Model profile
OpenAI

OpenAI: GPT-5.2-Codex

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

Best for: Long-context research / MultimodalN/A latencyLarge contextBudget pricing
Intelligence
41.2

Benchmark blend

Coding
N/A

Dev workflow signal

Context
400K Tokens

Large

Input Price
$1.75

Budget tier

Decision snapshot
63

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

Overall profile
Selective fit
Best for
Long-context research / Multimodal
Latency tier
N/A
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
41.2
41

General reasoning and benchmark headroom.

Limited
Speed
N/A
N/A

Latency data is partial.

Unavailable
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-Codex in one narrative

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

Selective fitCoding score N/AMath score N/AVision enabled

GPT-5.2-Codex is an upgraded version of GPT-5.1-Codex optimized for software engineering and coding workflows. It is designed for both interactive development sessions and long, independent execution of complex engineering tasks....

Identity

OpenAI multimodal profile

Positioning

Long-context research / Multimodal with large context and partially published 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 data is incomplete, so interactive responsiveness is harder to rank confidently.

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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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
41.2
GPQA
89.9%
HLE
35.7%
Coding

Software implementation, debugging quality, and coding benchmark signal.

SciCode
54.6%

Extra benchmark cuts are not available for this category yet.

Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
77.6%
TAU2
92.1%
TerminalBench Hard
37.1%
LCR
79.3%

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
image, text
Tokenizer
GPT
Max Completion
128000
Moderation
No
Supported Parameters
include_reasoningmax_completion_tokensreasoningreasoning_effortresponse_formatseedstructured_outputstool_choicetools
Input Modalities
imagetext
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 Web Search Price
$0.0100
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.