Model profile
OpenAI

OpenAI: GPT-5 Mini

OpenAI: GPT-5 Mini is a budget-priced multimodal generalist from OpenAI with balanced runtime profile, large context posture, and the clearest fit around long-context research / multimodal.

Best for: Long-context research / MultimodalBalanced latencyLarge contextBudget pricing
Intelligence
20.7

Benchmark blend

Coding
21.9

Dev workflow signal

Context
400K Tokens

Large

Input Price
$0.25

Budget tier

Decision snapshot
51

OpenAI: GPT-5 Mini currently reads as a budget multimodal option with large context and a balanced runtime profile.

Overall profile
Use-case specific
Best for
Long-context research / Multimodal
Latency tier
Balanced
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
20.7
21

General reasoning and benchmark headroom.

Limited
Speed
76 tok/s
69

TTFT 0.93s

Competitive
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.

Use-case specificCoding score 22Math score 47Vision 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 balanced 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.

  • Latency and throughput look responsive enough for interactive loops.

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

  • Latency is balanced rather than ultra-fast, which is fine for most workflows but not the snappiest tier.

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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Intelligence
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Context
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Input Price
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Intelligence
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Context
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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
20.7
MMLU Pro
77.5%
GPQA
68.7%
HLE
5.0%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
21.9
LiveCodeBench
0.545
SciCode
36.9%
Math

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

Math Index
46.7
AIME 2025
46.7%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
45.6%
TAU2
31.9%
TerminalBench Hard
14.4%
LCR
35.7%

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_tokensreasoningresponse_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.