BenchLeader

GPT-4o

Reasoning effort

GPT-4o is an OpenAI proprietary model, released 13 May 2024. Its best configuration ranks #558 of 785 on the BenchLeader Index at 43.9 ±4.9, in the lower half. The ± is the point: 316 other configurations score within that range, so they and this one cannot be told apart on quality alone — price and speed are what separate them. It scores highest in human preference (63) and lowest in agents & tools (34). At $4.38 per million tokens blended it is among the most expensive ranked models. Output speed of 164 tokens per second puts it in the fastest quarter, with a first answer in 1.1 s. It has been measured at 2 reasoning-effort settings; this summary describes the best-scoring one, and the tabs above switch between them. Last measured 22 Oct 2026.

Blended price
$4.38/M
$2.50 in · $10.00 out
Output speed
164 tok/s
measured by Artificial Analysis
First answer
1.08 s
first token 0.62 s
Context
128k
Full answer
4.12 s
median, reasoning included
Cost per run
–
Released
13 May 2024
How it scores by categoryDashed line = average model (50). One step of 15 = one standard deviation.
  1. Overall index44
  2. Reasoning38
  3. Coding35
  4. Agents & tools34
  5. Maths41
  6. Knowledge43
  7. Instruction following41
  8. Human preference63
  9. Multimodal48
  10. Long context53
  11. Composite39

Reasoning-effort configurations

The same model behaves differently depending on how much it is allowed to think. Each row is one setting, scored only on the benchmarks that were run at that setting. “Not stated” collects results from publishers that did not say which setting they used; for a reasoning model that is usually its thinking mode, but we do not assume it. Pick a setting here or at the top of the page to see its price, speed and category scores.

EffortIndexRankSpeedFirst answerChat reply costCategories
high41.7#619135 tok/s1.08 s$0.0040Coding 29 · Knowledge 46 · Maths 41
not statedbest43.9#558164 tok/s1.08 s$0.0040Agents & tools 34 · Coding 35 · Composite 39 · Human preference 63 · Instruction following 41 · Knowledge 43 · Long context 53 · Maths 41 · Multimodal 48 · Reasoning 38

How its index has moved

8 Sept 2026 to 21 Oct 2026
364350
  • not stated
  • high

The index is recomputed from scratch every day, so a line moves when a new benchmark result lands, when a publisher revises a score, or when the models it is normalised against change. Early movement usually means the score is still settling.

Benchmark results

See all LLM benchmarks. One column per reasoning effort. Rank is among every configuration of every model on that benchmark. Hover a score for the run it came from.

Reasoning

Benchmarkhighnot statedSourceTrend
GPQA Diamondeffort not stated–49.2%#216Epoch AI Benchmarking Hub
Humanity's Last Exameffort not stated–2.7%#48Scale AI / CAIS
SimpleBencheffort not stated–17.8%#85SimpleBench
Arena Hard Promptseffort not stated–1455#106Arena (LMArena)
GPQA Diamond (AA)not in indexeffort not stated–65.5%#343Artificial Analysis
Humanity's Last Exam (AA)not in indexeffort not stated–4.0%#555Artificial Analysis
GPQA Diamond (Vals)not in index53.8%#112–Vals AI
Kagi LLM Benchmarkeffort not stated–75.0%#12Kagi LLM Benchmark
GPQA Diamond (HELM)not in indexeffort not stated–52.0%#35HELM Capabilities
ARC-AGI-1effort not stated–4.5%#248ARC Prize
ARC-AGI-2effort not stated–0.0%#242ARC Prize
CritPteffort not stated–0.0%#315Artificial Analysis
Chess Puzzlesnot in indexeffort not stated–6.0%#130Epoch AI Benchmarking Hub
LMCAnot in indexeffort not stated–16.6%#169Epoch AI Benchmarking Hub
DTBenchnot in indexeffort not stated–64.5%#169Epoch AI Benchmarking Hub
ForecastBenchnot in indexeffort not stated–61.2%#68ForecastBench

Coding

Benchmarkhighnot statedSourceTrend
SWE-bench Verified (Epoch)effort not stated–31.0%#33Epoch AI Benchmarking Hub
WeirdMLeffort not stated–25.1%#136WeirdML
GSO-Bencheffort not stated–0.0%#35GSO-Bench
Arena Codingeffort not stated–1467#121Arena (LMArena)
LiveCodeBench43.4%#118–Vals AI
Aider Polygloteffort not stated–45.3%#28Aider polyglot leaderboard
SWE-bench Verified (bash only)effort not stated–21.6%#41SWE-bench
SWE-bench Verified (any scaffold)not in indexeffort not stated–38.8%#48SWE-bench
CadEvalnot in indexeffort not stated–26.0%#12Epoch AI Benchmarking Hub

Agents & tools

Benchmarknot statedSourceTrend
GDPval9.9%#11OpenAI
Cybench12.5%#14Cybench
Terminal-Bench Hard8.3%#235Artificial Analysis
τ²-Bench Telecom (AA)not in index28.9%#265Artificial Analysis
BALROGnot in index32.3%#16BALROG
TheAgentCompanynot in index8.6%#8TheAgentCompany
METR Time Horizonsnot in index40.8%#30METR
BrowseCompnot in index0.7%#22OpenAI

