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Claude Sonnet 4.6 (Non-reasoning, Low Effort)

★ featured
Anthropic · released 2026-02-17 · 14 recorded token offers

Top 5 cheapest providers (Adjusted $/task)

Within the active global provider, residency and confidentiality filters.

#ProviderPlatformRaw input $/1MRaw output $/1MAdjusted $/task
1Google Vertex AIGoogle Vertex AI$3.00$15.00
2AnthropicAnthropic$3.00$15.00
3Claude Platform on AWSOpenRouter$3.00$15.00
4AzureOpenRouter$3.00$15.00
5AnthropicOpenRouter$3.00$15.00

Benchmarks

Composite57.3
Mean-imputed base55.2
Dominance adjustment+2.1
Composite evidence1/5
AA Coding · unversioned snapshot 2026-09-10
AA Coding Agent · v1.4 · 2026-09-09
AA Intelligence · unversioned snapshot 2026-09-1023.3
DesignArena Frontend · unversioned snapshot 2026-09-10
DesignArena Full-Stack · unversioned snapshot 2026-09-10
Output speed (t/s)52

MODEL EVIDENCE

Benchmark sheet

10 / 73 registered benchmark versions covered · 11 observations including existing index snapshots.

Compare this model ↗
Composite retains Coding Agent v1.4 · source 2026-09-09

The five Composite inputs remain unchanged. Its Coding Agent input is the median across complete harness results in the retained v1.4 snapshot from 2026-09-09. Artificial Analysis now publishes v1.5, with different components. Explore current v1.5 separately. Dated snapshot labels on other indices identify unversioned source captures, not a verified semantic version.

Profile signals

No threshold-crossing signal · 11 eligible benchmark families

How flags are calculated

Heuristic screen, not statistical significance: benchmark families are correlated and source uncertainty is unknown. Peer evidence needs ≥ 20 independently measured matched configurations from ≥ 10 distinct model families. A flag needs a directed population z-score of magnitude ≥ 1.5 and a gap of ≥ 1.5 from the leave-one-benchmark-family-out mean z in the same direction, over ≥ 5 other benchmark families.

Protocol-compatible measured/vendor divergences

No verified protocol-compatible vendor/measured pair is available for this model; agreement cannot be assessed.

Agentic

Agentic: every observation for this exact model configuration
Benchmark / versionEvaluation groupScore / provenance
Terminal-Bench Hard (AA)

Version 74221fb

Published board

Tests a pinned 44-task hard subset of terminal-based work using Terminus 2.

0.42424 fractionmeasuredobserved 2026-09-10artificialanalysis.ai
Evidence
Axis: Terminal-Bench Hard (AA) · 74221fb · Published board
Exact value: 0.424242424242424 fraction
Observed: 2026-09-10T21:47:16.627Z · publication date: not recorded
Observation id: aa:f2e21112-192e-4aed-ae82-68ca3b38e667:terminalbenchHard

Instruction-following

Instruction-following: every observation for this exact model configuration
Benchmark / versionEvaluation groupScore / provenance
IFBench (AA single-turn)

Version snapshot-2026-09-10

Published board

Tests precise single-turn instructions with deterministic rule checks.

0.42381 fractionmeasuredobserved 2026-09-10artificialanalysis.ai
Evidence
Axis: IFBench (AA single-turn) · snapshot-2026-09-10 · Published board
Exact value: 0.423809523809524 fraction
Observed: 2026-09-10T21:47:16.627Z · publication date: not recorded
Observation id: aa:f2e21112-192e-4aed-ae82-68ca3b38e667:ifbench

Knowledge

Knowledge: every observation for this exact model configuration
Benchmark / versionEvaluation groupScore / provenance
Humanity's Last Exam (AA text-only)

Version snapshot-2026-09-10

Published board

Tests expert-level knowledge on AA's text-only Humanity's Last Exam subset.

