anthropic

Anthropic: Claude Sonnet 4.6 (batch)

anthropic/claude-sonnet-4.6:batch

Our take

AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.

OpenRouter lists anthropic/claude-sonnet-4.6:batch as a text-output model accepting text, image, and file inputs, with a one-million-token context window. The catalog presents it as the highest-capability entry in Anthropic's Sonnet class for coding, agentic work, and professional tasks, but that is vendor positioning rather than independently verified performance. It lists configurable reasoning effort, tool use, and structured outputs, plus Design Arena and Artificial Analysis measurements. No independent model card was available in this source pack.[1]

Strengths

Accepts text, image, and file inputs and produces text output.[1]
Supports multiple reasoning effort levels, tool calls, structured outputs, and response-format options.[1]
OpenRouter reports a one-million-token context window.[1]

Limitations

The supplied evidence is catalog-only; no independent model card was available in this source pack.[1]
The :batch label appears in the listing, but its delivery timing, queuing behavior, and operational terms are not defined in the supplied text.[1]
Design Arena and Artificial Analysis figures are measured indicators, not capability declarations, and no evaluation methodology is provided.[1]

Where to use it

May fit iterative coding and navigation of large codebases; validate on the intended workload.[1]
May fit agentic professional workflows that require tools, adjustable reasoning, and structured outputs.[1]
May fit document or image-assisted analysis where text-only output is sufficient, subject to testing.[1]

Sources

[1] OpenRouter catalog · Anthropic: Claude Sonnet 4.6 (batch)Sources checked: Nov 13, 2026

About this model

Description below is the original text supplied by OpenRouter, shown in the source language.

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...

Specifications

Model ID: anthropic/claude-sonnet-4.6:batch
Canonical ID: anthropic/claude-4.6-sonnet-20260217
Creator: anthropic
Added to OpenRouter: 17.02.2026
Context length: 1,000,000
Modality: text+image+file->text
Input modalities: text, image, file
Output modalities: text
Token pricing: USD per 1M tokens: in $1.5 / out $7.5
Knowledge cutoff: —
Expiration date: —

Tasks and capabilities

A grade appears only where the source supplies a matching measurement for that task, the model is active and the cohort holds at least 10 comparable models. The capability column lists facts the source declares — supported formats, reasoning and tool parameters — which are not measured quality.

Task	Grade	Source-declared capability
General quality	No measurement	reasoning: optional · default medium · efforts: max, high, medium, low, none · parameters: include_reasoning, reasoning, reasoning_effort
Coding	AA — next 20% of the cohort	—
Agents	AA — next 20% of the cohort	parameters: structured_outputs, tool_choice, tools
Writing	No measurement	output: text
Reasoning	No measurement	reasoning: optional · default medium · efforts: max, high, medium, low, none · parameters: include_reasoning, reasoning, reasoning_effort
Document analysis	No measurement	input: file
Vision	No measurement	input: image
Image generation	No measurement	—
Audio	No measurement	—
Video	No measurement	—
Embeddings	No measurement	—

Evaluations

Source	Benchmark	Value	Grade	Data retrieved
Artificial Analysis	Coding index	63	AA — next 20% of the cohort	13.11.2026
Artificial Analysis	Agentic index	31.8	AA — next 20% of the cohort	13.11.2026
Design Arena	agents · agenticgamedevwin rate 51.1% · rank 17	1,119Elo	CC — below the B band	13.11.2026
Design Arena	agents · androidnativewin rate 61.8% · rank 17	1,182Elo	BB — next 30% of the cohort	13.11.2026
Design Arena	agents · fullstackwin rate 64.3% · rank 16	1,191Elo	BB — next 30% of the cohort	13.11.2026
Design Arena	agents · godotgamedevwin rate 60.6% · rank 7	1,226Elo	AA — next 20% of the cohort	13.11.2026
Design Arena	agents · mobileappswin rate 63.6% · rank 18	1,206Elo	BB — next 30% of the cohort	13.11.2026
Design Arena	agents · webappswin rate 57% · rank 29	1,172Elo	BB — next 30% of the cohort	13.11.2026
Design Arena	models · 3dwin rate 57.5% · rank 45	1,217Elo	BB — next 30% of the cohort	13.11.2026
Design Arena	models · asciiartwin rate 60.1% · rank 21	1,231Elo	BB — next 30% of the cohort	13.11.2026
Design Arena	models · codecategorieswin rate 59.4% · rank 29	1,267Elo	AA — next 20% of the cohort	13.11.2026
Design Arena	models · datavizwin rate 58.3% · rank 22	1,276Elo	AA — next 20% of the cohort	13.11.2026
Design Arena	models · gamedevwin rate 58.8% · rank 40	1,249Elo	BB — next 30% of the cohort	13.11.2026
Design Arena	models · svgwin rate 58.7% · rank 32	1,188Elo	BB — next 30% of the cohort	13.11.2026
Design Arena	models · uicomponentwin rate 58.3% · rank 36	1,264Elo	AA — next 20% of the cohort	13.11.2026
Design Arena	models · websitewin rate 60% · rank 24	1,279Elo	AA — next 20% of the cohort	13.11.2026

How grades work

Each letter grade compares a model only against other models scored by the same source and metric in the current snapshot. S means the top 10%, A the next 20%, B the next 30%, C the rest. Models without a published measurement are shown as not rated — a supported format alone never implies measured quality.

S — S — top 10% of the cohort
A — A — next 20% of the cohort
B — B — next 30% of the cohort
C — C — below the B band

Providers

Optional live provider statistics from OpenRouter. Missing values are unknown, not zero.

Provider	Context length	Price in /1M	Price out /1M	Latency p50 (30m, s)	Throughput p50 (30m, tok/s)	Uptime (30m)
Anthropic	1,000,000	$1.5	$7.5	—	—	—

Provider data retrieved: 13.11.2026

No published articles identify this model yet.