[whatmodel.sh](../../README.md) / GPT-5.2
# GPT-5.2
OpenAI · released 2025-12 · closed weights
**score 72.4** ±0.5 · #57 of 732 · from 51 benchmark results across 27 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks GPT-5.2 among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█▄▅▅●▄▂▁ #65 of 313 21% 14
coding ▁▁▁▃▅▇█▇●▄▃▁ #85 of 469 10% 3
chat & writing ▁▁▁▃▆▄▆█▆▇●▃ #71 of 402* 2.5% 1
math ▁▁▃▅█▆▄█●▂▁▁ #34 of 431 30% 9
vision & documents ▁▁▂▂▁▃▄▂▆●█▄ #48 of 153 4.9% 5
web search ▁▁▁▅●█▃▄·▁·▁ #56 of 84* 0.9% 1
agent tasks · #65 of 313 · 21% of its score
Trails [GLM-5.1](glm-5-1.md), [Beam 1.0 501B A23B](beam-1-0-501b-a23b.md), [Claude Opus 4.5](claude-opus-4-5.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
SWE-bench Verified 73.8% ▁··▁·▁▂▁▄●▄▁ beats 14/29 hard · avg 19% 4.4% sharp
MCP-Atlas (public set) 68.0% ▄······▄▄▄▄● beats 6/6 hard · avg 26% 2.9% at its level · several runs
Terminal-Bench 2.0 with Terminus … 54.0% ▃··▃·▃▃██▅▃● beats 13/14 very hard · avg 6.1% 2.3% sharp · at its level
ContractBench success rate (33 ob… 74.7% ▆··▆▄▂▄▂▆▆█● beats 22/23 very hard · avg 13% 1.7% sharp · at its level
τ³-bench banking_knowledge (v1.0.… 32.2% ▃▃▂▃·▆▃●█▅▅▂ beats 13/25 very hard · avg 4.3% 1.6% at its level
τ²-bench aggregate 87.1% ▄····█▄●···▄ beats 4/5 medium · avg 45% 1.2% typical
Vibe Code Bench (test split, 50 w… 53.5% ▃··▃·▃██▃▃▃● beats 13/14 very hard · avg 2.8% 1.1% sharp · at its level · noisy
Thinkingbox-bench pass@1 (507 sta… 46.3% ▅··▃·▃·█▃●██ beats 8/15 very hard · avg 5.1% 1.1% sharp · at its level
BFCL V4 (December 2025 archive) 55.9% ▁▁▁▂██▅▅●▂▂▂ beats 66/77 hard · avg 31% 1.1% at its level
τ³-bench airline (tau2-bench ≥ v1… 83.0% ▃······▃·▃●▃ beats 5/6 medium · avg 51% 0.9% typical
τ³-bench retail (tau2-bench ≥ v1.… 81.6% ▄▄·█··▄·●··▄ beats 5/6 medium · avg 48% 0.8% typical
τ³-bench telecom (tau2-bench ≥ v1… 89.7% █·●█▄······▄ beats 2/6 medium · avg 64% 0.7% maxed out
τ²-bench Telecom (maker) 98.7% ▃·▅▅▃▃··█▃▃● beats 13/13 hard · avg 16% 0.6% sharp · noisy
τ²-Bench-Verified (GPT-5.1 user) 80.3% ▂··▄···▂▄▄●▄ beats 11/13 medium · avg 45% 0.4% typical
coding · #85 of 469 · 10% of its score
Trails [Llama 3.1 Nemotron Ultra 253B v1](llama-3-1-nemotron-ultra-253b.md), [GLM-4.7](glm-4-7.md), [Gemini 3.5 Flash-Lite](gemini-3-5-flash-lite.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena WebDev (Code Arena) 1416.4 ▁▁▃▂▅●▃▆▅▅▁▂ beats 50/129 avg 981 4.5% sharp
SWE-bench Verified 73.8% ▁··▁·▁▂▁▄●▄▁ beats 14/29 hard · avg 19% 4.4% sharp
Vibe Code Bench (test split, 50 w… 53.5% ▃··▃·▃██▃▃▃● beats 13/14 very hard · avg 2.8% 1.1% sharp · at its level · noisy
chat & writing · #71 of 402 · 2.5% of its score
Beats [Claude Haiku 5.5](claude-haiku-5-5.md), [DeepSeek V4 Flash (0731)](deepseek-v4-flash.md) here, though they rank higher overall. Trails [Gemma 4 26B-A4B](gemma-4-26b-a4b.md), [Nemotron Cascade 2 30B-A3B](nemotron-cascade-2-30b-a3b.md), [Gemma 4 31B](gemma-4-31b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1437.3 ▁▁▁▂▃▃▃▆▆▇●▃ beats 299/385 avg 1260 2.5% blunt
