The most-searched AI comparison of 2026. DeepSeek's reasoning model R1 beats GPT-5.4 on math benchmarks at 91% lower cost. Here's the full breakdown.
| Benchmark | DeepSeek R1 | GPT-5.4 | Winner |
|---|---|---|---|
| MATH-500 | 97.3% | 94.5% | R1 +2.8 |
| AIME 2024 | 79.8% | 67.1% | R1 +12.7 |
| GPQA Diamond | 71.5% | 58.2% | R1 +13.3 |
| Codeforces | 96.3% | 96.8% | GPT +0.5 |
| MMLU-Pro | 84.0% | 87.4% | GPT +3.4 |
| SWE-bench Verified | 49.2% | 62.1% | GPT +12.9 |
| Multimodal (vision) | N/A | 85.3% | GPT only |
R1 wins pure reasoning (math/logic/science). GPT-5.4 wins agentic coding, multimodal, and general knowledge.
$0.34
/ M input tokens
$0.50
/ M output tokens
$3.25
/ M input tokens (10x)
$19.50
/ M output tokens (39x)
Processing 1M input + 1M output tokens:
$0.84 R1vs$22.75 GPT-5.4
R1 saves 97% on reasoning tasks
Your app probably has a mix of workloads. Use the right model for each, save money without quality loss.
# Math/logic/reasoning → DeepSeek R1
def solve_math(question):
return client.chat.completions.create(
model="deepseek/deepseek-reasoner", # $0.84/M combined
messages=[{"role": "user", "content": question}],
)
# Coding agents / latest knowledge → GPT-5.4
def code_agent(task):
return client.chat.completions.create(
model="openai/gpt-5.4", # $26.25/M combined
messages=[...],
)
# Don't know which? Let AI decide
client.chat.completions.create(model="auto", ...) # smart routerNo Chinese phone needed for DeepSeek. No VPN needed for GPT. WeChat Pay supported.