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How smart is GPT-4o (Nov 2024)?

69 SI-IQ · 90% interval 64–73
Rank 78 of 80 ranked models · Plausible rank range 75–80

GPT-4o (Nov 2024) is a model from OpenAI, released on Nov 20, 2024. Its SI-IQ is 69 (90% interval 64–73), estimated from 5 benchmark results by 2 evaluators. Its strongest area is Math and its weakest is Coding.

Data as of Oct 18, 2026 · method 1.0.0

Facts

Developer: OpenAI
Released: Nov 20, 2024
Access: API access
Benchmarks used: 5 benchmark results from 2 evaluators
API price: $2.50 input / $10 output per 1M tokens (developer's price list, Oct 18, 2026)
Context window: 128,000 tokens

Ability by area (SI-IQ, 90% interval)

Every benchmark result we used

Each score is one value exactly as the source published it: for Epoch AI the best of its run settings, for OpenRouter the median hosting provider (the developer's own hosting when it ran the test). “Implied SI-IQ” is the ability that this single result points to; the overall SI-IQ is their precision-weighted average.

Benchmark	Area	Score	Implied SI-IQ	Evaluated by	As listed by the source	Retrieved	Source
MATH Level 5	Math	49.8%	69	Epoch AI	`gpt-4o-2024-11-20`	Oct 18, 2026	Epoch AI Benchmarking Hub · data file
OTIS Mock AIME 2024–2025	Math	6.2%	65	Epoch AI	`gpt-4o-2024-11-20`	Oct 18, 2026	Epoch AI Benchmarking Hub · data file
Aider Polyglot	Coding	18.2%	75	Aider	`gpt-4o-2024-11-20`	Oct 18, 2026	Aider LLM Leaderboards · data file
SWE-bench Verified (Epoch AI run)	Coding	31.0%	60	Epoch AI	`gpt-4o-2024-11-20`	Oct 18, 2026	Epoch AI Benchmarking Hub · data file
GPQA Diamond	Knowledge	47.9%	70	Epoch AI	`gpt-4o-2024-11-20`	Oct 18, 2026	Epoch AI Benchmarking Hub · data file

Models with similar SI-IQ

Model	SI-IQ
GPT-4o miniOpenAI	68	GPT-4o (Nov 2024) vs GPT-4o mini
Llama 3.3 70BMeta AI	66	GPT-4o (Nov 2024) vs Llama 3.3 70B
GPT-4o (Aug 2024)OpenAI	73	GPT-4o (Nov 2024) vs GPT-4o (Aug 2024)
GPT-4.1 nanoOpenAI	76	GPT-4o (Nov 2024) vs GPT-4.1 nano
Gemma 3 27BGoogle DeepMind	77	GPT-4o (Nov 2024) vs Gemma 3 27B

Computed with SI-IQ method version 1.0.0. Read the full methodology

Data sources: Aider polyglot leaderboard data (Apache-2.0); Epoch AI, ‘Capabilities & benchmarking’ (CC BY 4.0); LiteLLM, model_prices_and_context_window.json (MIT) (licences and changes).