Model profile
openai

OpenAI: GPT-3.5 Turbo

OpenAI: GPT-3.5 Turbo is a budget text-first model from openai with a fast runtime profile, compact context posture, and the clearest fit around agent workflows / multimodal.

Best for: Agent workflows / MultimodalFast latencyCompact contextBudget pricing
Intelligence
9.0

Benchmark blend

Coding
10.7

Dev workflow signal

Context
16K Tokens

Compact

Input Price
$0.50

Budget tier

Decision snapshot
39

OpenAI: GPT-3.5 Turbo currently reads as a budget text-first option with compact context and a fast runtime profile.

Overall profile
Use-case specific
Best for
Agent workflows / Multimodal
Latency tier
Fast
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
9.0
9

General reasoning and benchmark headroom.

Limited
Speed
108 tok/s
88

TTFT 0.41s

Above average
Context
16K Tokens
28

How much prompt and task state can stay in view.

Limited
Price
$0.50
86

$1.50 output / 1M

Efficient

Editorial Profile

OpenAI: GPT-3.5 Turbo 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 11Math score 44

GPT-3.5 Turbo is OpenAI's fastest model. It can understand and generate natural language or code, and is optimized for chat and traditional completion tasks. Training data up to Sep 2021.

Identity

openai text-first profile

Positioning

Agent workflows / Multimodal with compact context and fast runtime.

Cost posture

Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.

Strengths
  • Latency and throughput look responsive enough for interactive loops.

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

  • 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
9.0
MMLU Pro
46.2%
GPQA
29.7%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
10.7

Extra benchmark cuts are not available for this category yet.

Math

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

Math 500
44.1%

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
16K 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
$0.50
Output
per 1M output tokens
$1.50
Blended
AA 3:1 mix
$0.75

This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.

Metadata

Raw source tables at the end

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