Model profile
Anthropic

Claude 4 Opus (Reasoning)

Claude 4 Opus (Reasoning) is a premium-priced text-first model from Anthropic with partial runtime data, partial context coverage, and the clearest fit around coding / agent workflows.

Best for: Coding / Agent workflowsN/A latencyN/A contextPremium pricing
Intelligence
31.7

Benchmark blend

Coding
0.636

Dev workflow signal

Context
N/A

N/A

Input Price
$15.00

Premium tier

Decision snapshot
41

Claude 4 Opus (Reasoning) currently reads as a premium text-first option with partially published context and a partially published runtime profile.

Overall profile
Use-case specific
Best for
Coding / Agent workflows
Latency tier
N/A
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
31.7
32

General reasoning and benchmark headroom.

Limited
Speed
N/A
N/A

Latency data is partial.

Unavailable
Context
N/A
N/A

How much prompt and task state can stay in view.

Unavailable
Price
$15.00
38

$75.00 output / 1M

Expensive

Editorial Profile

Claude 4 Opus (Reasoning) 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 64Math score 73

The Claude 4 Opus (Reasoning) AI model by Anthropic.

Identity

Anthropic text-first profile

Positioning

Coding / Agent workflows with partially published context and partially published 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 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.

  • Context limits are only partially published, so long-session planning needs extra validation.

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

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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
31.7
MMLU Pro
87.3%
GPQA
79.6%
HLE
12.3%
Coding

Software implementation, debugging quality, and coding benchmark signal.

LiveCodeBench
0.636
SciCode
39.8%
Math

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

Math Index
73.3
AIME
75.7%
AIME 2025
73.3%
Math 500
98.2%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
53.7%
TAU2
73.4%
TerminalBench Hard
31.1%
LCR
36.3%

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.