software
DeepSeek vs Gemini 2026: AI Reasoning Cost Efficiency
DeepSeek is a Chinese AI model optimized for reasoning and code with lower computational costs, while Google's Gemini is a broader multimodal AI assistant with superior image understanding and tight Google ecosystem integration. DeepSeek excels in mathematical problem-solving, whereas Gemini dominates in creative tasks and real-time information access.
D
DeepSeek
Chinese AI model focused on reasoning, mathematics, and code efficiency with low computational overhead.
Developers, researchers, and organizations prioritizing cost-effective AI inference for reasoning tasks, mathematical problem-solving, and code generation
Score63%
VS
G
Gemini
Google's multimodal AI assistant with real-time web access, image generation, and tight integration with Google services.
Enterprise teams, creative professionals, and Google Workspace users needing multimodal AI with real-time information and image generation
Score63%
Quick Answer
AI SummaryDeepSeek is a Chinese AI model optimized for reasoning and code with lower computational costs, while Google's Gemini is a broader multimodal AI assistant with superior image understanding and tight Google ecosystem integration. DeepSeek excels in mathematical problem-solving, whereas Gemini dominates in creative tasks and real-time information access.
Our Verdict
AI-assistedChoose DeepSeek if you prioritize cost-efficient reasoning tasks, mathematical problem-solving, and code generation without cloud ecosystem lock-in. Choose Gemini if you need multimodal capabilities, real-time web integration, image generation, and seamless Google Workspace connectivity for enterprise or creative professional use.
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D
DeepSeek
7.3/10
Gemini
7.7/10
G
D
Choose DeepSeek if
Developers, researchers, and organizations prioritizing cost-effective AI inference for reasoning tasks, mathematical problem-solving, and code generation
G
Choose Gemini if
Best pickEnterprise teams, creative professionals, and Google Workspace users needing multimodal AI with real-time information and image generation
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Key Differences at a Glance
- Developer Origin:Chinese startup (High-Flyer AI) vs Google (US multinational)
- Primary Strength:Mathematical reasoning & code generation vs Multimodal understanding & creative tasks
- Training Cost (reported):✓ DeepSeek wins($5.5 million USD vs $50+ million USD (estimated))
Key Facts & Figures
86 numeric metrics compared
| Metric | DeepSeek | Gemini | Ratio |
|---|---|---|---|
| API Cost (Input Tokens)($ per million tokens) | $0.014 (DeepSeek-Chat) | — | — |
| Context Window(tokens) | 164K tokens | 1M tokens (3.1 Pro) | — |
| Minimum Monthly Cost (Consumer)($) | Free tier available | — | — |
| Context Window Size (V3/O1)(tokens) | 4,096 tokens (DeepSeek-V3) | — | — |
| Minimum Subscription Cost(USD/month) | Free (with API credits) | — | — |
| Reasoning Task Performance (GPQA Benchmark)(percentage) | 92% (R1) | — | — |
| AIME 2024 Benchmark (Math Reasoning)(percent) | 96.3% | — | — |
| API Input Token Cost(USD per 1M tokens) | $0.14 | — | — |
| Largest Model Parameter Count(parameters) | 685B (DeepSeek-V3) | — | — |
| MMLU General Knowledge Benchmark(percent) | 92.3% | — | — |
| Minimum GPU VRAM for Full Model Inference(GB) | 40GB (with MoE efficiency) | — | — |
| LiveCodeBench Score(percent) | 88.7% | — | — |
| Math Reasoning Accuracy (AIME 2024)(percent correct) | 79.8% | — | — |
| Code Generation Performance (HumanEval)(%) | 92.3% (DeepSeek-V3) | — | — |
| API Cost per Million Input Tokens(USD) | $0.14 | — | — |
| General Knowledge (MMLU Benchmark)(percent accuracy) | 86.5% (DeepSeek-V3) | — | — |
| Model Size Options Available(count) | 2 primary versions (limited small sizes) | — | — |
