DeepSeek vs Mistral 2026: Pricing, Performance, Benchmarks
DeepSeek offers superior reasoning capabilities and lower operational costs through its MoE architecture, while Mistral provides better multilingual support and stronger European regulatory compliance. DeepSeek-R1 achieves 96.3% on AIME 2024 vs Mistral Large 2's 85.9%, but Mistral maintains higher transparency and full open-source availability.
DeepSeek
Chinese AI model focused on reasoning, mathematics, and code efficiency with low computational overhead.
Cost-conscious developers, research teams needing superior math/reasoning capabilities, and organizations building production systems with tight compute budgets.
Mistral AI
French AI company providing scalable open-source LLMs with enterprise-grade optimization and modular architecture.
European enterprises requiring GDPR compliance, research institutions valuing open-source models, and teams prioritizing transparency over raw performance metrics.
Quick Answer
AI SummaryDeepSeek offers superior reasoning capabilities and lower operational costs through its MoE architecture, while Mistral provides better multilingual support and stronger European regulatory compliance. DeepSeek-R1 achieves 96.3% on AIME 2024 vs Mistral Large 2's 85.9%, but Mistral maintains higher transparency and full open-source availability.
Our Verdict
AI-assistedChoose DeepSeek if you need cutting-edge reasoning performance, cost-effective API operations, or plan to deploy locally with maximum parameter efficiency—it excels at complex mathematics and delivers 14x cheaper inference. Choose Mistral if transparency, full open-source licensing, and European data residency compliance are priorities, or if you prefer smaller, easier-to-manage models with strong multilingual capabilities.
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Choose DeepSeek if
Best pickCost-conscious developers, research teams needing superior math/reasoning capabilities, and organizations building production systems with tight compute budgets.
Choose Mistral AI if
European enterprises requiring GDPR compliance, research institutions valuing open-source models, and teams prioritizing transparency over raw performance metrics.
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Key Differences at a Glance
- AIME 2024 Math Benchmark Score:✓ DeepSeek wins(96.3% vs 85.9%)
- API Cost (per 1M input tokens):✓ DeepSeek wins($0.14 vs $2.00)
- Primary Model Architecture:Mixture of Experts (MoE) vs Dense Transformer
Key Facts & Figures
51 numeric metrics compared
| Metric | DeepSeek | Mistral AI | Ratio |
|---|---|---|---|
| API Cost (Input Tokens)($ per million tokens) | $0.014 (DeepSeek-Chat) | — | — |
| Context Window(tokens) | 164K tokens | — | — |
| Minimum Monthly Cost (Consumer)($) | 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% | 85.9% | |
| API Input Token Cost(USD per 1M tokens) | $0.14 | $2.00 | |
| Largest Model Parameter Count(parameters) | 685B (DeepSeek-V3) | 123 billion | |
| MMLU General Knowledge Benchmark(percent) | 92.3% | 92.2% | |
| Minimum GPU VRAM for Full Model Inference(GB) | 40GB (with MoE efficiency) | 246GB | |
| LiveCodeBench Score(percent) | 88.7% | 84.2% | |
| 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% | — | — |
| Estimated Training Cost(USD millions) | $5.5M | $14.2M | |
| Code Generation - Codeforces Problems Solved(problems) | 70+ advanced problems | — | — |
| API Cost (per 1M input tokens)(USD) | $0.14 | — | — |
| AIME 2024 Math Reasoning Accuracy(%) | 94% | — | — |
| Average Response Latency(ms) | 250ms | — | — |
| Context Window Size(tokens) | 128K | 200K | |
| API Cost per 1M Input Tokens(USD) | $0.14 | $2.70 | |
| 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% | 91.2% | |
| Inference Speed(tokens/second) | 45 tokens/sec | 62 tokens/sec | |
