Model profile
Anthropic

Claude Opus 4.5 (Non-reasoning)

Claude Opus 4.5 (Non-reasoning) is a mid-range text-first model from Anthropic with a balanced runtime profile, unknown context posture, and the clearest fit around agent workflows / reasoning.

Best for: Agent workflows / ReasoningBalanced latencyUnknown contextMid-range pricing
Intelligence
43.1

Benchmark blend

Coding
42.9

Dev workflow signal

Context
N/A

Unknown

Input Price
$5.00

Mid-range tier

Decision snapshot
47

Claude Opus 4.5 (Non-reasoning) currently reads as a mid-range text-first option with unknown context and a balanced runtime profile.

Overall profile
Use-case specific
Best for
Agent workflows / Reasoning
Latency tier
Balanced
Price tier
Mid-range
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
43.1
43

General reasoning and benchmark headroom.

Situational
Speed
51 tok/s
59

TTFT 1.04s

Competitive
Context
N/A
34

How much prompt and task state can stay in view.

Limited
Price
$5.00
62

$25.00 output / 1M

Competitive

Editorial Profile

Claude Opus 4.5 (Non-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 43Math score 63

The Claude Opus 4.5 (Non-reasoning) AI model by Anthropic.

Identity

Anthropic text-first profile

Positioning

Agent workflows / Reasoning with unknown context and balanced runtime.

Cost posture

Balanced spend profile. Easier to justify in mixed production and exploration workloads.

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

Tradeoffs
  • Costs look manageable, but still deserve attention in always-on agents or batch jobs.

  • Latency is balanced rather than ultra-fast, which is fine for most workflows but not the snappiest tier.

  • 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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Intelligence
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Context
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Input Price
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Intelligence
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Context
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Input Price
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Claude 4.5 Sonnet (Reasoning)

Intelligence
43.0
Context
N/A
Input Price
$3.00

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
43.1
MMLU Pro
88.9%
GPQA
81.0%
HLE
12.9%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
42.9
LiveCodeBench
0.738
SciCode
47.0%
Math

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

Math Index
62.7
AIME 2025
62.7%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
43.0%
TAU2
86.3%
TerminalBench Hard
40.9%
LCR
65.3%

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
$5.00
Output
per 1M output tokens
$25.00
Blended
AA 3:1 mix
$10.00

This model sits in a balanced spend range. It is easier to justify across both production and exploratory workflows.

Metadata

Raw source tables at the end

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