| Inference Cost per 1M Tokens(USD) | $0.21 (average) | — | — |
| Math Reasoning Accuracy (AIME Benchmark)(%) | 94% | — | — |
| Documentation Completeness Score(/10) | 4/10 | — | — |
| Community Size & Ecosystem(relative rank) | Emerging (rank #8 in AI models) | — | — |
| AIME Math Benchmark Score(%) | 79.8% | 80%+ | |
| Estimated Training Cost(USD millions) | $5.5M | $50M+ | |
| API Pricing (per 1M tokens, input)(USD) | Not publicly available | $0.0075-$0.075 | — |
| Code Generation - Codeforces Problems Solved(problems) | 70+ advanced problems | ~60+ (estimated) | |
| API Cost (per 1M input tokens)(USD) | $0.14 | — | — |
| AIME 2024 Math Reasoning Accuracy(%) | 94% | — | — |
| Average Response Latency(milliseconds) | 250ms | 320 | |
| Context Window Size(tokens) | 128K | 1,000,000 | |
| API Cost per 1M Input Tokens(USD) | $0.14 | — | — |
| AIME 2024 Reasoning Benchmark(percent correct) | 96% | — | — |
| Monthly Subscription Cost (Individual)(USD) | $0.00 (Free tier available) | — | — |
| Code Generation Benchmark (LMSYS)(%) | 82% | — | — |
| Windows OS Market Share(%) | 0% (external integration required) | — | — |
| API Input Cost per 1M Tokens(USD) | $0.14 | — | — |
| API Output Cost per 1M Tokens(USD) | $0.28 | — | — |
| HumanEval Coding Pass Rate(percent) | 96.3% | — | — |
| Average Citations per Response(count) | 2-5 | — | — |
| AIME 2024 Reasoning Accuracy(percent) | 71% | — | — |
| HumanEval Code Pass Rate(%) | 96.3% | — | — |
| Largest Model Size(B parameters) | 671B | — | — |
| API Pricing (Input Tokens)(USD per 1M tokens) | $0.07 | — | — |
| MMLU Benchmark (General Knowledge)(%) | 92.3% | — | — |
| AIME 2024 Benchmark Score(%) | 96.3% | — | — |
| Inference Speed(tokens/second) | 45 tokens/sec | — | — |
| Supported Languages(languages) | 25 languages | — | — |
| Model Quantization Formats(count) | 4 formats | — | — |
| Time to Market (Latest Model Release)(months) | 8 months | — | — |
| Open Source Models Available(model families) | 3 families | — | — |
| Monthly Active Users(millions) | Gemini | Gemini | |
| Developer Preference Rating(%) | Gemini | Gemini | |
| Company Valuation(billions USD) | Gemini (Google) | Gemini (Google) | |
| Monthly Active Users(millions) | Gemini | Gemini | |
| Daily Active Users(millions) | Gemini | Gemini | |
| Year-over-Year User Growth(percent) | Gemini | Gemini | |
| Monthly Active Users(millions) | 1.5 billion+ (all Google products) | 1.5 billion+ (all Google products) | |
| Starting Price (Monthly)(USD) | Free (Gemini Free or Premium $20) | Free (Gemini Free or Premium $20) | |
| Context Window Size(tokens) | 1 million tokens (Gemini 2.0) | 1 million tokens (Gemini 2.0) | |
| Supported Programming Languages(languages) | 40+ languages with debugging | 40+ languages with debugging | |
| Uptime SLA (Enterprise)(percent) | 99.5% (Google Cloud SLA) | 99.5% (Google Cloud SLA) | |
| Context Window (Tokens)(tokens) | 1,000,000 | 1,000,000 | |
| Input Cost per Million Tokens(USD) | $7.50 | $7.50 | |
| Output Cost per Million Tokens(USD) | $30.00 | $30.00 | |
| Multimodal Format Support(formats) | 5 (text, image, audio, video, docs) | 5 (text, image, audio, video, docs) | |
| Third-Party Integrations Available(count) | 1,500+ | 1,500+ | |
| Code Generation Benchmark Score(%) | 89.7 | 89.7 | |
| Reasoning Capability Rating(score (1-10)) | 8.0 | 8.0 | |
| Complex Reasoning Accuracy (AIME Benchmark)(percentage) | ~62% | ~62% | |
| Monthly Subscription Cost (Premium)(USD) | $20/month (Google One Premium) | $20/month (Google One Premium) | |
| Code Generation Quality (HumanEval Benchmark)(percentage) | ~92% | ~92% | |
| Maker Trading Fee(%) | 0.50% | 0.50% | |
| Taker Trading Fee(%) | 0.60% | 0.60% | |
| Supported Cryptocurrencies(assets) | 95+ | 95+ | |
| Staking Assets Available(count) | 8 | 8 | |
