Pairwise benchmark snapshot · Aug 17, 2026noindex (thin pair)

OpenAI: GPT-5.2 Chat vs OpenAI: o1 benchmark

Name: OpenAI: GPT-5.2 Chat vs OpenAI: o1 AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares OpenAI: GPT-5.2 Chat (OpenAI) and OpenAI: o1 (OpenAI) using the latest snapshots we have as of Aug 17, 2026. OpenAI: GPT-5.2 Chat wins on output price at $14/1M. OpenAI: o1 has the larger context window (200k tokens). Every cell below is a dated snapshot from a named public source — we do not invent missing scores, and we do not run SWE-bench ourselves.

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OpenAI

OpenAI: GPT-5.2 Chat

Auto-discovered from OpenRouter (openai/gpt-5.2-chat). Preview until a second source matches.

$14/1M out

OpenAI

OpenAI: o1

Auto-discovered from OpenRouter (openai/o1). Preview until a second source matches.

Elo 1,512$60/1M out

OpenAI: o1 Inference Compute Profile

Inference-Time Compute & Reasoning EffortDynamic CoT

OpenAI: o1 Reasoning Scaler & Token Billing Simulator

Param: reasoning_effort

Reasoning models scale test-time compute by generating hidden chain-of-thought (CoT) tokens. These tokens are billed at standard output rates and directly expand latency (TTFT) in exchange for higher GPQA Diamond and SWE-bench Verified problem resolve rates.

Estimated TTFT

420 ms

1.0x (Standard eval)

CoT Thinking Tokens

~1,500

Billed as output tokens

Est. Cost / 1k Calls

$115.50

4.5x base invoice

STEM / SWE Boost

+4.5%

GPQA & SWE-bench scaling

Medium (Standard) Operational Profile

Default evaluation baseline balancing reasoning depth with stream rate.

Direct Provider APIOpenAI

"reasoning_effort": "medium"

OpenRouter Unified APIUnified Gateway

"reasoning": {
  "effort": "medium",
  "max_tokens": 1500
}

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Model A

Model B

Quick vs:vs Claude Opus 4.5 vs Claude Opus 4.6 vs Claude Sonnet 4.5 vs Claude 3.7 Sonnet

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

OpenAI: GPT-5.2 Chat (2)vsOpenAI: o1 (8)

OpenAI: GPT-5.2 Chat: 2W (20%)Overall: OpenAI: o1OpenAI: o1: 8W (80%)

← OpenAI: GPT-5.2 ChatOpenAI: o1 →

OpenAI: o1 (5/5)

Intelligence & Reasoning

Preference Elo, Coding proficiency, SWE-bench & LiveBench accuracy

Preference Elo

—vs1,512

Coding Elo

—vs1,485

SWE-bench

—vs48.9%

LiveBench

—vs65.4%

GPQA Diamond

—vs78%

OpenAI: GPT-5.2 Chat: 0WOpenAI: o1: 5W

OpenAI: o1 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs45 tok/s

Time to first token

—vs420 ms

OpenAI: GPT-5.2 Chat: 0WOpenAI: o1: 2W

OpenAI: GPT-5.2 Chat (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$14/1Mvs$60/1M

Input price

$1.75/1Mvs$15/1M

Context window

128kvs200k

OpenAI: GPT-5.2 Chat: 2WOpenAI: o1: 1W

Capability Matchup Radar

Multi-Dimensional Capability Radar

Each spoke is a skill. Farther from the center is better (0–100th percentile). Tap any dot to inspect details.

Tap any node to inspect

Percentile 0–100

Dimensional AdvantageOpenAI: o1 5/6

Preference Elo

No shared data

Throughput Speed

No shared data

Token Price Efficiency

OpenAI: GPT-5.2 Chat

$46.00/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

—

EloHigh is better

1,512

Code Elo

High is better

—

Code EloHigh is better

1,485

LiveBench

High is better

—

LiveBenchHigh is better

65.4%

SWE-bench

High is better

—

SWE-benchHigh is better

48.9%

GPQA

High is better

—

GPQAHigh is better

78%

TTFT

Low is better

—

TTFTLow is better

420 ms

Speed

High is better

—

SpeedHigh is better

45 tok/s

In $

Low is better

+757%$1.75 / 1M

In $Low is better

$15.00 / 1M

Out $

Low is better

+329%$14.00 / 1M

Out $Low is better

$60.00 / 1M

Context

High is better

128k

ContextHigh is better

200k+56%

Interactive Simulator (USD)

Workload Cost & Savings Calculator

Simulate monthly production API costs in USD (US Dollar).

Save up to 81% with OpenAI: GPT-5.2 Chat

Monthly Volume50M tokens/mo

Quick Presets:

Token Ratio (In vs Out)70% In / 30% Out

RAG / Search (10% out)Coding / Chat (50% out)

OpenAI: GPT-5.2 Chat$271.25 / mo

In: $61.25Out: $210

OpenAI: o1$1,425.00 / mo

In: $525Out: $900

Estimated Cost Delta

OpenAI: GPT-5.2 Chat is estimated to save $1,153.75/month ($13,845/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
OpenAI: GPT-5.2 Chat:Pick OpenAI: GPT-5.2 Chat when you are optimizing output cost.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatOpenAI: GPT-5.2 Chat

Lower output list price ($14/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGOpenAI: o1

Larger window (200k).

