DeepSeek vs OpenAI: Complete API Comparison 2026

DeepSeek vs OpenAI API Comparison

Compare DeepSeek V3/R1 against GPT-4o/o1. Find the best model for your use case and budget.

Pricing Comparison

| Model | Input (1M) | Output (1M) | vs GPT-4o | |---|---|---|---| | GPT-4o | $2.50 | $10.00 | Baseline | | GPT-4o-mini | $0.15 | $0.60 | 94% cheaper | | DeepSeek V3 | $0.27 | $1.10 | 89% cheaper | | DeepSeek R1 | $0.27 | $1.10 | 89% cheaper | | o3-mini | $1.10 | $4.40 | 56% cheaper | | o1 | $15.00 | $60.00 | 6x more expensive |

Performance Benchmarks

| Benchmark | GPT-4o | DeepSeek V3 | Winner | |---|---|---|---| | MMLU (knowledge) | 88.7 | 88.5 | ≈ Tie | | HumanEval (coding) | 92.0 | 88.3 | GPT-4o | | MATH (math) | 76.6 | 90.2 | DeepSeek V3 | | GSM8K (reasoning) | 93.0 | 93.0 | ≈ Tie |

Feature Comparison

| Feature | GPT-4o | DeepSeek V3 | DeepSeek R1 | |---|---|---|---| | Context Window | 128K | 128K | 128K | | Vision | ✅ | ✅ | ❌ | | Function Calling | ✅ | ✅ | ✅ | | JSON Mode | ✅ | ✅ | ✅ | | Streaming | ✅ | ✅ | ✅ | | Chain-of-Thought | ❌ | ❌ | ✅ | | Open Weight | ❌ | ✅ | ✅ |

Code Example: Same Code for Both

import openai

client = openai.OpenAI(
    api_key="your-hub-key",
    base_url="https://api.apiyihe.org/v1"
)

# Test both models with the same prompt
for model in ["gpt-4o", "deepseek-v3"]:
    response = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": "Solve: 3x + 7 = 22"}]
    )
    print(f"{model}: {response.choices[0].message.content}")

Which Model to Choose?

| Use Case | Recommended Model | |---|---| | Budget applications | DeepSeek V3 | | Complex reasoning | DeepSeek R1 | | Vision/multimodal | GPT-4o | | Startup/SaaS | DeepSeek V3 | | Enterprise | Either (flexible) | | Coding projects | DeepSeek V3 OR GPT-4o |

Cost Projection: 1M API Calls

| Model | Total Cost (est.) | |---|---| | GPT-4o | $6,250 | | DeepSeek V3 | $685 | | Savings with DeepSeek | $5,565 (89%) |