| Maximum Staking APY(%) | Up to 5% | Up to 5% | |
| Minimum Subscription Cost (Annual)(USD) | $0 (Free tier available) | $0 (Free tier available) | |
| Supported Input Formats(count) | Text, images, audio, video | Text, images, audio, video | |
| Maker Trading Fee(%) | 0.25% | 0.25% | |
| Taker Trading Fee(%) | 0.35% | 0.35% | |
| Available Trading Pairs(pairs) | 120+ | 120+ | |
| US State Money Transmitter Licenses(states) | 50 states (all) | 50 states (all) | |
| Mobile App Rating (iOS)(stars out of 5) | 4.6/5 | 4.6/5 | |
| Year Founded | 2014 | 2014 | |
| Coding Performance (HumanEval Benchmark)(%) | 92.3% | 92.3% | |
| Mathematical Reasoning (MATH-500)(%) | 90% | 90% | |
| Premium Subscription Cost (Monthly)(USD) | $20 (Gemini Advanced) | $20 (Gemini Advanced) |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
D
1DeepSeekGemini leads3 ties
G
3GeminiDeepSeek
Attribute
Gemini
- Chinese startup (High-Flyer AI)Developer OriginGoogle (US multinational)
- Mathematical reasoning & code generationPrimary StrengthMultimodal understanding & creative tasks
- $5.5 million USD(winner)Training Cost (reported)$50+ million USD (estimated)
- NoReal-time Web AccessYes (Gemini Advanced)(winner)
- Limited/NoneImage GenerationNative integration with Imagen 3(winner)
- Limited access, API in betaAPI AvailabilityFull commercial API with Vertex AI(winner)
- 79.8%AIME Math Benchmark Score80%+
- Developer Origin
DeepSeek
Chinese startup (High-Flyer AI)
Gemini
Google (US multinational)
- Primary Strength
DeepSeek
Mathematical reasoning & code generation
Gemini
Multimodal understanding & creative tasks
- Training Cost (reported)
DeepSeek
$5.5 million USD(winner)
Gemini
$50+ million USD (estimated)
- Real-time Web Access
DeepSeek
No
Gemini
Yes (Gemini Advanced)(winner)
- Image Generation
DeepSeek
Limited/None
Gemini
Native integration with Imagen 3(winner)
- API Availability
DeepSeek
Limited access, API in beta
Gemini
Full commercial API with Vertex AI(winner)
- AIME Math Benchmark Score
DeepSeek
79.8%
Gemini
80%+
Full Comparison
| Attribute | DeepSeek | Gemini |
|---|---|---|
| API Cost (Input Tokens)($ per million tokens) | $0.014 (DeepSeek-Chat) | — |
| Minimum Monthly Cost (Consumer)($) | Free tier available | — |
| Minimum Subscription Cost(USD/month) | Free (with API credits) | — |
| API Cost per Million Input Tokens(USD) | $0.14 | — |
| API Cost (per 1M input tokens)(USD) | $0.14 | — |
Show 19 more attributesAPI Cost per 1M Input Tokens(USD) $0.14 — Monthly Subscription Cost (Individual)(USD) $0.00 (Free tier available) — API Input Cost per 1M Tokens(USD) $0.14 — API Output Cost per 1M Tokens(USD) $0.28 — Free Tier Availability Limited API access required Yes (with limitations) Cost to Users Free (premium paid options) — Starting Price (Monthly)(USD) Free (Gemini Free or Premium $20) — Input Cost per Million Tokens(USD) $7.50 — Output Cost per Million Tokens(USD) $30.00 — Free tier 3.5 Flash (generous limits) — Paid entry plan Google AI Pro $19.99/mo — Power-user tier Ultra $100–$200/mo — Team plan Workspace Business (varies) — API input (per 1M tokens) 3.5 Flash: ~$0.10 — Monthly Subscription Cost (Premium)(USD) $20/month (Google One Premium) — Minimum Subscription Cost (Annual)(USD) $0 (Free tier available) — Maker Trading Fee(%) 0.25% — Taker Trading Fee(%) 0.35% — Premium Subscription Cost (Monthly)(USD) $20 (Gemini Advanced) — | ||
| Context Window(tokens) | 164K tokens | 1M tokens (3.1 Pro) |
| Reasoning Benchmark Score(percentile) | Top-tier (R1/V3.2 optimized) | — |
| Reasoning Task Performance (GPQA Benchmark)(percentage) | 92% (R1) | — |
| AIME 2024 Benchmark (Math Reasoning)(percent) | 96.3% | — |
| MMLU General Knowledge Benchmark(percent) | 92.3% | — |
| LiveCodeBench Score(percent) | 88.7% | — |