math · #34 of 431 · 30% of its score
Beats [Gemini 3.8 Flash](gemini-3-8-flash.md), [DeepSeek V4.1 Flash](deepseek-v4-1-flash.md), [DeepSeek V4 Pro (0813)](deepseek-v4-pro.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
FrontierMath tiers 1–3 v2 67.4% ▂··▁▁▃▄▄█●▄▃ beats 56/86 very hard · avg 1.2% 12% sharp · at its level
FrontierMath tier 4 v2 31.7% ▁▂▁▂█●▂▃▂▁▁▁ beats 36/67 very hard · avg 0.1% 8.6% sharp · at its level
MathArena ArXivMath February 2026… 37.5% ▁▁▁▁█●▄▃▁▃·▃ beats 10/24 very hard · avg 9.4% 3.0% at its level
MathArena ArXivMath January 2026 … 67.9% ▂··▄▂▄█▆▆●▄█ beats 17/23 hard · avg 23% 2.0% at its level
MathArena ArXivMath December 2025… 52.2% ▃·▃▃█▁·●·▁▁▁ beats 15/18 hard · avg 19% 1.5% at its level
MathArena BrokenArXiv February 20… 25.8% ▃▂▂▃█▃●·▃··▂ beats 13/16 very hard · avg 0.9% 0.9% sharp · noisy
MATH-Vision 83.0% █·····●▄·▄·▄ beats 2/5 medium · avg 47% 0.8% typical
OTIS mock AIME 2024–2025 96.1% ▃▃▄▃▃▃▇▇█●▂▅ beats 166/197 very hard · avg 7.2% 0.5% sharp · maxed out · noisy
FormalProofBench (200 graduate-le… 13.5% ▃▅·▃··●▃█··▃ beats 5/10 very hard · avg 3.1% 0.5% noisy
vision & documents · #48 of 153 · 4.9% of its score
Beats [MiMo-V2.6-Pro](mimo-v2-6-pro.md), [MiMo-V2.6-Flash (309B-A15B)](mimo-v2-6-flash.md), [GPT-6 Luna](gpt-6-luna.md) here, though they rank higher overall. Trails [Qwen3.6 Flash](qwen3-6-flash.md), [Gemini 3.5 Flash-Lite](gemini-3-5-flash-lite.md), [Kimi K2.5](kimi-k2-5.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena vision (style controlled) 1242.8 ▁▁▂▂▂▂▃▄▅▅●▇ beats 99/152 avg 1061 1.7% blunt
CharXiv (reasoning questions) (ma… 82.1% ▄··▂█··▂···● beats 9/9 hard · avg 32% 1.2% typical
Arena document (style controlled) 1423.2 ▁▁●▃▄▃▇█▅▇▄▅ beats 3/37 avg 1331 0.9% blunt
MATH-Vision 83.0% █·····●▄·▄·▄ beats 2/5 medium · avg 47% 0.8% typical
Diagram-MMU diagram question answ… 81.7% ▄·····▄●·█·█ beats 2/6 medium · avg 56% 0.3% blunt
web search · #56 of 84 · 0.9% of its score
Trails [GPT-4o mini (2024-07-18)](gpt-4o-mini-2024-07-18.md), [Mistral Small 3.1 24B (2503)](mistral-small-3-1.md), [Llama 4 Scout (109B-A17B)](llama-4-scout.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena search systems (style contr… 1161.4 ▁·▁▁▂●▂█▆▃▂▂ beats 7/36 avg 1069 0.9% blunt
other tests (20)
test result vs the others in its run difficulty counts why
Furniture Assembly (Epoch) 38.3% █▆▆●▆▂▆▂▂▂▂▅ beats 15/32 very hard · avg 1.2% 3.8% sharp · at its level
EBR-bench (Epoch) 23.0% ▄▂·▆█▂●▆▄▆·▆ beats 11/25 very hard · avg 0.3% 3.7% sharp
KMMLU-HARD (Daekeun Kim, zero-sho… 74.6% █···▁▁▁▂▃·●▂ beats 18/23 very hard · avg 29% 3.4% sharp · at its level
Mystery Game Puzzles (Epoch) 23.0% █··▂▃▇●█▂▄▂▁ beats 41/77 very hard · avg 9.4% 3.1% sharp · noisy
