstract reasoning without prior training data.
DeepSeek V3.2
4.0
Gemini 2.5 Pro
4.9
Chatbot Arena Elo · Coding
DeepSeek V3.2 leads by +127.6
DeepSeek V3.2
1331.9
Gemini 2.5 Pro
1204.3
Chatbot Arena Elo · Overall
Gemini 2.5 Pro leads by +20.7
DeepSeek V3.2
1425.0
Gemini 2.5 Pro
1445.7
Chess Puzzles
Gemini 2.5 Pro leads by +6.0
Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities.
DeepSeek V3.2
14.0
Gemini 2.5 Pro
20.0
FrontierMath-2025-02-28-Private
DeepSeek V3.2 leads by +8.0
FrontierMath (Feb 2025) · original research-level math problems created by mathematicians, testing capabilities at the boundary of current AI mathematical reasoning.
DeepSeek V3.2
22.1
Gemini 2.5 Pro
14.1
FrontierMath-Tier-4-2025-07-01-Private
Gemini 2.5 Pro leads by +2.1
FrontierMath Tier 4 (Jul 2025) · the most challenging tier of frontier mathematics, containing problems that push the absolute limits of AI mathematical reasoning.
DeepSeek V3.2
2.1
Gemini 2.5 Pro
4.2
GPQA diamond
Gemini 2.5 Pro leads by +2.5
Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs.
DeepSeek V3.2
77.9
Gemini 2.5 Pro
80.4
OpenCompass · AIME2025
DeepSeek V3.2 leads by +4.3
DeepSeek V3.2
93.0
Gemini 2.5 Pro
88.7
OpenCompass · GPQA-Diamond
Gemini 2.5 Pro leads by +0.1
DeepSeek V3.2
84.6
Gemini 2.5 Pro
84.7
OpenCompass · HLE
DeepSeek V3.2 leads by +2.1
DeepSeek V3.2
23.2
Gemini 2.5 Pro
21.1
OpenCompass · IFEval
Gemini 2.5 Pro leads by +0.3
DeepSeek V3.2
89.7
Gemini 2.5 Pro
90.0
OpenCompass · LiveCodeBenchV6
DeepSeek V3.2 leads by +4.1
DeepSeek V3.2
75.4
Gemini 2.5 Pro
71.3
OpenCompass · MMLU-Pro
DeepSeek V3.2
85.8
Gemini 2.5 Pro
85.8
OTIS Mock AIME 2024-2025
DeepSeek V3.2 leads by +3.1
OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills.
DeepSeek V3.2
87.8
Gemini 2.5 Pro
84.7
SimpleQA Verified
Gemini 2.5 Pro leads by +28.5
SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information.
DeepSeek V3.2
27.5
Gemini 2.5 Pro
56.0
Terminal Bench
DeepSeek V3.2 leads by +7.0
Terminal-Bench 2.0 · evaluates AI agents on real terminal-based coding tasks · writing scripts, debugging, running tests, and managing projects entirely through command-line interaction. Tests both code quality and terminal fluency. Claude Opus 4.7 scores 69.4%, demonstrating significant agentic terminal competence.
DeepSeek V3.2
39.6
Gemini 2.5 Pro
32.6
Full benchmark table
| Benchmark | DeepSeek V3.2 | 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?" | 52.9 | 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. | 36.7 | 31.9 |
Artificial Analysis · Quality Index | 41.7 | 27.0 |
Aider polyglot Aider Polyglot · measures how well AI models can edit code across multiple programming languages using the Aider coding assistant framework. | 74.2 | 83.1 |
APEX-Agents APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments. | 7.0 | 6.6 |
ARC-AGI ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization. | 57.0 | 41.0 |
ARC-AGI-2 ARC-AGI-2 · the second iteration of the Abstraction and Reasoning Corpus, testing novel pattern recognition and abstract reasoning without prior training data. | 4.0 | 4.9 |
Chatbot Arena Elo · Coding | 1331.9 | 1204.3 |
Chatbot Arena Elo · Overall | 1425.0 | 1445.7 |
Chess Puzzles Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities. | 14.0 | 20.0 |
FrontierMath-2025-02-28-Private FrontierMath (Feb 2025) · original research-level math problems created by mathematicians, testing capabilities at the boundary of current AI mathematical reasoning. | 22.1 | 14.1 |
FrontierMath-Tier-4-2025-07-01-Private FrontierMath Tier 4 (Jul 2025) · the most challenging tier of frontier mathematics, containing problems that push the absolute limits of AI mathematical reasoning. | 2.1 | 4.2 |
GPQA diamond Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs. | 77.9 | 80.4 |
OpenCompass · AIME2025 | 93.0 | 88.7 |
OpenCompass · GPQA-Diamond | 84.6 | 84.7 |
OpenCompass · HLE | 23.2 | 21.1 |
OpenCompass · IFEval | 89.7 | 90.0 |
OpenCompass · LiveCodeBenchV6 | 75.4 | 71.3 |
OpenCompass · MMLU-Pro | 85.8 | 85.8 |
OTIS Mock AIME 2024-2025 OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills. | 87.8 | 84.7 |
SimpleQA Verified SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information. | 27.5 | 56.0 |
Terminal Bench Terminal-Bench 2.0 · evaluates AI agents on real terminal-based coding tasks · writing scripts, debugging, running tests, and managing projects entirely through command-line interaction. Tests both code quality and terminal fluency. Claude Opus 4.7 scores 69.4%, demonstrating significant agentic terminal competence. | 39.6 | 32.6 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $0.21 | $0.32 | 131K tokens (~66 books) | $2.41 | |
| $1.25 | $10.00 | 1.0M tokens (~524 books) | $34.38 |