Model profile
Anthropic
New in 2026

Claude Opus 5 (Adaptive Reasoning, Medium Effort)

Claude Opus 5 (Adaptive Reasoning, Medium Effort) is a mid-range-priced text-first model from Anthropic with heavy runtime profile, partial context coverage, and the clearest fit around agent workflows / coding.

Best for: Agent workflows / CodingHeavy latencyN/A contextMid-range pricing
Intelligence
58.6

Benchmark blend

Coding
74.3

Dev workflow signal

Context
N/A

N/A

Input Price
$5.00

Mid-range tier

Decision snapshot
54

Claude Opus 5 (Adaptive Reasoning, Medium Effort) currently reads as a mid-range text-first option with partially published context and a heavy runtime profile.

Overall profile
Use-case specific
Best for
Agent workflows / Coding
Latency tier
Heavy
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
58.6
59

General reasoning and benchmark headroom.

Competitive
Speed
51 tok/s
18

TTFT 6.91s

Limited
Context
N/A
N/A

How much prompt and task state can stay in view.

Unavailable
Price
$5.00
62

$25.00 output / 1M

Competitive

Editorial Profile

Claude Opus 5 (Adaptive Reasoning, Medium Effort) 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 74Math score N/A

The Claude Opus 5 (Adaptive Reasoning, Medium Effort) AI model by Anthropic.

Identity

Anthropic text-first profile

Positioning

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

Cost posture

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

Strengths
  • Coding indicators point to a strong developer workflow fit.

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

  • 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 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
58.6
GPQA
91.9%
HLE
51.3%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
74.3
SciCode
50.7%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

LCR
78.7%

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
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.