ARC-AGI-2 public evaluation set (… 27.6% █··▂▅▁▃●▃·▁▃ beats 17/24 very hard · avg 0.3% 2.7% sharp · at its level · noisy
WebCompass (overall) 61.9% ▅·▃▃▃···▃·●█ beats 6/9 hard · avg 18% 2.4% at its level
GPQA Diamond 91.4% ▁▁▁▁▂▄▆▄▅▇●▅ beats 185/211 hard · avg 39% 2.1% sharp
KINA (Knowledge Index of Noah's A… 39.5% ▁▁▂▁▃▄█▆▄●▂▃ beats 33/39 very hard · avg 21% 2.1% at its level
Chess Puzzles (Epoch) 49.0% █▁▁▁▁▂▃▃▃●▁▁ beats 129/137 very hard · avg 5.8% 2.1% sharp · noisy
Medmarks-V weighted mean win rate… 62.4% ▂▃▅▂▆▃█▅█▅▃● beats 32/34 medium · avg 41% 1.8% blunt · at its level
CLIcK Korean (Daekeun Kim) 90.9% ▆▄▄▄█▄▂·▄●·▄ beats 18/23 medium · avg 56% 1.6% typical
EQ-Bench 3 (emotional intelligenc… 1314.3 ▁·▂▁▄▅▅▃█●▁▁ beats 53/62 avg 1034 1.5% typical
ARC-AGI-1 public evaluation set (… 95.0% ▁·▇▃▁·█▇▁·●▃ beats 22/27 very hard · avg 4.0% 1.2% sharp · noisy
HAE-RAE 1.0 (Daekeun Kim) 91.8% ▄▄▄▂▂█▂▄▂●▂▄ beats 16/22 medium · avg 61% 1.2% typical
RPC-Bench overall, text input (Z.… 35.2% ▄··▄▄·▄▄█·●▄ beats 7/8 very hard · avg 12% 1.1% at its level
SimpleQA Verified 37.1% ▁▁▃▂▃▃●▆█▄▂▅ beats 32/81 very hard · avg 8.0% 1.0% at its level
Icelandic LLM Leaderboard average… 47.0% ▁▁▃▂▂▄▁▃●▄▄█ beats 18/38 very hard · avg 14% 0.9% at its level
HLE-Verified full set, text-only … 43.3% █·█·····●··█ beats 2/3 very hard · avg 15% 0.7% at its level
FinSheet-Bench accuracy (501 ques… 80.4% █···█●·····█ beats 2/3 medium · avg 53% 0.6% typical
Vectara hallucination leaderboard… 89.2% ▁▂▁▂▅●▂▃▄▃▁▁ beats 35/98 very easy · avg 90% 0.0% blunt
source results share of the score
Epoch AI 10 41% ▇▇▇▇▇
LMArena leaderboard dataset 5 10% ▇
MathArena ArXivMath / BrokenArXiv / ArX… 4 7% ▇
Daekeun Kim Korean LLM benchmark runs 3 6% ▇
τ²/τ³-bench leaderboard: Sierra's own e… 4 4% ▇
ARC Prize Foundation model_baseline out… 2 4% ▇
WebCompass: Towards Multimodal Web Codi… 1 2% ▇
Terminal-Bench: Benchmarking Agents on … 1 2% ▇
Knowledge Index of Noah's Ark 1 2% ▇
Qwen/Qwen3.5-397B-A17B model card 2 2% ▇
OpenAI vendor-reported benchmark scores… 2 2% ▇
Medmarks: A Comprehensive Open-Source L… 1 2% ▇
ContractBench: Can LLM Agents Preserve … 1 2% ▇
EQ-Bench 3 canonical leaderboard results 1 2% ▇
zai-org/GLM-5 model card 1 1% ▇
GLM-5 Team 1 1% ▇
Vibe Code Bench 1 1% ▇
One Success Isn't Reliability: Thinking… 1 1% ▇
RPC-Bench leaderboard 1 1% ▇
Berkeley Function Calling Leaderboard r… 1 1% ▇
Miðeind Icelandic LLM Leaderboard 1 1% ▇
HLE-Verified: A Systematic Verification… 1 1% ▇
FinSheet-Bench: From Simple Lookups to … 1 1% ▇
FormalProofBench 1 0% ▇
Amazon AGI 1 0% ▇
Diagram-MMU: A Multi-Modal Benchmark fo… 1 0% ▇
Vectara hallucination leaderboard 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 72.4