Show 18 more attributesMath Reasoning Accuracy (AIME 2024)(percent correct) 79.8% — Code Generation Performance (HumanEval)(%) 92.3% (DeepSeek-V3) — General Knowledge (MMLU Benchmark)(percent accuracy) 86.5% (DeepSeek-V3) — Math Reasoning Accuracy (AIME Benchmark)(%) 94% — AIME Math Benchmark Score(%) 79.8% 80%+ Code Generation - Codeforces Problems Solved(problems) 70+ advanced problems ~60+ (estimated) AIME 2024 Reasoning Benchmark(percent correct) 96% — Code Generation Benchmark (LMSYS)(%) 82% — HumanEval Coding Pass Rate(percent) 96.3% — AIME 2024 Reasoning Accuracy(percent) 71% — AIME 2024 Benchmark Score(%) 96.3% — Inference Speed(tokens/second) 45 tokens/sec — Context Window Size(tokens) 1 million tokens (Gemini 2.0) — Code Generation Benchmark Score(%) 89.7 — Complex Reasoning Accuracy (AIME Benchmark)(percentage) ~62% — Code Generation Quality (HumanEval Benchmark)(percentage) ~92% — Coding Performance (HumanEval Benchmark)(%) 92.3% — Mathematical Reasoning (MATH-500)(%) 90% — | ||
| Multimodal Support | Text, emerging vision | — |
| Context Window (Tokens)(tokens) | 1,000,000 | — |
| Multimodal Format Support(formats) | 5 (text, image, audio, video, docs) | — |
| Reasoning Capability Rating(score (1-10)) | 8.0 | — |
| Real-Time Information | April 2024 knowledge cutoff | — |
Show 1 more attributeSupported Input Formats(count) Text, images, audio, video — | ||
| On-Premise Deployment | Yes, fully supported | — |
| Minimum GPU VRAM for Full Model Inference(GB) | 40GB (with MoE efficiency) | — |
| Local Deployment Support | Not supported (API only) | — |
| Third-Party Integrations(count) | Growing (API-focused) | — |
| User Interface Rating(stars out of 5) | Technical, developer-centric | — |
| Microsoft 365 Integration | Limited (API-only) | — |
| Microsoft 365 Native Integration | None (API only) | — |
| Ecosystem Integration | Google Search, Workspace, Maps | — |
| Google Workspace Integration | 10 | — |
| Developer API Availability | Full access via Vertex AI and AI Studio | — |
Show 1 more attributeWorkplace Integration Google Workspace, Drive, Gmail, Search — | ||
| Model Availability | Open-source weights available | — |
| Open Source Model Weights | Yes, publicly available(winner) | No, closed source |
| Open Source Models Available(model families) | 3 families | — |
| Mobile App Availability | iOS, Android (native Gemini app) | — |
| Enterprise Data Compliance | Subject to Chinese data laws | — |
| Data Privacy (External Processing) | Higher risk - processed by DeepSeek servers | — |
| USD Balance Insurance (FDIC) | Up to $250,000 | — |
| Context Window Size (V3/O1)(tokens) | 4,096 tokens (DeepSeek-V3) | — |
| API Input Token Cost(USD per 1M tokens) | $0.14 | — |
| Estimated Training Cost(USD millions) | $5.5M(winner) | $50M+ |
| API Pricing (per 1M tokens, input)(USD) | Not publicly available | $0.0075-$0.075 |
| Largest Model Parameter Count(parameters) | 685B (DeepSeek-V3) | — |
| Open-Source Weight Availability | Partial (R1 inference-only) | — |
| Commercial Use Clarity(null) | Restricted in some jurisdictions; unclear terms | — |
| Open Source License(license type) | Closed, proprietary | — |
| Company Location | China | — |
| Regulatory License | Consumer AI Product | — |
| Source Code Availability | Closed-source, API-only | — |
| Documentation Completeness Score(/10) | 4/10 | — |
| Model Size Options Available(count) | 2 primary versions (limited small sizes) | — |
| Largest Model Size(B parameters) | 671B | — |
| Inference Cost per 1M Tokens(USD) | $0.21 (average) | — |
| API Pricing (Input Tokens)(USD per 1M tokens) | $0.07 | — |
| Commercial License Type | Proprietary with restrictions | — |
| Model License Type | MIT Open-source | — |
| Community Size & Ecosystem(relative rank) | Emerging (rank #8 in AI models) | — |
| Monthly Active Users(millions) | ~8 million | 100(winner) |
