Model profile
anthropic
New in 2026

Anthropic: Claude Opus 4.6

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

Best for: Long-context research / MultimodalHeavy latencyLarge contextMid-range pricing
Intelligence
46.5

Benchmark blend

Coding
47.6

Dev workflow signal

Context
1000K Tokens

Large

Input Price
$5.00

Mid-range tier

Decision snapshot
58

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

Overall profile
Selective fit
Best for
Long-context research / Multimodal
Latency tier
Heavy
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
46.5
47

General reasoning and benchmark headroom.

Situational
Speed
48 tok/s
49

TTFT 1.68s

Situational
Context
1000K Tokens
100

How much prompt and task state can stay in view.

Above average
Price
$5.00
62

$25.00 output / 1M

Competitive

Editorial Profile

Anthropic: Claude Opus 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 48Math score 36Vision enabled

Opus 4.6 is Anthropic’s strongest model for coding and long-running professional tasks. It is built for agents that operate across entire workflows rather than single prompts, making it especially effective for large codebases, complex refactors, and multi-step debugging that unfolds over time. The model shows deeper contextual understanding, stronger problem decomposition, and greater reliability on hard engineering tasks than prior generations. Beyond coding, Opus 4.6 excels at sustained knowledge work. It produces near-production-ready documents, plans, and analyses in a single pass, and maintains coherence across very long outputs and extended sessions. This makes it a strong default for tasks that require persistence, judgment, and follow-through, such as technical design, migration planning, and end-to-end project execution. For users upgrading from earlier Opus versions, see our [official migration guide here](https://openrouter.ai/docs/guides/guides/model-migrations/claude-4-6-opus)

Identity

anthropic multimodal profile

Positioning

Long-context research / Multimodal with large context and heavy 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 profile is better for deliberate runs than rapid back-and-forth chat.

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.

General intelligence

Broad reasoning, knowledge depth, and flagship benchmark posture.

Intelligence Index
46.5
GPQA
84.0%
HLE
18.6%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
47.6
SciCode
45.7%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
44.6%
TAU2
84.8%
TerminalBench Hard
48.5%
LCR
58.3%

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
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
$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.

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.