💰 Unit Cost Winner Gemini 2.5 Pro $1.25/1M in · $10.00/1M out
⚡ Latency & Speed Winner Grok 4 480ms TTFT · 95 tok/s
💻 Coding (SWE) Winner Grok 4 73.2% SWE-bench · 95% HumanEval
🧠 STEM Reasoning Winner Grok 4 86.2% GPQA · 97.6% MATH-500

Which model is better: Gemini 2.5 Pro or Grok 4?

Gemini 2.5 Pro is 50.0% cheaper for input tokens ($1.25 vs. $2.50 per 1M tokens) and $10.00 vs. $12.50 for output tokens (2.0x cost advantage). In terms of operational performance, Grok 4 delivers lower response latency at 480ms TTFT (220ms faster than Gemini 2.5 Pro) with 95 vs. 80 tok/s throughput. Grok 4 leads coding evaluations at 73.2% SWE-bench Verified vs. 63.8% for Gemini 2.5 Pro and 86.2% vs. 74.0% 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 Gemini 2.5 Pro Grok 4 Net Delta / Advantage
Input Cost / 1M Tokens $1.25 $2.50 Gemini 2.5 Pro is 2.0x cheaper
Output Cost / 1M Tokens $10.00 $12.50 Gemini 2.5 Pro (20.0% lower)
Prompt Cache Read / 1M $0.313 $0.500 Gemini 2.5 Pro is 1.6x cheaper
Prompt Cache Write / 1M $1.25 $2.50 Gemini 2.5 Pro is 2.0x cheaper
Context Window (Max Output) 1,024k (66k out) 262k (66k out) Gemini 2.5 Pro (3.9x larger context)
Avg TTFT Response Latency 700ms 480ms Grok 4 (220ms faster)
Generation Speed (Throughput) 80 tok/s 95 tok/s Grok 4 (+15 tok/s faster)
SWE-bench Verified (Coding) 63.8% 73.2% Grok 4 (+9.4% lead)
GPQA Diamond (PhD STEM) 74% 86.2% Grok 4 (+12.2% lead)
MMLU-Pro (Multi-Discipline) 85.2% 89.9% Grok 4 (+4.7% lead)
MATH-500 (Quantitative Logic) 95% 97.6% Grok 4 (+2.6% lead)
Inference Value Score (IVR) 56.8 / 100 57.6 / 100 Grok 4 (+0.8 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

Gemini 2.5 Pro $69.53

$1.25/1M in · $10.00/1M out

Prompt Cache: $0.313/1M read · $1.25/1M write

Grok 4 $100.00

$2.50/1M in · $12.50/1M out

Prompt Cache: $0.500/1M read · $2.50/1M write

Projected Monthly Cost Reduction
$30.47 / mo
(30.5% lower cost with Gemini 2.5 Pro)
Open Full Matchup Analysis →
A

When to Choose Gemini 2.5 Pro

Choose Gemini 2.5 Pro (Google) for long-context document and video analysis, large codebase ingestion, and multimodal reasoning — offering a 1,024k context window, 700ms TTFT, and $1.25/1M input pricing ($0.312/1M cached reads).

B

When to Choose Grok 4

Choose Grok 4 (xAI) for real-time financial/market intelligence, PhD-level STEM problem solving, and autonomous code generation — offering a 262k context window, 480ms TTFT, and $2.50/1M input pricing ($0.500/1M cached reads).

Related Questions & Decision Factors

Which is cheaper: Gemini 2.5 Pro or Grok 4?

Gemini 2.5 Pro is cheaper at $1.25/1M input tokens ($10.00/1M output) compared to $2.50/1M input ($12.50/1M output) for Grok 4.

Which model has lower TTFT latency and higher tok/s throughput: Gemini 2.5 Pro or Grok 4?

Grok 4 delivers faster first-token latency at 480ms TTFT (95 tok/s throughput) compared to 700ms TTFT (80 tok/s) for Gemini 2.5 Pro.

How do Gemini 2.5 Pro and Grok 4 compare on SWE-bench coding and GPQA Diamond reasoning?

On SWE-bench Verified software engineering, Gemini 2.5 Pro scores 63.8% versus 73.2% for Grok 4. On GPQA Diamond graduate-level STEM reasoning, Gemini 2.5 Pro scores 74% versus 86.2% for Grok 4 (and 95% vs 97.6% on MATH-500).

When should you choose Gemini 2.5 Pro?

Choose Gemini 2.5 Pro (Google) for long-context document and video analysis, large codebase ingestion, and multimodal reasoning — offering a 1,024k context window, 700ms TTFT, and $1.25/1M input pricing ($0.312/1M cached reads).

When should you choose Grok 4?

Choose Grok 4 (xAI) for real-time financial/market intelligence, PhD-level STEM problem solving, and autonomous code generation — offering a 262k context window, 480ms TTFT, and $2.50/1M input pricing ($0.500/1M cached reads).

Explore High-Priority Related Model Matchups