Model profile
anthropic
New in 2026

Anthropic: Claude Sonnet 4.6

Anthropic: Claude Sonnet 4.6 is a mid-range multimodal generalist from anthropic with a balanced runtime profile, large context posture, and the clearest fit around long-context research / multimodal.

Best for: Long-context research / MultimodalBalanced latencyLarge contextMid-range pricing
Intelligence
44.4

Benchmark blend

Coding
46.4

Dev workflow signal

Context
1000K Tokens

Large

Input Price
$3.00

Mid-range tier

Decision snapshot
59

Anthropic: Claude Sonnet 4.6 currently reads as a mid-range multimodal option with large context and a balanced runtime profile.

Overall profile
Selective fit
Best for
Long-context research / Multimodal
Latency tier
Balanced
Price tier
Mid-range
Source coverage
OpenRouterArtificial AnalysisVision 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
44.4
44

General reasoning and benchmark headroom.

Situational
Speed
53 tok/s
57

TTFT 1.19s

Situational
Context
1000K Tokens
100

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 4.6 in one narrative

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

Selective fitCoding score 46Math score 36Vision enabled

Sonnet 4.6 is Anthropic's most capable Sonnet-class model yet, with frontier performance across coding, agents, and professional work. It excels at iterative development, complex codebase navigation, end-to-end project management with memory, polished document creation, and confident computer use for web QA and workflow automation.

Identity

anthropic multimodal profile

Positioning

Long-context research / Multimodal with large context and balanced 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 is balanced rather than ultra-fast, which is fine for most workflows but not the snappiest tier.

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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Context
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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
44.4
GPQA
79.9%
HLE
13.2%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
46.4
SciCode
46.9%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
41.2%
TAU2
79.5%
TerminalBench Hard
46.2%
LCR
57.7%

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
1000K Tokens
Vision
Enabled
Modalities
text, image->text, image
Tokenizer
Claude
Max Completion
128000
Moderation
Yes
Supported Parameters
include_reasoningmax_tokensreasoningresponse_formatstopstructured_outputstemperaturetool_choicetoolstop_ktop_pverbosity
Input Modalities
textimage
Output Modalities
text
Price architecture
Input
per 1M input tokens
$3.00
Output
per 1M output tokens
$15.00
Blended
AA 3:1 mix
$6.00

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.