💰 Unit Cost Winner DeepSeek V3 $0.14/1M in · $0.28/1M out
⚡ Latency & Speed Winner DeepSeek V3 410ms TTFT · 78 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 V3 or Gemini 4 Argon?

DeepSeek V3 is 93.0% cheaper for input tokens ($0.14 vs. $2.00 per 1M tokens) and $0.28 vs. $10.00 for output tokens (14.3x cost advantage). In terms of operational performance, DeepSeek V3 delivers lower response latency at 410ms TTFT (440ms faster than Gemini 4 Argon) with 78 vs. 115 tok/s throughput. Gemini 4 Argon leads coding evaluations at 84.5% SWE-bench Verified vs. 42.0% for DeepSeek V3 and 93.8% vs. 59.1% 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 V3 Gemini 4 Argon Net Delta / Advantage
Input Cost / 1M Tokens $0.14 $2.00 DeepSeek V3 is 14.3x cheaper
Output Cost / 1M Tokens $0.28 $10.00 DeepSeek V3 (97.2% lower)
Prompt Cache Read / 1M $0.014 $0.100 DeepSeek V3 is 7.1x cheaper
Prompt Cache Write / 1M $0.14 $0.50 DeepSeek V3 is 3.6x cheaper
Context Window (Max Output) 131k (8k out) 1,024k (1000k out) Gemini 4 Argon (7.8x larger context)
Avg TTFT Response Latency 410ms 850ms DeepSeek V3 (440ms faster)
Generation Speed (Throughput) 78 tok/s 115 tok/s Gemini 4 Argon (+37 tok/s faster)
SWE-bench Verified (Coding) 42% 84.5% Gemini 4 Argon (+42.5% lead)
GPQA Diamond (PhD STEM) 59.1% 93.8% Gemini 4 Argon (+34.7% lead)
MMLU-Pro (Multi-Discipline) 75.9% 91.4% Gemini 4 Argon (+15.5% lead)
MATH-500 (Quantitative Logic) 90.2% 98.2% Gemini 4 Argon (+8.0% lead)
Inference Value Score (IVR) 75 / 100 62.6 / 100 DeepSeek V3 (+12.4 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 V3 $3.33

$0.14/1M in · $0.28/1M out

Prompt Cache: $0.014/1M read · $0.14/1M 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
$72.93 / mo
(95.6% lower cost with DeepSeek V3)
Open Full Matchup Analysis →
A

When to Choose DeepSeek V3

Choose DeepSeek V3 (DeepSeek) for cost-sensitive batch text processing, drafting, and open-weight self-hosted inference — offering a 131k context window, 410ms TTFT, and $0.14/1M input pricing ($0.014/1M 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 V3 or Gemini 4 Argon?

DeepSeek V3 is cheaper at $0.14/1M input tokens ($0.28/1M 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 V3 or Gemini 4 Argon?

DeepSeek V3 delivers faster first-token latency at 410ms TTFT (78 tok/s throughput) compared to 850ms TTFT (115 tok/s) for Gemini 4 Argon.

How do DeepSeek V3 and Gemini 4 Argon compare on SWE-bench coding and GPQA Diamond reasoning?

On SWE-bench Verified software engineering, DeepSeek V3 scores 42% versus 84.5% for Gemini 4 Argon. On GPQA Diamond graduate-level STEM reasoning, DeepSeek V3 scores 59.1% versus 93.8% for Gemini 4 Argon (and 90.2% vs 98.2% on MATH-500).

When should you choose DeepSeek V3?

Choose DeepSeek V3 (DeepSeek) for cost-sensitive batch text processing, drafting, and open-weight self-hosted inference — offering a 131k context window, 410ms TTFT, and $0.14/1M input pricing ($0.014/1M 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