OpenAI, released Nov 20, 2024

GPT-4o (2024-11-20)price, context, benchmarks and release details

Provisional: not enough results to rank yet 61% confidence 61 percent, Medium confidence, 3 of 5 expected sources in
42.2
SI Score
#121 of 142 ranked models
Input, per 1M tokens
$2.50models.devFirst-party hosted API; MIT models.dev transcription. Provider documentation: https://platform.openai.com/docs/models. Exact canonical endpoint; lowest short-context Standard USD token tier; cache/batch discounts excluded. Deprecated endpoints excluded; openai/gpt-4o-2024-11-20 Retrieved Oct 9, 2026 · MIT
Open source ↗
Output, per 1M tokens
$10.00models.devFirst-party hosted API; MIT models.dev transcription. Provider documentation: https://platform.openai.com/docs/models. Exact canonical endpoint; lowest short-context Standard USD token tier; cache/batch discounts excluded. Deprecated endpoints excluded; openai/gpt-4o-2024-11-20 Retrieved Oct 9, 2026 · MIT
Open source ↗
Context window
128Kmodels.devPublished source fact Retrieved Oct 9, 2026 · MIT
Open source ↗
Max output
16.4Kmodels.devPublished source fact Retrieved Oct 9, 2026 · MIT
Open source ↗
Released
Nov 20, 2024models.devPublished source fact Retrieved Oct 9, 2026 · MIT
Open source ↗

How the score breaks down

Coding (weight 40 percent) 23.2
Math (weight 15 percent) 35.3
Preference (weight 15 percent) —
Reasoning (weight 30 percent) 47.9

Weights: reasoning 30%, math 15%, coding 40%, preference 15%. Results use fixed 0–100 scales before averaging, and thin evidence is pulled toward 50. Method si-v3-retained-evidence-2, computed Oct 9, 2026, 06:15 UTC.

Around it on the leaderboard

  1. 119 Gemini 2.5 Pro 45.2
  2. 120 Gemma 3 12B IT 45.1
  3. 121 GPT-4o (2024-11-20) 42.2
  4. 122 Gemma 3 27B IT 42.0
  5. 123 o3-mini 41.6

Full leaderboard

Benchmark results

6 benchmarks, 6 results

Each row shows the best published result. Where a model was tested at several settings, such as reasoning effort, open the row to see each one. Hover or tap a value for its source.

GPQA Diamondreasoning 47.9%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Feb 5, 2025 Retrieved Oct 9, 2026 · CC-BY
Open source ↗
47.9
MATH Level 5math 49.8%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Feb 5, 2025 Retrieved Oct 9, 2026 · CC-BY
Open source ↗
49.8
OTIS Mock AIME 2024–2025math 6.3%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Feb 25, 2025 Retrieved Oct 9, 2026 · CC-BY
Open source ↗
6.3
Aider Polyglotcoding 18.2%Aider polyglotPublished source fact [variant] Aider polyglot; 225 cases; 2 attemptsPublished Dec 30, 2024 Retrieved Oct 9, 2026 · Apache-2.0
Open source ↗
18.2
SWE-bench Verifiedcoding 31.0%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Feb 11, 2026 Retrieved Oct 9, 2026 · CC-BY
Open source ↗
31.0
Terminal-Benchcoding 8.3%Official model cards via models.devLab-reported; metric success rate; transcribed by MIT models.dev catalog; not independently evaluated [variant] Published Mar 11, 2026 Retrieved Oct 9, 2026 · factual citation; MIT transcription
Open source ↗
8.3

Normalization uses a fixed 0–100 scale for each unit, independent of other models. Compare evaluation conditions before reading a small gap as decisive. “Lab-reported” marks the provider's own published figure.

Details and sources

Open weights
Nomodels.devPublished source fact Retrieved Oct 9, 2026 · MIT
Open source ↗
License
not yet reported
Input modalities
text, imagemodels.devPublished source fact Retrieved Oct 9, 2026 · MIT
Open source ↗
First seen by SuperIndex
Oct 8, 2026
Coverage
49% of expected source weight

Reported (3)

  • Aider polyglotOct 8, 2026
  • Epoch AI BenchmarkingOct 8, 2026
  • Official model cards via models.devOct 8, 2026

Awaiting (2)

  • LiveBench17% of weight
  • LMArena / Arena34% of weight

Confidence rises as pending sources publish. Some sources never cover some models, so confidence reaches 100% at 80% of expected weight.

What changed

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