| Monthly Active Users(millions) | Gemini | — |
| Real-time Web Access | No | Yes (Gemini Advanced)(winner) |
| Native Image Generation | None | Imagen 3 integrated(winner) |
| Enterprise API Availability | Limited beta access | Full commercial with SLA(winner) |
| Vision Capability(supported formats) | Limited (text-focused) | — |
| Real-Time Web Search | No (cutoff April 2024) | Free |
Show 9 more attributesAverage Citations per Response(count) 2-5 — Supported Languages(languages) 25 languages — Multimodal Capabilities Advanced (image, video, text) — Supported Assets Text, Images, Video, Code — Image Generation Yes — Imagen 4 — Mobile App Polished; merges with Google app on Android — Multimodal Input Support Text, images, video, audio — Supported Cryptocurrencies(assets) 95+ — Real-Time Information Access No (April 2024 cutoff) — | ||
| AIME 2024 Math Reasoning Accuracy(%) | 94% | — |
| HumanEval Code Pass Rate(%) | 96.3% | — |
| MMLU Benchmark (General Knowledge)(%) | 92.3% | — |
| Average Response Latency(milliseconds) | 250ms(winner) | 320 |
| Context Window Size(tokens) | 128K | 1,000,000(winner) |
| Training Data Recency(months_old) | 8 months old (April 2024)(winner) | 20 months (April 2024) |
| Windows OS Market Share(%) | 0% (external integration required) | — |
| Self-hosting/Local Deployment | Fully Supported | — |
| Model Quantization Formats(count) | 4 formats | — |
| US Market Accessibility | Restricted/Limited | — |
| Commercial Deployment Restrictions | U.S. export restrictions (China-based) | — |
| Technical Transparency | Limited disclosure, proprietary | — |
| Time to Market (Latest Model Release)(months) | 8 months | — |
| Free Tier Quality | Gemini 1.5 Pro (strong) | — |
| Developer Preference Rating(%) | Gemini | — |
| Coding Task Excellence(benchmark) | Good | — |
| Reasoning Quality(benchmark) | Strong | — |
| Company Valuation(billions USD) | Gemini (Google) | — |
| Content Writing Refinement | Good but formulaic | — |
| Monthly Active Users(millions) | Gemini | — |
| Daily Active Users(millions) | Gemini | — |
| Quarterly Trading Volume(USD Billions) | Gemini | — |
| Year-over-Year User Growth(percent) | Gemini | — |
| Primary Function | Conversational AI & LLM | — |
| Monthly Active Users(millions) | 1.5 billion+ (all Google products) | — |
| Supported Programming Languages(languages) | 40+ languages with debugging | — |
| Enterprise Deployment Options | Cloud-based primarily | — |
| Document Processing Types | PDF, Images, Web content, Video | — |
| Uptime SLA (Enterprise)(percent) | 99.5% (Google Cloud SLA) | — |
| Third-Party Integrations Available(count) | 1,500+ | — |
| Headline model | Gemini 3.1 Pro, 3.5 Flash | — |
| Coding (SWE-bench Verified) | Competitive (3.1 Pro) | — |
| Web grounding / citations | Native Search grounding | — |
| Multilingual quality | Broadest (Google translation lineage) | — |
| Structured output / JSON mode | Controlled generation + JSON schemas | — |
Show 1 more attributeTraining data cut-off ~2025 (Search compensates) — | ||
| Image input | Yes | — |
| Voice mode | Yes (rolling out) | — |
| Agentic capability | Agentic in Workspace | — |
| Plugin Ecosystem(available plugins) | Google integrations | — |
| Prompt caching (API) | Implicit + explicit context caching | — |
| Fine-tuning (API) | Gemini 3.5 Flash | — |
| On-prem / self-host | Cloud-only (Vertex AI) | — |
| API rate limits (Tier 1) | ~360 RPM / 4M TPM (3.1 Pro) | — |
| Content Moderation Strictness(level) | Strict - refuses harmful/controversial | — |
| Content Moderation Level | Standard | — |
| Maker Trading Fee(%) | 0.50% | — |
| Taker Trading Fee(%) | 0.60% | — |
| NY BitLicense Status | Approved | — |
| Staking Assets Available(count) | 8 | — |
| Maximum Staking APY(%) | Up to 5% | — |
| Margin Trading Available | No | — |
