BenchLeader

GPT-5.2

Reasoning effort

GPT-5.2 is an OpenAI proprietary reasoning model, released 11 Dec 2025. Its best configuration (xhigh reasoning effort) ranks #107 of 775 on the BenchLeader Index at 60.7 ±3.5, in the upper half. The ± is the point: 198 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 instruction following (74) and lowest in agents & tools (55). At $4.81 per million tokens blended it is among the most expensive ranked models. Output speed of 83 tokens per second puts it slower than most, with a first answer in 104.1 s. It has been measured at 6 reasoning-effort settings; this summary describes the best-scoring one, and the tabs above switch between them. Last measured 13 Oct 2026.

Blended price
$4.81/M
$1.75 in · $14.00 out
Output speed
83 tok/s
measured by Artificial Analysis
First answer
104 s
first token 2.51 s
Context
400k
Full answer
110 s
median, reasoning included
Cost per run
–
Released
11 Dec 2025
How it scores by categoryDashed line = average model (50). One step of 15 = one standard deviation.
  1. Overall index61
  2. Reasoning61
  3. Coding63
  4. Agents & tools55
  5. Maths57
  6. Knowledge57
  7. Instruction following74
  8. Long context67
  9. Composite64

Versions

OpenAI has shipped 11 models under this name. Each is ranked on its own results; a newer version often has fewer results so far, which holds its index nearer the average until more arrive.

ModelReleasedIndexRank
GPT-5.69 Jul 2026––
GPT-5.5xhigh23 Apr 202666.1#34
GPT-5.4xhigh5 Mar 202664.8#44
GPT-5.2xhighthis page11 Dec 202560.7#107
GPT-5.1high12 Nov 202557.8#169
GPT-5high7 Aug 202558.0#165
GPT-4.114 Apr 202548.4#420
GPT-4.527 Feb 202549.8#380
GPT 4 012525 Jan 202445.5#505
GPT 4 061313 Jun 202339.8#662
GPT-414 Mar 202343.4#555

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
no reasoning48.8#40873 tok/s0.71 s$0.0049Agents & tools 40 · Coding 50 · Composite 49 · Instruction following 49 · Knowledge 56 · Long context 47 · Maths 51 · Multimodal 49 · Reasoning 48
low49.7#38352 tok/s2.51 s$0.0049Coding 50 · Knowledge 43 · Maths 58 · Reasoning 48
medium57.6#17552 tok/s2.51 s$0.0049Agents & tools 61 · Coding 59 · Composite 59 · Instruction following 65 · Knowledge 52 · Long context 60 · Maths 65 · Multimodal 58 · Reasoning 54
high56.0#21352 tok/s2.51 s$0.0049Agents & tools 59 · Coding 59 · Human preference 62 · Knowledge 44 · Maths 62 · Multimodal 58 · Reasoning 56
xhighbest60.7#10783 tok/s104 s$0.0049Agents & tools 55 · Coding 63 · Composite 64 · Instruction following 74 · Knowledge 57 · Long context 67 · Maths 57 · Reasoning 61
not stated58.1#16152 tok/s2.51 s$0.0049Agents & tools 61 · Coding 53 · Human preference 67 · Instruction following 56 · Knowledge 56 · Maths 64 · Multimodal 59 · Reasoning 60

How its index has moved

8 Sept 2026 to 19 Oct 2026
515967
  • xhigh
  • not stated
  • medium
  • 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

Benchmarkno reasoninglowmediumhighxhighnot statedSourceTrend
GPQA Diamond73.2%#15782.7%#11087.9%#6488.2%#6291.4%#31–Epoch AI Benchmarking Hub
Humanity's Last Exameffort not stated–––––27.8%#14Scale AI / CAIS
SimpleBench–––45.8%#52–45.8%#52SimpleBench
Arena Hard Prompts–––1460#95–1497#38Arena (LMArena)
LiveBench 1.0 Reasoningnot in index–––83.2%#44––LiveBench
GPQA Diamond (AA)not in index71.2%#285–86.4%#109–90.3%#63–Artificial Analysis
Humanity's Last Exam (AA)not in index8.0%#348–26.7%#175–37.7%#101–Artificial Analysis
GPQA Diamond (Vals)not in index––––91.7%#18–Vals AI
Kagi LLM Benchmarkeffort not stated–––––73.3%#18Kagi LLM Benchmark
ARC-AGI-1–55.7%#16072.7%#13078.7%#11586.2%#9794.5%#37ARC Prize
ARC-AGI-2–9.7%#15026.7%#12943.3%#10752.9%#9872.9%#50ARC Prize
CritPt0.6%#249–7.9%#139–11.6%#109–Artificial Analysis
SciPredictnot in indexeffort not stated–––––20.6%#6Scale AI SEAL
Chess Puzzlesnot in index2.0%#13926.0%#7536.0%#4845.0%#2848.0%#19–Epoch AI Benchmarking Hub
Mystery Game Puzzlesnot in index14.0%#9610.0%#11122.0%#5423.0%#50––Epoch AI Benchmarking Hub
CL-benchnot in index–––18.1%#13–18.2%#12Epoch AI Benchmarking Hub
LMCAnot in index––––43.9%#97–Epoch AI Benchmarking Hub
DTBenchnot in index––––90.9%#76–Epoch AI Benchmarking Hub
ForecastBenchnot in indexeffort not stated–––––64.2%#31ForecastBench

