Model profile
OpenAI

OpenAI: GPT-5 Mini

OpenAI: GPT-5 Mini 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
31.6

Benchmark blend

Coding
0.692

Dev workflow signal

Context
400K Tokens

Large

Input Price
$0.25

Budget tier

Decision snapshot
61

OpenAI: GPT-5 Mini 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
31.6
32

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
$0.25
86

$2.00 output / 1M

Efficient

Editorial Profile

OpenAI: GPT-5 Mini in one narrative

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

Selective fitCoding score 69Math score 85Vision enabled

GPT-5 Mini is a compact version of GPT-5, designed to handle lighter-weight reasoning tasks. It provides the same instruction-following and safety-tuning benefits as GPT-5, but with reduced latency and cost....

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
  • Coding indicators point to a strong developer workflow fit.

  • 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
  • Code generation, refactors, test writing, and tool-assisted debugging.

  • 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
31.6
MMLU Pro
82.8%
GPQA
80.3%
HLE
15.9%
Coding

Software implementation, debugging quality, and coding benchmark signal.

LiveCodeBench
0.692
SciCode
41.0%
Math

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

Math Index
85.0
AIME 2025
85.0%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
71.2%
TAU2
71.1%
TerminalBench Hard
28.8%
LCR
69.7%

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
Yes
Supported Parameters
include_reasoningmax_completion_tokensmax_tokensreasoningreasoning_effortresponse_formatseedstructured_outputstool_choicetools
Input Modalities
fileimagetext
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.25
Output
per 1M output tokens
$2.00
Blended
AA 3:1 mix
$0.69

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.