Model profile
Anthropic

Anthropic: Claude Sonnet 4

Anthropic: Claude Sonnet 4 is a mid-range-priced multimodal generalist from Anthropic with partial runtime data, large context posture, and the clearest fit around long-context research / multimodal.

Best for: Long-context research / MultimodalN/A latencyLarge contextMid-range pricing
Intelligence
N/A

Benchmark blend

Coding
N/A

Dev workflow signal

Context
200K Tokens

Large

Input Price
$3.00

Mid-range tier

Decision snapshot
75

Anthropic: Claude Sonnet 4 currently reads as a mid-range multimodal option with large context and a partially published runtime profile.

Overall profile
Strong all-rounder
Best for
Long-context research / Multimodal
Latency tier
N/A
Price tier
Mid-range
Source coverage
OpenRouterVision signal

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
N/A
N/A

General reasoning and benchmark headroom.

Unavailable
Speed
N/A
N/A

Latency data is partial.

Unavailable
Context
200K Tokens
88

How much prompt and task state can stay in view.

Above average
Price
$3.00
62

$15.00 output / 1M

Competitive

Editorial Profile

Anthropic: Claude Sonnet 4 in one narrative

Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.

Strong all-rounderCoding score N/AMath score N/AVision enabled

Claude Sonnet 4 significantly enhances the capabilities of its predecessor, Sonnet 3.7, excelling in both coding and reasoning tasks with improved precision and controllability. Achieving state-of-the-art performance on SWE-bench (72.7%),...

Identity

Anthropic multimodal profile

Positioning

Long-context research / Multimodal with large context and partially published runtime.

Cost posture

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

Strengths
  • Large context headroom supports repo-wide prompts and long research sessions.

  • Vision-capable routing opens up multimodal review and extraction workflows.

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

  • Latency data is incomplete, so interactive responsiveness is harder to rank confidently.

Best fit
  • Image-grounded review, multimodal extraction, and UI audit workflows.

  • Long-context summarization, repo analysis, and policy or document review.

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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.

No benchmark data is available for this model 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
200K Tokens
Vision
Enabled
Modalities
file, image, text
Tokenizer
Claude
Max Completion
64000
Moderation
Yes
Supported Parameters
include_reasoningmax_tokensreasoningstoptemperaturetool_choicetoolstop_ktop_p
Input Modalities
fileimagetext
Output Modalities
text
Price architecture
Input
per 1M input tokens
$3.00
Output
per 1M output tokens
$15.00
Blended
AA 3:1 mix
N/A

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

OR Web Search Price
$0.0100
OR Cache Read
$0.00
OR Cache Write
$0.00

Metadata

Raw source tables at the end

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