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#62

GPT-5 Nano

OpenAI Release: 2025-08-07 Tested on: 2026-04-11 01:44 openai/gpt-5-nano::medium

Summary

GPT-5 Nano scores 6.3 on AI BENCHY and ranks #62. It has N/A reliability, a 59.3% pass rate, $0.066 total cost, and 44.13s average response time.

What makes GPT-5 Nano unique: It stands out most in Coding, where it ranks #3, while Data parsing and extraction is its weakest area at #15.

Score

6.3

Consistency

6.5

Reliability

N/A

Total Cost (Current Price)

$0.066

Total Output Tokens

161,268

Total Input Tokens

0

Input Price

$0.050 / 1M

Output Price

$0.400 / 1M

Tests Correct

Wrong Tests: 11

Attempt pass rate: 59.3%

Flaky tests

8

Flaky tests had mixed outcomes across runs (at least one pass and one fail).

Response Time (avg)

44.13s

Response Time (max): 204.02s

Response Time (total): 485.47s

Wrong answer: 7 Did not follow instructions: 3 Timed out: 1

Generation showcase

Hamster playing table tennis

Prompt: Create a detailed SVG illustration of a hamster playing table tennis.

#62 GPT-5 Nano

medium
Cost
$0.006
Time
108.5s
Tokens
13,209 tok

Run history

Tested on Score Reliability Tests Correct Total Cost Compare
2026-06-04 13:44 New test added 6.3 10.0 $0.081 Compare
2026-05-22 00:17 Suite changed 6.1 10.0 $0.076 Compare
2026-04-11 01:44 First recorded run 6.3 N/A $0.066 Current run

Run comparison

RunScoreConsistencyReliabilityTests CorrectFlaky testsTotal Output TokensTotal Input TokensTotal CostResponse Time (avg)
2026-04-11 01:44 · First recorded run6.36.5N/A7/188161,2680$0.06644.13s
2026-05-22 00:17 · Suite changed6.17.110.08/207184,9220$0.07643.11s
Difference+0.2-0.6-1+1-236540-$0.010+1022ms

These two runs used different benchmark suites, so the deltas reflect both model changes and suite changes.

Charts

Choose the first model, then click a second model to open a side-by-side page.

Total Output Tokens

Score vs Total Output Tokens

Quick Compare

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Category Breakdown

Category Score Consistency Tests Correct
Anti-AI Tricks 6.5 7.9
Coding 6.7 3.5
Combined 10.0 10.0
Data parsing and extraction 3.7 1.7
Domain specific 5.2 4.4
General Intelligence 4.1 10.0
Instructions following 8.5 6.8
Puzzle Solving 5.3 7.2
Tool Calling 10.0 10.0

Compared models