0.11214 fractionmeasuredobserved 2026-09-10artificialanalysis.ai
Evidence
Axis: Humanity's Last Exam (AA text-only) · snapshot-2026-09-10 · Published board
Exact value: 0.112140871177016 fraction
Observed: 2026-09-10T21:47:16.627Z · publication date: not recorded
Observation id: aa:f2e21112-192e-4aed-ae82-68ca3b38e667:hle
AA-Omniscience Index

Version snapshot-2026-09-10

Published board

Tests factual reliability while rewarding correct answers and penalizing hallucinations.

-2.1 pointsmeasuredobserved 2026-09-10artificialanalysis.ai
Evidence
Axis: AA-Omniscience Index · snapshot-2026-09-10 · Published board
Exact value: -2.1 points
Observed: 2026-09-10T21:47:16.627Z · publication date: not recorded
Observation id: aa:f2e21112-192e-4aed-ae82-68ca3b38e667:omniscience

Long-context

Long-context: every observation for this exact model configuration
Benchmark / versionEvaluation groupScore / provenance
AA-LCR v1.1

Version 1.1

Published board

Tests reasoning across multiple long documents with corrected answer keys and grading.

0.69333 fractionmeasuredobserved 2026-09-10artificialanalysis.ai
Evidence
Axis: AA-LCR v1.1 · 1.1 · Published board
Exact value: 0.693333333333333 fraction
Observed: 2026-09-10T21:47:16.627Z · publication date: not recorded
Observation id: aa:f2e21112-192e-4aed-ae82-68ca3b38e667:lcr
MLCR-AA

Version snapshot-2026-09-10

Published board

Tests medical-record synthesis and reasoning across long, fragmented case documents.

0.20556 fractionmeasuredobserved 2026-09-10artificialanalysis.ai
Evidence
Axis: MLCR-AA · snapshot-2026-09-10 · Published board
Exact value: 0.205555555555556 fraction
Observed: 2026-09-10T21:47:16.627Z · publication date: not recorded
Observation id: aa:f2e21112-192e-4aed-ae82-68ca3b38e667:mlcrOverall

Reasoning

Reasoning: every observation for this exact model configuration
Benchmark / versionEvaluation groupScore / provenance
AA Intelligence Index

Version snapshot-2026-09-10 (unversioned)

Published board

Published AA index from the retained API snapshot. No verified semantic version was supplied.

23.3 pointsmeasuredobserved 2026-09-10artificialanalysis.ai
Evidence
Axis: AA Intelligence Index · snapshot-2026-09-10 (unversioned) · Published board
Exact value: 23.3 points
Observed: 2026-09-10 · publication date: not recorded
Observation id: legacy:aa_intelligence_index:claude-sonnet-4.6::non-reasoning-low
Protocol: inspect · file data/raw/artificialanalysis.json

Science

Science: every observation for this exact model configuration
Benchmark / versionEvaluation groupScore / provenance
CritPt (AA)

Version snapshot-2026-09-10

Published board

Tests research-level physics reasoning with Python, symbolic and numerical answers.

0.00857 fractionmeasuredobserved 2026-09-10artificialanalysis.ai
Evidence
Axis: CritPt (AA) · snapshot-2026-09-10 · Published board
Exact value: 0.00857142857142857 fraction
Observed: 2026-09-10T21:47:16.627Z · publication date: not recorded
Observation id: aa:f2e21112-192e-4aed-ae82-68ca3b38e667:critpt
GPQA Diamond (AA)

Version snapshot-2026-09-10

Published board

Tests graduate-level biology, physics and chemistry knowledge on the Diamond subset.

0.79697 fractionmeasuredobserved 2026-09-10artificialanalysis.ai
Evidence
Axis: GPQA Diamond (AA) · snapshot-2026-09-10 · Published board
Exact value: 0.796969696969697 fraction
Observed: 2026-09-10T21:47:16.627Z · publication date: not recorded
Observation id: aa:f2e21112-192e-4aed-ae82-68ca3b38e667:gpqa

Tool-use

Tool-use: every observation for this exact model configuration
Benchmark / versionEvaluation groupScore / provenance
τ²-Bench Telecom (AA)

Version snapshot-2026-09-10

Published board

Tests dual-control telecom agents that coordinate tool use with a simulated user.

