Model profile
OpenAI

OpenAI: o3 Mini

OpenAI: o3 Mini is a budget-priced text-first model from OpenAI with partial runtime data, large context posture, and the clearest fit around long-context research / coding.

Best for: Long-context research / CodingN/A latencyLarge contextBudget pricing
Intelligence
19.2

Benchmark blend

Coding
0.717

Dev workflow signal

Context
200K Tokens

Large

Input Price
$1.10

Budget tier

Decision snapshot
56

OpenAI: o3 Mini currently reads as a budget text-first option with large context and a partially published runtime profile.

Overall profile
Selective fit
Best for
Long-context research / Coding
Latency tier
N/A
Price tier
Budget
Source coverage
OpenRouterArtificial Analysis

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
19.2
19

General reasoning and benchmark headroom.

Limited
Speed
N/A
N/A

Latency data is partial.

Unavailable
Context
200K Tokens
88

How much prompt and task state can stay in view.

Above average
Price
$1.10
86

$4.40 output / 1M

Efficient

Editorial Profile

OpenAI: o3 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 72Math score 97

OpenAI o3-mini is a cost-efficient language model optimized for STEM reasoning tasks, particularly excelling in science, mathematics, and coding. This model supports the `reasoning_effort` parameter, which can be set to...

Identity

OpenAI text-first profile

Positioning

Long-context research / Coding 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.

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.

  • Current metadata points to a text-first profile rather than a broad multimodal one.

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

  • 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
19.2
MMLU Pro
79.1%
GPQA
74.8%
HLE
7.9%
Coding

Software implementation, debugging quality, and coding benchmark signal.

LiveCodeBench
0.717
SciCode
39.9%
Math

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

AIME
77.0%
Math 500
97.3%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

TAU2
28.7%
TerminalBench Hard
6.8%

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
200K Tokens
Vision
Text-first
Modalities
file, text
Tokenizer
GPT
Max Completion
100000
Moderation
Yes
Supported Parameters
include_reasoningmax_tokensreasoningresponse_formatseedstructured_outputstool_choicetools
Input Modalities
filetext
Output Modalities
text
Price architecture
Input
per 1M input tokens
$1.10
Output
per 1M output tokens
$4.40
Blended
AA 3:1 mix
$1.93

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.