Licences: Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · MathArena ArXivMath / BrokenArXiv / ArXivLean model-output datasets ([CC BY-SA 4.0](../../licenses/matharena-sharealike-NOTICE.txt)) · Daekeun Kim Korean LLM benchmark runs ([MIT](../../licenses/korean-personal-benchmark-runs-MIT.txt)) · τ²/τ³-bench leaderboard: Sierra's own evaluation runs ([MIT](https://opensource.org/license/mit)) · ARC Prize Foundation model\_baseline outputs on ARC-AGI-1/2 public evaluation sets ([MIT](../../licenses/arcprize-model-baseline-outputs-MIT.txt)) · WebCompass: Towards Multimodal Web Coding Evaluation for Code Language Models ([CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)) · Terminal-Bench: Benchmarking Agents on Hard ([CC BY 4.0](../../licenses/paper-2601.11868v1-CC-BY-4.0.txt)) · Knowledge Index of Noah's Ark ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Qwen/Qwen3.5-397B-A17B model card ([apache-2.0](../../licenses/Qwen--Qwen3.5-397B-A17B.txt)) · Medmarks: A Comprehensive Open-Source LLM Benchmark Suite for Medical Tasks ([CC BY 4.0](../../licenses/paper-2605.01417v1-CC-BY-4.0.txt)) · ContractBench: Can LLM Agents Preserve Observation Contracts? ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · EQ-Bench 3 canonical leaderboard results ([MIT](../../licenses/eqbench3-MIT.txt)) · zai-org/GLM-5 model card ([MIT](../../licenses/zai-org--GLM-5.txt)) · GLM-5 Team ([CC BY 4.0](../../licenses/report-2602.15763v2.txt)) · Vibe Code Bench ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · One Success Isn't Reliability: Thinkingbox ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · RPC-Bench leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Berkeley Function Calling Leaderboard result archive ([Apache-2.0](../../licenses/bfcl-result-archive-v4-Apache-2.0.txt)) · Miðeind Icelandic LLM Leaderboard ([CC BY 4.0](../../licenses/mideind-icelandic-llm-leaderboard-CC-BY-4.0.txt)) · HLE-Verified: A Systematic Verification and Structured Revision of Humanity's Last Exam ([CC BY 4.0](../../licenses/paper-2602.13964v4-CC-BY-4.0.txt)) · FinSheet-Bench: From Simple Lookups to Complex Reasoning ([CC BY 4.0](../../licenses/paper-2603.07316v1-CC-BY-4.0.txt)) · FormalProofBench ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · Amazon AGI ([MIT](../../licenses/tau2-verified-MIT.txt)) · Diagram-MMU: A Multi-Modal Benchmark for Scientific Diagrams ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Vectara hallucination leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0))
## How to run it
### Through an API
api (OpenAI) · $4.81/M blended · $1.75 in / $14 out per 1M tokens · [platform.openai.com](https://platform.openai.com/docs/models)
[price source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/providers/openai/models/gpt-5.2.toml): Standard base token rate from models.dev. Provider-specific tiers, long-context surcharges, caching and promotions may differ. · [context source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/models/openai/gpt-5.2.toml)
[interactive version](https://whatmodel.sh/model/gpt-5-2) · [JSON](https://whatmodel.sh/api?format=json&all=1)