0.78947 fractionmeasuredobserved 2026-09-10artificialanalysis.ai
Evidence
Axis: τ²-Bench Telecom (AA) · snapshot-2026-09-10 · Published board
Exact value: 0.789473684210526 fraction
Observed: 2026-09-10T21:47:16.627Z · publication date: not recorded
Observation id: aa:f2e21112-192e-4aed-ae82-68ca3b38e667:tau2

Vision

Vision: every observation for this exact model configuration
Benchmark / versionEvaluation groupScore / provenance
MMMU Pro (AA)

Version snapshot-2026-09-10

Published board

Tests multimodal understanding using challenging ten-option questions.

0.69191 fractionmeasuredobserved 2026-09-10artificialanalysis.ai
Evidence
Axis: MMMU Pro (AA) · snapshot-2026-09-10 · Published board
Exact value: 0.691907514450867 fraction
Observed: 2026-09-10T21:47:16.627Z · publication date: not recorded
Observation id: aa:f2e21112-192e-4aed-ae82-68ca3b38e667:mmmuPro
Missing coverage · 66 benchmark versions

No result does not mean a zero, or that the model was never tested. Collection failures and disputed versions retain their distinct status.

Variants / reasoning settings

VariantCoding · snapshot 2026-09-10Intelligence · snapshot 2026-09-10
Claude Sonnet 4.6 (Adaptive Reasoning, Max Effort)63.030.5
Claude Sonnet 4.6 (Non-reasoning, Low Effort)23.3
Claude Sonnet 4.6 (Non-reasoning, High Effort)24.7
Sonnet 4.6 (medium)

GitHub Copilot

Current usage-based billing · model token cost is converted to AI Credits at 1 credit = $0.01.

Input $3.00 / 1M
Cached input $0.300 / 1M
Cache write $3.75 / 1M
Output $15.00 / 1M
Status GA

Legacy annual Pro/Pro+ request billing only.

Multiplier 9.00×
Effective cost $0.360 / request

GitHub marks this multiplier as subject to change.

Token offers by platform — Adjusted $/task

Adjusted costs are modeled USD per task: AA output tokens × OpenRouter usage I/O (Chutes global fallback). These are general usage and benchmark proxies for coding-agent work. Missing AA data assumes 1,000 output tokens/task; unknown cache hit assumes 0%; unmeasured additional cache writes assume 0 tokens. Click any underlined price for exact inputs, dates and assumptions. Raw list prices use the selected fixed input/output blend, in USD per million tokens.

14 offers within the active global filters; “—” means the catalog is active but no public token price is available.

Google Vertex AI (2)

Google Vertex AIglobal$3.00 raw in $/1M$15.00 raw out $/1M
Google Vertex AIeurope-west1EU$3.30 raw in $/1M$16.50 raw out $/1M

Anthropic (1)

Anthropicglobal$3.00 raw in $/1M$15.00 raw out $/1M

OpenRouter (9)

Claude Platform on AWSglobalclaude-on-aws$3.00 raw in $/1M$15.00 raw out $/1M
Azureglobalazure/global$3.00 raw in $/1M$15.00 raw out $/1M
Anthropicglobalanthropic$3.00 raw in $/1M$15.00 raw out $/1M
Amazon Bedrockglobalamazon-bedrock/global$3.00 raw in $/1M$15.00 raw out $/1M
Googleglobalgoogle-vertex/global$3.00 raw in $/1M$15.00 raw out $/1M
Amazon Bedrockglobalamazon-bedrock/us$3.30 raw in $/1M$16.50 raw out $/1M
Amazon Bedrockeuamazon-bedrock/eu-west-1EU$3.30 raw in $/1M$16.50 raw out $/1M
Googleeugoogle-vertex/europeEU$3.30 raw in $/1M$16.50 raw out $/1M
Googleglobalgoogle-vertex/us-east5$3.30 raw in $/1M$16.50 raw out $/1M

AWS Bedrock (1)

AWS Bedrockeu-central-1 (EU cross-region inference profile)EU$3.30 raw in $/1M$16.50 raw out $/1M

T-Systems LLM Hub (1)

T-Systems LLM HubeuEU$3.45 raw in $/1M$17.26 raw out $/1M
Benchmark Heaven — Model benchmarks & costs