| Code Generation Performance(benchmark) | Optimized with native debugging tools | — |
| Available Trading Pairs(pairs) | 120+ | — |
| US State Money Transmitter Licenses(states) | 50 states (all) | — |
| NY BitLicense Status | Fully licensed | — |
| Mobile App Rating (iOS)(stars out of 5) | 4.6/5 | — |
| Maximum Margin Leverage(x) | Limited/unavailable | — |
| Year Founded | 2014 | — |
| Video Processing Capability | Yes | — |
DDeepSeek
GGemini
API Cost (Input Tokens)($ per million tokens)
$0.014 (DeepSeek-Chat)
—
Minimum Monthly Cost (Consumer)($)
Free tier available
—
Minimum Subscription Cost(USD/month)
Free (with API credits)
—
API Cost per Million Input Tokens(USD)
$0.14
—
API Cost (per 1M input tokens)(USD)
$0.14
—
Show 19 more attributes
API Cost per 1M Input Tokens(USD)
$0.14
—
Monthly Subscription Cost (Individual)(USD)
$0.00 (Free tier available)
—
API Input Cost per 1M Tokens(USD)
$0.14
—
API Output Cost per 1M Tokens(USD)
$0.28
—
Free Tier Availability
Limited API access required
Yes (with limitations)
Cost to Users
Free (premium paid options)
—
Starting Price (Monthly)(USD)
Free (Gemini Free or Premium $20)
—
Input Cost per Million Tokens(USD)
$7.50
—
Output Cost per Million Tokens(USD)
$30.00
—
Free tier
3.5 Flash (generous limits)
—
Paid entry plan
Google AI Pro $19.99/mo
—
Power-user tier
Ultra $100–$200/mo
—
Team plan
Workspace Business (varies)
—
API input (per 1M tokens)
3.5 Flash: ~$0.10
—
Monthly Subscription Cost (Premium)(USD)
$20/month (Google One Premium)
—
Minimum Subscription Cost (Annual)(USD)
$0 (Free tier available)
—
Maker Trading Fee(%)
0.25%
—
Taker Trading Fee(%)
0.35%
—
Premium Subscription Cost (Monthly)(USD)
$20 (Gemini Advanced)
—
Context Window(tokens)
164K tokens
1M tokens (3.1 Pro)
Reasoning Benchmark Score(percentile)
Top-tier (R1/V3.2 optimized)
—
Reasoning Task Performance (GPQA Benchmark)(percentage)
92% (R1)
—
AIME 2024 Benchmark (Math Reasoning)(percent)
96.3%
—
MMLU General Knowledge Benchmark(percent)
92.3%
—
LiveCodeBench Score(percent)
88.7%
—
Show 18 more attributes
Math Reasoning Accuracy (AIME 2024)(percent correct)
79.8%
—
Code Generation Performance (HumanEval)(%)
92.3% (DeepSeek-V3)
—
General Knowledge (MMLU Benchmark)(percent accuracy)
86.5% (DeepSeek-V3)
—
Math Reasoning Accuracy (AIME Benchmark)(%)
94%
—
AIME Math Benchmark Score(%)
79.8%
80%+
Code Generation - Codeforces Problems Solved(problems)
70+ advanced problems
~60+ (estimated)
AIME 2024 Reasoning Benchmark(percent correct)
96%
—
Code Generation Benchmark (LMSYS)(%)
82%
—
HumanEval Coding Pass Rate(percent)
96.3%
—
AIME 2024 Reasoning Accuracy(percent)
71%
—
AIME 2024 Benchmark Score(%)
96.3%
—
Inference Speed(tokens/second)
45 tokens/sec
—
Context Window Size(tokens)
1 million tokens (Gemini 2.0)
—
Code Generation Benchmark Score(%)
89.7
—
Complex Reasoning Accuracy (AIME Benchmark)(percentage)
~62%
—
Code Generation Quality (HumanEval Benchmark)(percentage)
~92%
—
Coding Performance (HumanEval Benchmark)(%)
92.3%
—
Mathematical Reasoning (MATH-500)(%)
90%
—
Multimodal Support
Text, emerging vision
—
Context Window (Tokens)(tokens)
1,000,000
—
Multimodal Format Support(formats)
5 (text, image, audio, video, docs)
—
Reasoning Capability Rating(score (1-10))
8.0
—
Real-Time Information
April 2024 knowledge cutoff
—
Show 1 more attribute
Supported Input Formats(count)
Text, images, audio, video
—
On-Premise Deployment
Yes, fully supported
—
Minimum GPU VRAM for Full Model Inference(GB)
40GB (with MoE efficiency)
—
Local Deployment Support
Not supported (API only)
—
Third-Party Integrations(count)
Growing (API-focused)
—
User Interface Rating(stars out of 5)
Technical, developer-centric
—
Microsoft 365 Integration
Limited (API-only)
—
Microsoft 365 Native Integration
None (API only)
—
Ecosystem Integration
Google Search, Workspace, Maps
—
Google Workspace Integration