| Supported Languages(languages) | 25 languages | 40+ languages | |
| Model Quantization Formats(count) | 4 formats | 6 formats | |
| Time to Market (Latest Model Release)(months) | 8 months | 6 months | |
| Open Source Models Available(model families) | 3 families | 4 families | |
| MMLU Reasoning Benchmark Score(percentage) | 92.0% | 92.0% | |
| Maximum Context Window(tokens) | 128,000 (Mistral Large) | 128,000 (Mistral Large) | |
| Inference Speed (Small Model)(tokens per second) | ~400 tokens/sec (Mistral 7B) | ~400 tokens/sec (Mistral 7B) |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
- 96.3%(winner)AIME 2024 Math Benchmark Score85.9%
- $0.14(winner)API Cost (per 1M input tokens)$2.00
- Mixture of Experts (MoE)Primary Model ArchitectureDense Transformer
- Partial (R1 weights restricted)Open-Source Model AvailabilityFull (all weights public)(winner)
- 671B parameters(winner)Largest Public Model Size123B parameters
- 92.3%MMLU Benchmark Score92.2%
- ChinaCompany HeadquartersFrance
- AIME 2024 Math Benchmark Score
DeepSeek
96.3%(winner)
Mistral AI
85.9%
- API Cost (per 1M input tokens)
DeepSeek
$0.14(winner)
Mistral AI
$2.00
- Primary Model Architecture
DeepSeek
Mixture of Experts (MoE)
Mistral AI
Dense Transformer
- Open-Source Model Availability
DeepSeek
Partial (R1 weights restricted)
Mistral AI
Full (all weights public)(winner)
- Largest Public Model Size
DeepSeek
671B parameters(winner)
Mistral AI
123B parameters
- MMLU Benchmark Score
DeepSeek
92.3%
Mistral AI
92.2%
- Company Headquarters
DeepSeek
China
Mistral AI
France
Full Comparison
| Attribute | DeepSeek | Mistral AI |
|---|---|---|
| API Cost (Input Tokens)($ 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 5 more attributesAPI Cost per 1M Input Tokens(USD) $0.14 $2.70 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 — | ||
| Context Window(tokens) | 164K tokens | — |
| Context Window Size (V3/O1)(tokens) | 4,096 tokens (DeepSeek-V3) | — |
| Maximum Context Window(tokens) | 128,000 (Mistral Large) | — |
| 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%(winner) | 85.9% |
| MMLU General Knowledge Benchmark(percent) | 92.3%(winner) | 92.2% |
| LiveCodeBench Score(percent) | 88.7%(winner) | 84.2% |
Show 16 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% — Code Generation - Codeforces Problems Solved(problems) 70+ advanced problems — Average Response Latency(ms) 250ms — Context Window Size(tokens) 128K 200K 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% 91.2% Inference Speed(tokens/second) 45 tokens/sec 62 tokens/sec MMLU Reasoning Benchmark Score(percentage) 92.0% — Inference Speed (Small Model)(tokens per second) ~400 tokens/sec (Mistral 7B) — | ||
| Multimodal Support | Text, emerging vision | — |
| Native Multimodal Support | Text primary; limited image support | — |
| On-Premise Deployment | Yes, fully supported | — |
| Minimum GPU VRAM for Full Model Inference(GB) | 40GB (with MoE efficiency)(winner) | 246GB |
| Local Deployment Support | Not supported (API only) | — |
| Open-Source Model Availability | Mistral 7B and Mixtral 8x7B freely available | — |
| Third-Party Integrations(count) | Growing (API-focused) | — |
| User Interface Rating(stars out of 5) | Technical, developer-centric | — |
| Microsoft 365 Integration | Limited (API-only) | — |
| Model Availability | Open-source weights available | — |
| Open Source Model Weights | Yes, publicly available | — |
| Open Source Models Available(model families) | 3 families | 4 families(winner) |
| Enterprise Data Compliance | Subject to Chinese data laws | — |
| Data Privacy (External Processing) | Higher risk - processed by DeepSeek servers | — |
| API Input Token Cost(USD per 1M tokens) | $0.14(winner) | $2.00 |
| Estimated Training Cost(USD millions) | $5.5M(winner) | $14.2M |
| API Pricing (per 1M tokens, input)(USD) | Not publicly available | — |
