Model profile
OpenAI

OpenAI: GPT-5.2

OpenAI: GPT-5.2 is a budget-priced multimodal generalist from OpenAI with heavy runtime profile, large context posture, and the clearest fit around long-context research / multimodal.

Best for: Long-context research / MultimodalHeavy latencyLarge contextBudget pricing
Intelligence
51.3

Benchmark blend

Coding
48.7

Dev workflow signal

Context
400K Tokens

Large

Input Price
$1.75

Budget tier

Decision snapshot
58

OpenAI: GPT-5.2 currently reads as a budget multimodal option with large context and a heavy runtime profile.

Overall profile
Selective fit
Best for
Long-context research / Multimodal
Latency tier
Heavy
Price tier
Budget
Source coverage
OpenRouterArtificial AnalysisVision signal

Decision Strip

Decision rail before the raw tables

Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.

Intelligence
51.3
51

General reasoning and benchmark headroom.

Situational
Speed
76 tok/s
27

TTFT 96.66s

Limited
Context
400K Tokens
88

How much prompt and task state can stay in view.

Above average
Price
$1.75
86

$14.00 output / 1M

Efficient

Editorial Profile

OpenAI: GPT-5.2 in one narrative

Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.

Selective fitCoding score 49Math score 99Vision enabled

GPT-5.2 is the latest frontier-grade model in the GPT-5 series, offering stronger agentic and long context perfomance compared to GPT-5.1. It uses adaptive reasoning to allocate computation dynamically, responding quickly...

Identity

OpenAI multimodal profile

Positioning

Long-context research / Multimodal with large context and heavy runtime.

Cost posture

Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.

Strengths
  • Large context headroom supports repo-wide prompts and long research sessions.

  • Vision-capable routing opens up multimodal review and extraction workflows.

Tradeoffs
  • Budget-friendly input pricing is a strength, but raw capability may vary by workload.

  • Latency profile is better for deliberate runs than rapid back-and-forth chat.

Best fit
  • Image-grounded review, multimodal extraction, and UI audit workflows.

  • Long-context summarization, repo analysis, and policy or document review.

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Context
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Input Price
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Context
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Intelligence
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Context
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Input Price
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Benchmarks

Grouped by job-to-be-done

Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.

General intelligence

Broad reasoning, knowledge depth, and flagship benchmark posture.

Intelligence Index
51.3
MMLU Pro
87.4%
GPQA
90.3%
HLE
35.4%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
48.7
LiveCodeBench
0.889
SciCode
52.1%
Math

Formal reasoning, structured problem solving, and competition-style math.

Math Index
99.0
AIME 2025
99.0%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
75.4%
TAU2
84.8%
TerminalBench Hard
47.0%
LCR
72.7%

Specs & Pricing

Technical snapshot and cost posture

Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.

Technical snapshot
Context Window
400K Tokens
Vision
Enabled
Modalities
file, image, text
Tokenizer
GPT
Max Completion
128000
Moderation
No
Supported Parameters
include_reasoningmax_completion_tokensmax_tokensreasoningresponse_formatseedstructured_outputstool_choicetools
Input Modalities
fileimagetext
Output Modalities
text
Price architecture
Input
per 1M input tokens
$1.75
Output
per 1M output tokens
$14.00
Blended
AA 3:1 mix
$4.81

This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.

OR Cache Read
$0.00

Metadata

Raw source tables at the end

Verification details remain available, but the page no longer forces them ahead of the editorial read.