Maths

Benchmarkhighnot statedSourceTrend
FrontierMath Tiers 1–3effort not stated–0.3%#117Epoch AI Benchmarking Hub
OTIS Mock AIMEeffort not stated–6.4%#242Epoch AI Benchmarking Hub
MATH Level 5effort not stated–53.3%#51Epoch AI Benchmarking Hub
AIME (Vals)14.0%#82–Vals AI
MGSM90.7%#35–Vals AI
Omni-MATH (HELM)effort not stated–29.3%#46HELM Capabilities
MATH 500not in index75.2%#43–Vals AI
Arena Mathseffort not stated–1404#144Arena (LMArena)

Knowledge

Benchmarkhighnot statedSourceTrend
SimpleQA Verifiedeffort not stated–26.0%#69Epoch AI Benchmarking Hub
AA-Omniscienceeffort not stated–-10.5#195Artificial Analysis
MMLU-Pro74.1%#108–Vals AI
LegalBench82.2%#72–Vals AI
CorpFin45.9%#108–Vals AI
TaxEval74.5%#34–Vals AI
MedQA88.2%#54–Vals AI
MultiNRCeffort not stated–12.4%#40Scale AI SEAL
MMLU-Pro (HELM)not in indexeffort not stated–71.3%#38HELM Capabilities
CaseLaw v2not in index59.7%#25–Vals AI
MMLUnot in indexeffort not stated–88.1%#1Epoch AI Benchmarking Hub
AA-Omniscience: accuracynot in indexeffort not stated–23.7%#252Artificial Analysis
AA-Omniscience: non-hallucinationnot in indexeffort not stated–62.1%#71Artificial Analysis

Instruction following

Human preference

Benchmarknot statedSourceTrend
Arena Text1443#88Arena (LMArena)
Arena Creative Writingnot in index1421#81Arena (LMArena)
Arena Multi-turnnot in index1471#62Arena (LMArena)
Arena Longer Queriesnot in index1440#108Arena (LMArena)
Lech Mazur Creative Writingnot in index81.8%#13Lech Mazur

Multimodal

Benchmarkhighnot statedSourceTrend
Arena Visioneffort not stated–1243#70Arena (LMArena)
MMMU-Proeffort not stated–56.3%#224Artificial Analysis
VISTAeffort not stated–38.0%#42Scale AI SEAL
MMMU (validation)effort not stated–70.7%#21MMMU
MMMU-Pro (official)not in indexeffort not stated–51.9%#15MMMU
MMMU-Pro (Vals)not in index64.0%#76–Vals AI
MortgageTaxnot in index61.0%#62–Vals AI
GeoBenchnot in indexeffort not stated–71.0%#12GeoBench
VPCTnot in indexeffort not stated–40.0%#16Epoch AI Benchmarking Hub
Video-MMEnot in indexeffort not stated–71.9%#6Video-MME
ScienceQAnot in indexeffort not stated–88.5%#2Epoch AI Benchmarking Hub

Long context

Benchmarknot statedSourceTrend
AA-LCR56.0%#295Artificial Analysis

Safety & honesty

Benchmarknot statedSourceTrend
FORTRESSnot in index47.2%#15Scale AI SEAL
MASKnot in index60.1%#38Scale AI SEAL
PropensityBenchnot in index46.1%#8Scale AI SEAL

Where to run it

Every provider serving this model through OpenRouter, with throughput and first-token latency measured on live traffic over the last 30 minutes and each provider’s own price. Purple marks the best in each column.

ProviderSpeedFirst tokenInput $/MOutput $/MContextQuantisation
Azure77 tok/s1.44 s$2.50$10.00128k–
OpenAI77 tok/s0.62 s$2.50$10.00128k–

What a task costs

Estimates from list price, output speed and time to first answer for the best configuration. “With caching” assumes three-quarters of the input is served from the prompt cache at $1.25 per 1M. Reasoning tokens are not modelled.

WorkloadTokens in / outCostWith cachingTime
Chat reply400 / 300$0.0040$0.00362.9 s
Summarize a 30-page report12,000 / 600$0.036$0.0254.7 s
Code edit6,000 / 1,500$0.030$0.02410.2 s
Agentic coding session60,000 / 4,000$0.190$0.13425.4 s
Structured extraction2,000 / 200$0.0070$0.00512.3 s

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Questions people ask

What are GPT-4o's benchmark scores?
As of 24 Oct 2026, GPT-4o scores 43.9 ±4.9 on the BenchLeader Index, strongest in human preference (63) and weakest in agents & tools (34). That rests on 36 independent benchmark results, and every score on this page links to its publisher.
How does GPT-4o rank?
GPT-4o is #558 of 785 ranked configurations on 24 Oct 2026. 316 other configurations score within its margin, so the exact position matters less than the band. The leader is Claude Opus 5.5 (max effort) at 72.6.
How much does GPT-4o cost per 1M tokens?
As of 24 Oct 2026, GPT-4o costs $2.50 per 1M input tokens and $10.00 per 1M output tokens, $4.38 blended (three input tokens per output token).
When was GPT-4o released?
OpenAI released GPT-4o on 13 May 2024. BenchLeader has results for it at 2 reasoning-effort settings.

Data as of 24 Oct 2026. Compare these configurations.

Cite as: BenchLeader, “GPT-4o: benchmarks, pricing, speed and rank”, data as of 24 Oct 2026, https://www.benchleader.com/models/gpt-4o.