10
—
Developer API Availability
Full access via Vertex AI and AI Studio
—
Show 1 more attribute
Workplace Integration
Google Workspace, Drive, Gmail, Search
—
Model Availability
Open-source weights available
—
Open Source Model Weights
Yes, publicly available
No, closed source
Open Source Models Available(model families)
3 families
—
Mobile App Availability
iOS, Android (native Gemini app)
—
Enterprise Data Compliance
Subject to Chinese data laws
—
Data Privacy (External Processing)
Higher risk - processed by DeepSeek servers
—
USD Balance Insurance (FDIC)
Up to $250,000
—
Context Window Size (V3/O1)(tokens)
4,096 tokens (DeepSeek-V3)
—
API Input Token Cost(USD per 1M tokens)
$0.14
—
Estimated Training Cost(USD millions)
$5.5M
$50M+
API Pricing (per 1M tokens, input)(USD)
Not publicly available
$0.0075-$0.075
Largest Model Parameter Count(parameters)
685B (DeepSeek-V3)
—
Open-Source Weight Availability
Partial (R1 inference-only)
—
Commercial Use Clarity(null)
Restricted in some jurisdictions; unclear terms
—
Open Source License(license type)
Closed, proprietary
—
Company Location
China
—
Regulatory License
Consumer AI Product
—
Source Code Availability
Closed-source, API-only
—
Documentation Completeness Score(/10)
4/10
—
Model Size Options Available(count)
2 primary versions (limited small sizes)
—
Largest Model Size(B parameters)
671B
—
Inference Cost per 1M Tokens(USD)
$0.21 (average)
—
API Pricing (Input Tokens)(USD per 1M tokens)
$0.07
—
Commercial License Type
Proprietary with restrictions
—
Model License Type
MIT Open-source
—
Community Size & Ecosystem(relative rank)
Emerging (rank #8 in AI models)
—
Monthly Active Users(millions)
~8 million
100
Monthly Active Users(millions)
Gemini
—
Real-time Web Access
No
Yes (Gemini Advanced)
Native Image Generation
None
Imagen 3 integrated
Enterprise API Availability
Limited beta access
Full commercial with SLA
Vision Capability(supported formats)
Limited (text-focused)
—
Real-Time Web Search
No (cutoff April 2024)
Free
Show 9 more attributes
Average Citations per Response(count)
2-5
—
Supported Languages(languages)
25 languages
—
Multimodal Capabilities
Advanced (image, video, text)
—
Supported Assets
Text, Images, Video, Code
—
Image Generation
Yes — Imagen 4
—
Mobile App
Polished; merges with Google app on Android
—
Multimodal Input Support
Text, images, video, audio
—
Supported Cryptocurrencies(assets)
95+
—
Real-Time Information Access
No (April 2024 cutoff)
—
AIME 2024 Math Reasoning Accuracy(%)
94%
—
HumanEval Code Pass Rate(%)
96.3%
—
MMLU Benchmark (General Knowledge)(%)
92.3%
—
Average Response Latency(milliseconds)
250ms
320
Context Window Size(tokens)
128K
1,000,000
Training Data Recency(months_old)
8 months old (April 2024)
20 months (April 2024)
Windows OS Market Share(%)
0% (external integration required)
—
Self-hosting/Local Deployment
Fully Supported
—
Model Quantization Formats(count)
4 formats
—
US Market Accessibility
Restricted/Limited
—
Commercial Deployment Restrictions
U.S. export restrictions (China-based)
—
Technical Transparency
Limited disclosure, proprietary
—
Time to Market (Latest Model Release)(months)
8 months
—
Free Tier Quality
Gemini 1.5 Pro (strong)
—
Developer Preference Rating(%)
Gemini
—
Coding Task Excellence(benchmark)
Good
—
Reasoning Quality(benchmark)
Strong
—
Company Valuation(billions USD)
Gemini (Google)
—
Content Writing Refinement
Good but formulaic
—
Monthly Active Users(millions)
Gemini
—
Daily Active Users(millions)
Gemini
—
Quarterly Trading Volume(USD Billions)
Gemini
—
Year-over-Year User Growth(percent)
Gemini
—
Primary Function
Conversational AI & LLM
—
Monthly Active Users(millions)
1.5 billion+ (all Google products)
—
Supported Programming Languages(languages)
40+ languages with debugging
—
Enterprise Deployment Options
Cloud-based primarily
—
