Model profile
openai

OpenAI: GPT-4

OpenAI: GPT-4 is a premium text-first model from openai with a heavy runtime profile, compact context posture, and the clearest fit around agent workflows / multimodal.

Best for: Agent workflows / MultimodalHeavy latencyCompact contextPremium pricing
Intelligence
12.8

Benchmark blend

Coding
13.1

Dev workflow signal

Context
8K Tokens

Compact

Input Price
$30.00

Premium tier

Decision snapshot
23

OpenAI: GPT-4 currently reads as a premium text-first option with compact context and a heavy runtime profile.

Overall profile
Use-case specific
Best for
Agent workflows / Multimodal
Latency tier
Heavy
Price tier
Premium
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
12.8
13

General reasoning and benchmark headroom.

Limited
Speed
28 tok/s
51

TTFT 0.98s

Situational
Context
8K Tokens
28

How much prompt and task state can stay in view.

Limited
Price
$30.00
18

$60.00 output / 1M

Premium

Editorial Profile

OpenAI: GPT-4 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 13Math score 36

OpenAI's flagship model, GPT-4 is a large-scale multimodal language model capable of solving difficult problems with greater accuracy than previous models due to its broader general knowledge and advanced reasoning capabilities. Training data: up to Sep 2021.

Identity

openai text-first profile

Positioning

Agent workflows / Multimodal with compact context and heavy runtime.

Cost posture

Premium spend profile. Best when the upside justifies tighter budget control.

Strengths
  • The available source data suggests a balanced profile rather than one dominant edge.

Tradeoffs
  • Pricing sits in premium territory, so bulk usage needs tighter cost controls.

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

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

  • Context window is more comfortable for focused tasks than extremely long sessions.

Best fit
  • Focused chat, retrieval-augmented flows, and narrower production tasks.

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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
12.8

Extra benchmark cuts are not available for this category yet.

Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
13.1

Extra benchmark cuts are not available for this category yet.

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
8K Tokens
Vision
Text-first
Modalities
text->text, text
Tokenizer
GPT
Max Completion
4096
Moderation
Yes
Supported Parameters
frequency_penaltylogit_biaslogprobsmax_tokenspresence_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_logprobstop_p
Input Modalities
text
Output Modalities
text
Price architecture
Input
per 1M input tokens
$30.00
Output
per 1M output tokens
$60.00
Blended
AA 3:1 mix
$37.50

This model trades into premium territory. It makes sense when capability upside matters more than raw volume efficiency.

Metadata

Raw source tables at the end

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