| Largest Model Parameter Count(parameters) | 685B (DeepSeek-V3) | 123 billion(winner) |
| Open-Source Weight Availability | Partial (R1 inference-only) | Full (all models) |
| Commercial Use Clarity(null) | Restricted in some jurisdictions; unclear terms | — |
| Open Source License | Closed, proprietary | — |
| Company Location | China | France (EU) |
| 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) | — |
| Real-time Web Access | No | — |
| Native Image Generation | None | — |
| Enterprise API Availability | Limited beta access | — |
| Vision Capability(supported formats) | Limited (text-focused) | — |
| Real-Time Web Search | No (cutoff April 2024) | — |
Show 2 more attributesAverage Citations per Response(count) 2-5 — Supported Languages(languages) 25 languages 40+ languages | ||
| AIME 2024 Math Reasoning Accuracy(%) | 94% | — |
| HumanEval Code Pass Rate(%) | 96.3% | — |
| MMLU Benchmark (General Knowledge)(%) | 92.3% | — |
| Monthly Active Users(millions) | ~8 million | — |
| Training Data Recency | 8 months old (April 2024) | — |
| Microsoft 365 Native Integration | None (API only) | — |
| Ecosystem Integration | Hugging Face, AWS, Azure, Together AI | — |
| Windows OS Market Share(%) | 0% (external integration required) | — |
| Self-hosting/Local Deployment | Fully Supported | — |
| Model Quantization Formats(count) | 4 formats | 6 formats(winner) |
| 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 | 6 months(winner) |
Mistral AIShow 5 more attributes
Show 16 more attributes
Show 2 more attributes
Pros & Cons
10 pros·6 cons across both
DeepSeek
Pros
- DeepSeek-R1 achieves 96.3% on AIME 2024, outperforming most competitors on mathematical reasoning
- API pricing at $0.14 per 1M input tokens—14x cheaper than Mistral Large 2
- 671B parameter MoE model with 37B active parameters reduces computational overhead by 70%
- Strong performance on code generation with 88.7% on LiveCodeBench
- Efficient inference enables deployment on consumer-grade GPUs (16GB+ VRAM)
Cons
- R1 model weights not fully open-sourced; restricted to inference API
- Limited regulatory compliance documentation for EU/US enterprise deployments
- Smaller ecosystem and fewer third-party integrations compared to OpenAI/Anthropic
Mistral AI
Pros
- 100% open-source model weights—Mistral Large 2 (123B) available for unrestricted local deployment
- EU-headquartered with GDPR compliance and no data sharing with third parties
- Strong multilingual support: 92% accuracy on MGSM Chinese math word problems (multilingual evaluation)
- Active open-source community with 45K+ GitHub stars and extensive documentation
- Enterprise support contracts available with SLA guarantees for regulated industries
Cons
- Mistral Large 2 achieves only 85.9% on AIME 2024—10.4 percentage points below DeepSeek-R1
- API pricing at $2.00 per 1M input tokens significantly higher than DeepSeek
- Largest model requires 246GB VRAM for full inference; 14x more expensive to run than DeepSeek MoE
Frequently Asked Questions
5 questions
Yes, significantly. DeepSeek's API costs $0.14 per 1M input tokens compared to Mistral Large 2's $2.00—making DeepSeek approximately 14x cheaper. However, Mistral offers full open-source models that can be self-hosted for zero per-token costs if you have the compute infrastructure.
Resources & Learn More
Curated sources to dive deeper
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Wikipedia
- W
DeepSeek on Wikipedia (opens in new tab)
Chinese AI model focused on reasoning, mathematics, and code efficiency with low computational overhead.
- W
Mistral AI on Wikipedia (opens in new tab)
French AI company providing scalable open-source LLMs with enterprise-grade optimization and modular architecture.
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