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Model profile
Anthropic

Claude 3 Opus

Claude 3 Opus is a premium text-first model from Anthropic with a heavy runtime profile, unknown context posture, and the clearest fit around agent workflows / long-context research.

Best for: Agent workflows / Long-context researchHeavy latencyUnknown contextPremium pricing
Intelligence
12.5

Benchmark blend

Coding
19.5

Dev workflow signal

Context
N/A

Unknown

Input Price
$15.00

Premium tier

Decision snapshot
27

Claude 3 Opus currently reads as a premium text-first option with unknown context and a heavy runtime profile.

Overall profile
Use-case specific
Best for
Agent workflows / Long-context research
Latency tier
Heavy
Price tier
Premium
Source coverage
OpenRouterArtificial Analysis
Compare this modelSee leaderboard

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
12.5
13

General reasoning and benchmark headroom.

Limited
Speed
N/A
46

Latency data is partial.

Situational
Context
N/A
34

How much prompt and task state can stay in view.

Limited
Price
$15.00
38

$75.00 output / 1M

Expensive

Editorial Profile

Claude 3 Opus 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 20Math score 64

The Claude 3 Opus AI model by Anthropic.

Identity

Anthropic text-first profile

Positioning

Agent workflows / Long-context research with unknown 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.

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
12.5
MMLU Pro
69.6%
GPQA
48.9%
HLE
3.1%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
19.5
LiveCodeBench
0.279
SciCode
23.3%
Math

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

AIME
3.3%
Math 500
64.1%

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
N/A
Vision
Text-first
Price architecture
Input
per 1M input tokens
$15.00
Output
per 1M output tokens
$75.00
Blended
AA 3:1 mix
$30.00

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.