Coding

Benchmarkno reasoninglowmediumhighxhighnot statedSourceTrend
SWE-bench Verified (Epoch)–––73.8%#17––Epoch AI Benchmarking Hub
WeirdML49.6%#7049.6%#7063.4%#42–72.2%#30–WeirdML
GSO-Bench–––27.4%#13––GSO-Bench
Arena Coding–––1489#94–1515#45Arena (LMArena)
Arena WebDeveffort not stated–––––1417#82Arena (LMArena)
LiveBench 1.0 Codingnot in index–––76.1%#47––LiveBench
LiveCodeBench––––85.4%#31–Vals AI
SWE-bench (Vals)not in index––––75.8%#41–Vals AI
SWE-Bench Proeffort not stated–––––29.9%#16Scale AI SEAL
SWE-bench Verified (bash only)–––72.8%#7–69.0%#14SWE-bench
SWE-bench Verified (any scaffold)not in index–––72.8%#7––SWE-bench
Vibe Code Bench v1.1not in index––––53.5%#55–Vals AI
SWE-Bench Pro (private)not in indexeffort not stated–––––29.9%#16Scale AI SEAL
ALE-Benchnot in index––1249.8#371293.5#35––Epoch AI Benchmarking Hub
AlgoTunenot in index––2.0#1–––AlgoTune

Agents & tools

Benchmarkno reasoninglowmediumhighxhighnot statedSourceTrend
Terminal-Bench––64.9%#10––64.9%#9Terminal-Bench
GDPval49.7%#1–––––OpenAI
Remote Labor Index––2.5%#11––2.1%#12Scale AI / CAIS
LiveBench Agentic Codingnot in index–––50.3%#44––LiveBench
Terminal-Bench Hard31.8%#101–43.2%#41–47.0%#27–Artificial Analysis
τ²-Bench Telecom (AA)not in index46.5%#204–74.3%#140–84.8%#96–Artificial Analysis
MCP Atlas––––67.6%#23–Scale AI SEAL
BFCL Overalleffort not stated–––––55.9%#14Berkeley Function Calling Leaderboard
τ²-bench61.6%#8––84.8%#3––τ²-bench
τ³-bench Bankingnot in index12.6%#24––32.2%#13––τ²-bench
τ³-Banking (AA)11.1%#137–––––Artificial Analysis
Poker Agentnot in indexeffort not stated–––––1131.8%#1Vals AI
Terminal-Bench 2.0 (Vals)not in index–––51.7%#18––Vals AI
DeepResearch Benchnot in index–41.1%#35––––DeepResearch Bench
METR Time Horizonsnot in index–––75.3%#4––METR
Vending-Bench 2not in indexeffort not stated–––––3591.3#47Andon Labs

Maths

Benchmarkno reasoninglowmediumhighxhighnot statedSourceTrend
FrontierMath Tiers 1–3––––67.4%#31–Epoch AI Benchmarking Hub
FrontierMath Tier 4––––31.7%#38–Epoch AI Benchmarking Hub
OTIS Mock AIME62.2%#15878.9%#11993.9%#5696.1%#3996.1%#41–Epoch AI Benchmarking Hub
ProofBench––––15.0%#59–Vals AI
LiveBench Mathematicsnot in index–––93.2%#24––LiveBench
AIME (Vals)––––96.9%#2–Vals AI
MGSM––––94.0%#6–Vals AI
AIME 2026–––98.3%#4––MathArena
HMMT February 2026–––97.0%#3––MathArena
MathArena Apexpossible contamination–––13.5%#18––MathArena
Arena Maths–––1457#65–1454#68Arena (LMArena)

