💰 Unit Cost Winner DeepSeek R1 $0.55/1M in · $2.19/1M out
⚡ Latency & Speed Winner Gemini 4 Argon 850ms TTFT · 115 tok/s
💻 Coding (SWE) Winner Gemini 4 Argon 84.5% SWE-bench · 96.3% HumanEval
🧠 STEM Reasoning Winner Gemini 4 Argon 93.8% GPQA · 98.2% MATH-500

Which model is better: DeepSeek R1 or Gemini 4 Argon?

DeepSeek R1 is 72.5% cheaper for input tokens ($0.55 vs. $2.00 per 1M tokens) and $2.19 vs. $10.00 for output tokens (3.6x cost advantage). In terms of operational performance, Gemini 4 Argon delivers lower response latency at 850ms TTFT (600ms faster than DeepSeek R1) with 115 vs. 58 tok/s throughput. Gemini 4 Argon leads coding evaluations at 84.5% SWE-bench Verified vs. 49.2% for DeepSeek R1 and 93.8% vs. 71.5% on GPQA Diamond.
Verified daily via automated API latency tests and official documentation.

Verified Performance & Unit Economics Delta (12 Dimensions)

Daily synchronized metrics across token pricing, prompt cache economics, latency, throughput, and 5 capability benchmarks.

Metric / Dimension DeepSeek R1 Gemini 4 Argon Net Delta / Advantage
Input Cost / 1M Tokens $0.55 $2.00 DeepSeek R1 is 3.6x cheaper
Output Cost / 1M Tokens $2.19 $10.00 DeepSeek R1 (78.1% lower)
Prompt Cache Read / 1M $0.140 $0.100 Gemini 4 Argon is 1.4x cheaper
Prompt Cache Write / 1M $0.55 $0.50 Gemini 4 Argon is 1.1x cheaper
Context Window (Max Output) 131k (33k out) 1,024k (1000k out) Gemini 4 Argon (7.8x larger context)
Avg TTFT Response Latency 1450ms 850ms Gemini 4 Argon (600ms faster)
Generation Speed (Throughput) 58 tok/s 115 tok/s Gemini 4 Argon (+57 tok/s faster)
SWE-bench Verified (Coding) 49.2% 84.5% Gemini 4 Argon (+35.3% lead)
GPQA Diamond (PhD STEM) 71.5% 93.8% Gemini 4 Argon (+22.3% lead)
MMLU-Pro (Multi-Discipline) 84% 91.4% Gemini 4 Argon (+7.4% lead)
MATH-500 (Quantitative Logic) 97.3% 98.2% Gemini 4 Argon (+0.9% lead)
Inference Value Score (IVR) 64.8 / 100 62.6 / 100 DeepSeek R1 (+2.2 pts)

Interactive Monthly Token Economics & ROI Forecaster

Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.

25.0M Tokens
500K 50M 250M 500M+
5.0M Tokens
100K 25M 75M 150M+
50% Cached
0% (Uncached) 25% 50% (Typical Agent) 95% Max

Estimated Monthly Spend

DeepSeek R1 $19.58

$0.55/1M in · $2.19/1M out

Prompt Cache: $0.140/1M read · $0.55/1M write

Gemini 4 Argon $76.25

$2.00/1M in · $10.00/1M out

Prompt Cache: $0.100/1M read · $0.50/1M write

Projected Monthly Cost Reduction
$56.68 / mo
(74.3% lower cost with DeepSeek R1)
Open Full Matchup Analysis →
A

When to Choose DeepSeek R1

Choose DeepSeek R1 (DeepSeek) for open-weight chain-of-thought reasoning, synthetic data distillation, and complex mathematical verification — offering a 131k context window, 1450ms TTFT, and $0.55/1M input pricing ($0.140/1M cached reads).

B

When to Choose Gemini 4 Argon

Choose Gemini 4 Argon (Google) for engineered with a 1M output token limit for long-horizon software engineering, cybersecurity vulnerability remediation, and deep multimodal enterprise work across legal and financial domains — offering a 1,024k context window, 850ms TTFT, and $2.00/1M input pricing ($0.100/1M cached reads).

Related Questions & Decision Factors

Which is cheaper: DeepSeek R1 or Gemini 4 Argon?

DeepSeek R1 is cheaper at $0.55/1M input tokens ($2.19/1M output) compared to $2.00/1M input ($10.00/1M output) for Gemini 4 Argon.

Which model has lower TTFT latency and higher tok/s throughput: DeepSeek R1 or Gemini 4 Argon?

Gemini 4 Argon delivers faster first-token latency at 850ms TTFT (115 tok/s throughput) compared to 1450ms TTFT (58 tok/s) for DeepSeek R1.

How do DeepSeek R1 and Gemini 4 Argon compare on SWE-bench coding and GPQA Diamond reasoning?

On SWE-bench Verified software engineering, DeepSeek R1 scores 49.2% versus 84.5% for Gemini 4 Argon. On GPQA Diamond graduate-level STEM reasoning, DeepSeek R1 scores 71.5% versus 93.8% for Gemini 4 Argon (and 97.3% vs 98.2% on MATH-500).

When should you choose DeepSeek R1?

Choose DeepSeek R1 (DeepSeek) for open-weight chain-of-thought reasoning, synthetic data distillation, and complex mathematical verification — offering a 131k context window, 1450ms TTFT, and $0.55/1M input pricing ($0.140/1M cached reads).

When should you choose Gemini 4 Argon?

Choose Gemini 4 Argon (Google) for engineered with a 1M output token limit for long-horizon software engineering, cybersecurity vulnerability remediation, and deep multimodal enterprise work across legal and financial domains — offering a 1,024k context window, 850ms TTFT, and $2.00/1M input pricing ($0.100/1M cached reads).

Explore High-Priority Related Model Matchups