Screenshots / visionOpenAI: o1

OpenAI: o1 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	insufficient data	Need SWE-bench on both sides.
High-volume chat	OpenAI: GPT-5.2 Chat	Lower output list price ($14/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	OpenAI: o1	Larger window (200k).
Screenshots / vision	OpenAI: o1	OpenAI: o1 is the side marked multimodal in the catalog.

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CompareLLM Matrix

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LIVE

OpenAI

OpenAI: GPT-5.2 Chat

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost$14/1M

OpenAI

OpenAI: o1

Elo 1,512

LiveBench65.4%

SWE-bench48.9%

Speed45 tok/s

Output cost$60/1M

Frequently Asked Questions

Which is better overall, OpenAI: GPT-5.2 Chat or OpenAI: o1?: We do not have preference Elo for both models, so we do not declare an overall winner. Compare the metrics that exist.
Which is better at coding, OpenAI: GPT-5.2 Chat or OpenAI: o1?: SWE-bench is missing for at least one model, so we do not rank coding from a single number.
Which is cheaper to run in production?: OpenAI: GPT-5.2 Chat output tokens are $14/1M versus $60/1M. Input prices and retry rates still move the real bill.
Which is faster, OpenAI: GPT-5.2 Chat or OpenAI: o1?: We do not have TTFT for both models.
Which has the larger context window?: OpenAI: o1 accepts 200k tokens versus 128k.
Can I self-host either model?: Neither model is marked open-weights here. You are comparing hosted APIs.
Are these scores from CompareLLM’s own evals?: No. V1 aggregates public leaderboards (and optional first-party latency pings). Each cell names its source and date. Read /methodology.
What does preference Elo mean on this page?: Preference Elo is a crowd vote from LMArena / Arena. People see two hidden answers and pick the one they like more. The model that wins more often gets a higher Elo. That means people preferred it — not that it passed a school test. It is not SWE-bench, not accuracy, and not a number we invent. OpenAI: GPT-5.2 Chat and OpenAI: o1 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All OpenAI: GPT-5.2 Chat matchups →|All OpenAI: o1 matchups →

OpenAI: o1 vs GPT-4o (previous gpt-flagship)OpenAI: o1 vs GPT-4.5 Orion (next gpt-flagship)GPT-5 vs OpenAI: o1 GPT-5.6 Sol vs OpenAI: o1 GPT-5.6 Terra vs OpenAI: o1 GPT-5 mini vs OpenAI: o1

OpenAI: GPT-5.2 Chat vs OpenAI: o1 benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

OpenAI: GPT-5.2 Chat (2)vsOpenAI: o1 (8)

OpenAI: GPT-5.2 Chat: 2W (20%)Overall: OpenAI: o1OpenAI: o1: 8W (80%)

← OpenAI: GPT-5.2 ChatOpenAI: o1 →

OpenAI: o1 (5/5)

Intelligence & Reasoning

Preference Elo, Coding proficiency, SWE-bench & LiveBench accuracy

Preference Elo

—vs1,512

Coding Elo

—vs1,485

SWE-bench

—vs48.9%

LiveBench

—vs65.4%

GPQA Diamond

—vs78%

OpenAI: GPT-5.2 Chat: 0WOpenAI: o1: 5W

OpenAI: o1 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs45 tok/s

Time to first token

—vs420 ms

OpenAI: GPT-5.2 Chat: 0WOpenAI: o1: 2W

OpenAI: GPT-5.2 Chat (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$14/1Mvs$60/1M

Input price

$1.75/1Mvs$15/1M

Context window

128kvs200k

OpenAI: GPT-5.2 Chat: 2WOpenAI: o1: 1W

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatOpenAI: GPT-5.2 Chat

Lower output list price ($14/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGOpenAI: o1

Larger window (200k).

Screenshots / visionOpenAI: o1

OpenAI: o1 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	insufficient data	Need SWE-bench on both sides.
High-volume chat	OpenAI: GPT-5.2 Chat	Lower output list price ($14/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	OpenAI: o1	Larger window (200k).
Screenshots / vision	OpenAI: o1	OpenAI: o1 is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, OpenAI: GPT-5.2 Chat or OpenAI: o1?

We do not have preference Elo for both models, so we do not declare an overall winner. Compare the metrics that exist.

Which is better at coding, OpenAI: GPT-5.2 Chat or OpenAI: o1?

SWE-bench is missing for at least one model, so we do not rank coding from a single number.

Which is cheaper to run in production?

OpenAI: GPT-5.2 Chat output tokens are $14/1M versus $60/1M. Input prices and retry rates still move the real bill.

Which is faster, OpenAI: GPT-5.2 Chat or OpenAI: o1?

We do not have TTFT for both models.

Which has the larger context window?

OpenAI: o1 accepts 200k tokens versus 128k.

Can I self-host either model?

Neither model is marked open-weights here. You are comparing hosted APIs.

Are these scores from CompareLLM’s own evals?

No. V1 aggregates public leaderboards (and optional first-party latency pings). Each cell names its source and date. Read /methodology.

What does preference Elo mean on this page?

Preference Elo is a crowd vote from LMArena / Arena. People see two hidden answers and pick the one they like more. The model that wins more often gets a higher Elo. That means people preferred it — not that it passed a school test. It is not SWE-bench, not accuracy, and not a number we invent. OpenAI: GPT-5.2 Chat and OpenAI: o1 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.