💰 Unit Cost Winner GPT-4o $2.50/1M in · $10.00/1M out
⚡ Latency & Speed Winner GPT-4o 280ms TTFT · 100 tok/s
💻 Coding (SWE) Winner Grok 3 65.8% SWE-bench · 91.5% HumanEval
🧠 STEM Reasoning Winner Grok 3 84.6% GPQA · 89.8% MATH-500

Which model is better: GPT-4o or Grok 3?

GPT-4o is 16.7% cheaper for input tokens ($2.50 vs. $3.00 per 1M tokens) and $10.00 vs. $15.00 for output tokens (1.2x cost advantage). In terms of operational performance, GPT-4o delivers lower response latency at 280ms TTFT (340ms faster than Grok 3) with 100 vs. 88 tok/s throughput. Grok 3 leads coding evaluations at 65.8% SWE-bench Verified vs. 48.0% for GPT-4o and 84.6% vs. 53.6% 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 GPT-4o Grok 3 Net Delta / Advantage
Input Cost / 1M Tokens $2.50 $3.00 GPT-4o is 1.2x cheaper
Output Cost / 1M Tokens $10.00 $15.00 GPT-4o (33.3% lower)
Prompt Cache Read / 1M $1.250 $0.750 Grok 3 is 1.7x cheaper
Prompt Cache Write / 1M $2.50 $3.00 GPT-4o is 1.2x cheaper
Context Window (Max Output) 131k (16k out) 134k (16k out) Grok 3 (1.0x larger context)
Avg TTFT Response Latency 280ms 620ms GPT-4o (340ms faster)
Generation Speed (Throughput) 100 tok/s 88 tok/s GPT-4o (+12 tok/s faster)
SWE-bench Verified (Coding) 48% 65.8% Grok 3 (+17.8% lead)
GPQA Diamond (PhD STEM) 53.6% 84.6% Grok 3 (+31.0% lead)
MMLU-Pro (Multi-Discipline) 75.9% 80% Grok 3 (+4.1% lead)
MATH-500 (Quantitative Logic) 76.6% 89.8% Grok 3 (+13.2% lead)
Inference Value Score (IVR) 47.6 / 100 52.7 / 100 Grok 3 (+5.1 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

GPT-4o $96.88

$2.50/1M in · $10.00/1M out

Prompt Cache: $1.250/1M read · $2.50/1M write

Grok 3 $121.88

$3.00/1M in · $15.00/1M out

Prompt Cache: $0.750/1M read · $3.00/1M write

Projected Monthly Cost Reduction
$25.00 / mo
(20.5% lower cost with GPT-4o)
Open Full Matchup Analysis →
A

When to Choose GPT-4o

Choose GPT-4o (OpenAI) for general enterprise assistants, multimodal customer support, and structured JSON output pipelines — offering a 131k context window, 280ms TTFT, and $2.50/1M input pricing ($1.250/1M cached reads).

B

When to Choose Grok 3

Choose Grok 3 (xAI) for massive compute foundation model specialized in PhD-level science reasoning, real-time data analysis, and enterprise logic — offering a 134k context window, 620ms TTFT, and $3.00/1M input pricing ($0.750/1M cached reads).

Related Questions & Decision Factors

Which is cheaper: GPT-4o or Grok 3?

GPT-4o is cheaper at $2.50/1M input tokens ($10.00/1M output) compared to $3.00/1M input ($15.00/1M output) for Grok 3.

Which model has lower TTFT latency and higher tok/s throughput: GPT-4o or Grok 3?

GPT-4o delivers faster first-token latency at 280ms TTFT (100 tok/s throughput) compared to 620ms TTFT (88 tok/s) for Grok 3.

How do GPT-4o and Grok 3 compare on SWE-bench coding and GPQA Diamond reasoning?

On SWE-bench Verified software engineering, GPT-4o scores 48% versus 65.8% for Grok 3. On GPQA Diamond graduate-level STEM reasoning, GPT-4o scores 53.6% versus 84.6% for Grok 3 (and 76.6% vs 89.8% on MATH-500).

When should you choose GPT-4o?

Choose GPT-4o (OpenAI) for general enterprise assistants, multimodal customer support, and structured JSON output pipelines — offering a 131k context window, 280ms TTFT, and $2.50/1M input pricing ($1.250/1M cached reads).

When should you choose Grok 3?

Choose Grok 3 (xAI) for massive compute foundation model specialized in PhD-level science reasoning, real-time data analysis, and enterprise logic — offering a 134k context window, 620ms TTFT, and $3.00/1M input pricing ($0.750/1M cached reads).

Explore High-Priority Related Model Matchups