Knowledge

Benchmarkno reasoninglowmediumhighxhighnot statedSourceTrend
SimpleQA Verified–32.8%#6032.7%#6234.3%#5237.1%#48–Epoch AI Benchmarking Hub
LiveBench 1.0 Data Analysisnot in index–––78.2%#27––LiveBench
AA-Omniscience-12.3#208–0.3#132–-0.9#143–Artificial Analysis
MMLU-Pro––––86.2%#44–Vals AI
LegalBench––––82.8%#64–Vals AI
CorpFin––––65.9%#25–Vals AI
TaxEval––––75.8%#11–Vals AI
MedQA––––94.1%#19–Vals AI
MultiNRCeffort not statedpossible contamination–––––42.2%#17Scale AI SEAL
CaseLaw v2not in index––––66.0%#8–Vals AI
MedCodenot in index––––49.8%#20–Vals AI
MedScribenot in index––––84.4%#35–Vals AI
DrugDiscoveryBenchnot in index––––29.3%#15–Scale AI SEAL
EBR-benchnot in index––––23.0%#16–Epoch AI Benchmarking Hub
AA-Omniscience: accuracynot in index30.9%#173–38.3%#134–44.3%#93–Artificial Analysis
AA-Omniscience: non-hallucinationnot in index37.7%#174–38.4%#169–18.8%#291–Artificial Analysis

Instruction following

Benchmarkno reasoningmediumhighxhighnot statedSourceTrend
LiveBench Languagenot in index––79.8%#32––LiveBench
LiveBench Instruction Followingnot in index––61.8%#55––LiveBench
IFBench47.4%#18665.2%#103–75.4%#31–Artificial Analysis
TutorBencheffort not stated––––53.5%#11Scale AI SEAL
Arena Instruction Followingnot in index––1427#96–1457#55Arena (LMArena)

Human preference

Benchmarkhighnot statedSourceTrend
Arena Text1437#991476#34Arena (LMArena)
Arena Creative Writingnot in index1386#1271433#68Arena (LMArena)
Arena Multi-turnnot in index1442#981495#16Arena (LMArena)
Arena Longer Queriesnot in index1434#1141472#58Arena (LMArena)

Multimodal

Benchmarkno reasoningmediumhighxhighnot statedSourceTrend
Arena Vision––1246#67–1268#47Arena (LMArena)
MMMU-Pro65.8%#17174.6%#109–––Artificial Analysis
VISTAeffort not stated––––46.6%#20Scale AI SEAL
MMMU-Pro (Vals)not in index–––86.7%#17–Vals AI
MortgageTaxnot in index–––67.1%#29–Vals AI
SAGEnot in index–––49.3%#22–Vals AI
VPCTnot in index––67.0%#484.0%#2–Epoch AI Benchmarking Hub

Long context

Benchmarkno reasoningmediumhighxhighnot statedSourceTrend
AA-LCR45.7%#33070.3%#208–82.7%#35–Artificial Analysis
Arena Documentnot in index––1423#41–1425#40Arena (LMArena)

Composite

Benchmarkno reasoningmediumhighxhighnot statedSourceTrend
Epoch Capabilities Indexnot in indexeffort not stated––––153.7#45Epoch AI Benchmarking Hub
LiveBench 1.0not in index––74.6%#40––LiveBench
AA Intelligence Index v4.3.217.0#26026.5#149–30.4#118–Artificial Analysis

Safety & honesty

Benchmarknot statedSourceTrend
FORTRESSnot in index17.5%#45Scale AI SEAL
MASKnot in indexpossible contamination86.7%#13Scale AI SEAL
PropensityBenchnot in index34.4%#9Scale AI SEAL

Where it wins

Benchmarks where this configuration ranks in the top five of every configuration measured.

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
OpenAI59 tok/s0.60 s$3.50$28.00400k–
Azure55 tok/s1.68 s$1.75$14.00400k–
OpenAI52 tok/s1.69 s$1.75$14.00400k–

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 $0.175 per 1M. Reasoning tokens are not modelled.

WorkloadTokens in / outCostWith cachingTime
Chat reply400 / 300$0.0049$0.00441.8 min
Summarize a 30-page report12,000 / 600$0.029$0.0151.9 min
Code edit6,000 / 1,500$0.032$0.0242.0 min
Agentic coding session60,000 / 4,000$0.161$0.0902.5 min
Structured extraction2,000 / 200$0.0063$0.00391.8 min

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See also

Data as of 21 Oct 2026. Compare these configurations.

Cite as: BenchLeader, “GPT-5.2: benchmarks, pricing, speed and rank”, https://www.benchleader.com/models/gpt-5-2, data as of 21 Oct 2026.