Document Processing Types
PDF, Images, Web content, Video
—
Uptime SLA (Enterprise)(percent)
99.5% (Google Cloud SLA)
—
Third-Party Integrations Available(count)
1,500+
—
Headline model
Gemini 3.1 Pro, 3.5 Flash
—
Coding (SWE-bench Verified)
Competitive (3.1 Pro)
—
Web grounding / citations
Native Search grounding
—
Multilingual quality
Broadest (Google translation lineage)
—
Structured output / JSON mode
Controlled generation + JSON schemas
—
Show 1 more attribute
Training data cut-off
~2025 (Search compensates)
—
Image input
Yes
—
Voice mode
Yes (rolling out)
—
Agentic capability
Agentic in Workspace
—
Plugin Ecosystem(available plugins)
Google integrations
—
Prompt caching (API)
Implicit + explicit context caching
—
Fine-tuning (API)
Gemini 3.5 Flash
—
On-prem / self-host
Cloud-only (Vertex AI)
—
API rate limits (Tier 1)
~360 RPM / 4M TPM (3.1 Pro)
—
Content Moderation Strictness(level)
Strict - refuses harmful/controversial
—
Content Moderation Level
Standard
—
Maker Trading Fee(%)
0.50%
—
Taker Trading Fee(%)
0.60%
—
NY BitLicense Status
Approved
—
Staking Assets Available(count)
8
—
Maximum Staking APY(%)
Up to 5%
—
Margin Trading Available
No
—
Code Generation Performance(benchmark)
Optimized with native debugging tools
—
Available Trading Pairs(pairs)
120+
—
US State Money Transmitter Licenses(states)
50 states (all)
—
NY BitLicense Status
Fully licensed
—
Mobile App Rating (iOS)(stars out of 5)
4.6/5
—
Maximum Margin Leverage(x)
Limited/unavailable
—
Year Founded
2014
—
Video Processing Capability
Yes
—
Pros & Cons
10 pros·6 cons across both
D
G
D
DeepSeek
+5-3
Pros
- 79.8% accuracy on AIME math problems, competitive with frontier models
- Reported $5.5M training cost—10x cheaper than comparable models
- Superior code generation in C++, Python, and algorithms (70+ Codeforces problems solved)
- Open-source model weights available for self-hosting and fine-tuning
- Low latency inference due to optimized architecture
Cons
- No real-time web access or current information beyond training data
- Extremely limited image understanding and zero native image generation
- Smaller developer ecosystem and less third-party integration compared to Gemini
G
Gemini
+5-3
Pros
- 80%+ accuracy on AIME and strong performance across academic benchmarks
- Native real-time web search integration for current information access
- Advanced image understanding (Gemini 2.0 Vision) and Imagen 3 image generation
- Full commercial API via Google Cloud Vertex AI with enterprise SLA support
- Deep integration with Google Workspace, Gmail, Docs, Slides, and ecosystem
Cons
- Estimated $50M+ training costs passed to users via API pricing ($0.0075-$0.30 per 1M tokens)
- Data privacy concerns due to Google's data collection practices and terms of service
- Multimodal reasoning sometimes less reliable than specialized reasoning models
Frequently Asked Questions
5 questions
DeepSeek slightly edges Gemini for pure coding and mathematics. DeepSeek achieved 79.8% on AIME math benchmarks and solved 70+ advanced Codeforces problems, compared to Gemini's comparable but slightly lower code-specific performance. However, Gemini excels when the task requires web research to verify solutions or involves creative coding patterns.
Resources & Learn More
Curated sources to dive deeper
Where to Buy
As an affiliate, we may earn a commission from qualifying purchases at no extra cost to you. Learn more about our affiliate disclosure
Wikipedia
- W
DeepSeek on Wikipedia (opens in new tab)
Chinese AI model focused on reasoning, mathematics, and code efficiency with low computational overhead.
- W
Gemini on Wikipedia (opens in new tab)
Google's multimodal AI assistant with real-time web access, image generation, and tight integration with Google services.
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