Model Comparison

GPT-4.1 nano vs Grok 3

OpenAI vs xAI

Side-by-side benchmarks, pricing, and value analysis. See which model costs less per intelligence point.

GPT-4.1 nano (OpenAI) and Grok 3 (xAI) are both large language models available via API. On list price, GPT-4.1 nano is cheaper, while Grok 3 scores higher on benchmarks. When you factor in token efficiency — how many tokens each model needs for the same task — Grok 3 delivers more intelligence per dollar. List prices can be misleading because different models consume different numbers of tokens for the same work. The effective costs below adjust for this using benchmark data, so you can compare what equivalent work actually costs.

Benchmark Scores

Intelligence Index

GPT-4.1 nano 12.9
Grok 3 25.0

MMLU-Pro

GPT-4.1 nano 0.7
Grok 3 0.8

GPQA

GPT-4.1 nano 0.5
Grok 3 0.7

AIME

GPT-4.1 nano 0.2
Grok 3 0.3

Performance

Metric GPT-4.1 nano Grok 3 Gap
Output tokens/sec 111.1 66.5 1.7x
Time to first token 0.40s 0.74s 1.8x
Context window 400,000 2,000,000 5.0x

Pricing per 1M Tokens

Metric GPT-4.1 nano Grok 3 Gap
Input price / 1M tokens $0.1 $3.0 30.0x
Output price / 1M tokens $0.4 $15.0 37.5x
Cache hit price / 1M tokens $0.5 $0.03 16.7x

Effective Cost per 1M Tokens

List prices adjusted for token efficiency. Different models use different numbers of tokens for the same task — these prices reflect what equivalent work actually costs.

Metric GPT-4.1 nano Grok 3 Gap
Input (adjusted) / 1M $0.3397 $0.8831 2.6x
Output (adjusted) / 1M $5.2494 $1.143 4.6x
Input token ratio 3.40x 0.29x
Output token ratio 13.12x 0.08x

Intelligence vs Price

Higher is smarter, further left is cheaper. Top-left is best value. Prices adjusted for token efficiency.

10 15 20 25 30 35 40 $0.5 $1 $2 $5 $10 $20 Effective $/1M tokens (input + output) Intelligence Index Claude 4.5 Sonn... Gemini 2.5 Pro Grok 3 mini Rea... GPT-4.1 Gemini 2.5 Flas... Claude 4 Sonnet... GPT-4.1 mini DeepSeek R1 052... GPT-4.1 nano Grok 3
GPT-4.1 nano Grok 3 Other models

Value Analysis

Cheaper

GPT-4.1 nano

Higher Benchmarks

Grok 3

Better Value ($/IQ point)

Grok 3

GPT-4.1 nano

$0.43 / IQ point

Grok 3

$0.08 / IQ point

Frequently Asked Questions

Which is cheaper, GPT-4.1 nano or Grok 3?

GPT-4.1 nano is cheaper on list price. GPT-4.1 nano costs $0.1/M input and $0.4/M output tokens. Grok 3 costs $3.0/M input and $15.0/M output tokens. On combined list price, GPT-4.1 nano is 36.0x cheaper than Grok 3. However, list prices alone can be misleading because different models use different numbers of tokens for the same task. Check the effective cost comparison above, which adjusts for token efficiency using benchmark data.

Which scores higher on benchmarks, GPT-4.1 nano or Grok 3?

Grok 3 has a higher Intelligence Index (25.0) compared to GPT-4.1 nano (12.9). The Intelligence Index is a composite score from three industry-standard benchmarks: MMLU-Pro (general knowledge and reasoning), GPQA (graduate-level science), and AIME (mathematical problem solving). A higher score means the model produces more accurate and capable responses across a broad range of tasks. This composite approach is more reliable than any single benchmark because it measures different types of capability.

Which model is better value for money, GPT-4.1 nano or Grok 3?

Grok 3 offers better value at $0.08 per intelligence point compared to GPT-4.1 nano at $0.43 per intelligence point. Cost per intelligence point measures how much you pay for each unit of benchmark performance, calculated as the combined token cost divided by the Intelligence Index score. When token efficiency data is available, this calculation uses effective prices (adjusted for the fact that different models consume different numbers of tokens for the same task) rather than raw list prices. A lower cost per intelligence point means you get more capability per dollar.

Which has a larger context window, GPT-4.1 nano or Grok 3?

Grok 3 supports 2,000,000 tokens compared to GPT-4.1 nano with 400,000 tokens. The context window determines how much text (including your prompt, conversation history, and documents) the model can process in a single request. A larger context window is important for tasks like document summarization, long-form analysis, and multi-turn conversations with extensive history. If your use case involves processing large inputs, the context window may be a deciding factor.

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