This model is deprecated. We only continue performance benchmarking for the default 10k input token workload. Results for other workloads are historical and no longer updated.

Anthropic has launched a newer release, Claude Sonnet 5.5. We suggest considering it instead.

Anthropic

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Claude Sonnet 5

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Proprietary model

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Released June 2026

Claude Sonnet 5 (Low) Intelligence, Performance & Price Analysis

Name: Intelligence
Creator: Artificial Analysis
License: https://artificialanalysis.ai/docs/legal/Terms-of-Use.pdf

Try it out API Provider Benchmarks

Cost

In $2.00Out $10.00Cache Discount 90%

$0.51

Cost per Intelligence Index task

Verbosity

25M

Output tokens from Intelligence Index

Claude Sonnet 5 (Low) is below average in intelligence and somewhat expensive when comparing to other models of similar price. It's also slower than average, however highly concise. The model supports text and image input, outputs text, and has a 1M tokens context window.

Claude Sonnet 5 (Low) scores 24 on the Artificial Analysis Intelligence Index, placing it below average among comparable models (median: 26). When evaluating the Intelligence Index, it generated 25M tokens, which is very concise in comparison to the median of 82M.

Pricing for Claude Sonnet 5 (Low) is $2.00 per 1M input tokens (moderately priced, median: $2.00) and $10.00 per 1M output tokens (moderately priced, median: $10.00). On average, it costs $0.51 per task to evaluate Claude Sonnet 5 (Low) on the Intelligence Index.

At 61 tokens per second, Claude Sonnet 5 (Low) is slower than average (79).

Reasoning	Yes This page shows the reasoning version of this model. A non-reasoning variant may also exist.
Input modality	Supports: text and image
Output modality	Supports: text
Context window	1M ~1500 A4 pages of size 12 Arial font

Metrics are compared against models of the same class:

Non-reasoning models → compared only with other non-reasoning models
Reasoning models → compared across both reasoning and non-reasoning
Open weights models → compared only with other open weights models of the same size class:

Tiny: ≤4B parameters
Small: 4B–40B parameters
Medium: 40B–150B parameters
Large: >150B parameters

Proprietary models → compared across proprietary and open weights models of the same price range, using a blended 3:1 input/output price ratio:

<$0.15 per 1M tokens
$0.15–$1 per 1M tokens
>$1 per 1M tokens

Intelligence

Artificial Analysis Intelligence Index · Higher is better

Not publicly available

Speed

Output tokens per second · Higher is better

Not publicly available

Cost per Task

Weighted average cost (USD) per Intelligence Index task · Lower is better

Not publicly available

Intelligence

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.3.2 incorporates 10 evaluations: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1

Not publicly available

Artificial Analysis Intelligence Index v4.3.2 includes: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Not publicly available

Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if commercial use is limited by conditions, and as 'Non-commercial' if the license prohibits commercial use.

Capability Indexes

Measures the performance of models on specific capabilities and industries

Artificial Analysis Finance & Accounting Index

Incorporates 7 evaluations: AA-Omniscience, GDPval-AA v2.1, AA-Briefcase v1.1, Humanity's Last Exam, AutomationBench-AA, AA-LCR v1.1, GDP.pdf · Higher is better

Not publicly available

Benchmarks

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better

Not publicly available

AA-Briefcase v1.1Updated

Agentic knowledge work, (Elo-500)/2000

GDPval-AA v2.1Updated

Agentic real-world work tasks, (Elo-500)/2000

AutomationBench-AA

Agentic SaaS workflows

Terminal-Bench 4.0

Agentic coding & terminal use

SciCodeUnder review

Coding

Humanity's Last Exam

Reasoning & knowledge

GDP.pdf

Professional document reasoning, All-pass

CritPtUnder review

Physics reasoning

AA-Omniscience Accuracy

Knowledge

AA-Omniscience Non-Hallucination Rate

1 - hallucination rate

AA-LCR v1.1

Long context reasoning

Harvey LAB-AA v1.1Updated

Legal agentic work, Hallucination-Gated All-Pass Rate

EnterpriseOps-Gym-AA

Agentic business operations

Terminal-Bench-Science 0.1New

Agentic scientific research workflows in a terminal

AA-AnalystAgent

Quantitative analysis on spreadsheets & documents

ITBench-AA

Kubernetes incident root-cause analysis

MMMU-Pro

Visual reasoning

MLCR-AA

Medical long context reasoning

While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.

AA-Briefcase v1.1Updated

AA-Briefcase Elo

AA-Briefcase v1.1 is an agentic knowledge work benchmark developed by Artificial Analysis. AA-Briefcase Elo is a combined metric that aggregates rubric pass rate, analytical quality Elo and presentation Elo · Higher is better

Not publicly available

AA-Briefcase Elo is a combined metric that aggregates analytical quality Elo, presentation Elo, and rubric pass rate, with rubric performance converted into Elo via synthetic head-to-head matches. Elo and 95% confidence interval bounds are clamped at 0.

AA-Omniscience

AA-Omniscience Index

AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.

Not publicly available

Intelligence Index Comparisons

Intelligence Index vs. Cost per Intelligence Index Task

Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task

Most attractive quadrant

Pareto line

Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.

Token Use

Output Tokens per Intelligence Index Task

Weighted average number of output tokens used to run one task in the Artificial Analysis Intelligence Index

Not publicly available

The number of tokens required per Intelligence Index task. This is calculated by multiplying the output tokens per eval by the relative weights of each benchmark in the Intelligence Index, then dividing by task count (excluding repeats).

Cost

Cost per Intelligence Index Task

Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better

Not publicly available

Cost to Run Artificial Analysis Intelligence Index

Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index

Not publicly available

The cost to run the evaluations in the Artificial Analysis Intelligence Index, calculated using the model's input, cache hit, cache write, reasoning, and answer token prices and the number of tokens used across evaluations (excluding repeats).

Pricing: Cache Hit, Input, and Output

Price (USD per M Tokens)

Not publicly available

Price per token for cached prompts (previously processed), typically offering a significant discount compared to regular input price, represented as USD per million tokens. The values shown here are the cache hit price; cache write and cache storage are billed separately and vary by provider — see "Cache pricing by provider" for detail.

Context Window

Context window: tokens limit · Higher is better

Not publicly available

Larger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data.

Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).

Speed

Measured by Output Speed (tokens per second)

Output Speed

Output tokens per second · Higher is better

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

Figures represent performance of the model's first-party API or the median across providers where a first-party API is not available.

Time per Intelligence Index Task

Weighted average decode time (minutes) per task; excludes TTFT and overhead time · Lower is better

The weighted average time (seconds) per Artificial Analysis Intelligence Index task. This is calculated by dividing output tokens per task by output speed, weighted by the relative weights of each benchmark in the Intelligence Index.

Latency

Measured by Time (seconds) to First Token

Latency: Time To First Answer Token

Seconds to first answer token received · Accounts for reasoning model 'thinking' time

Time to first answer token received, in seconds, after API request sent. For reasoning models, this includes the 'thinking' time of the model before providing an answer. For models which do not support streaming, this represents time to receive the completion.

End-to-End Response Time

Seconds to output 500 tokens, calculated based on time to first token, 'thinking' time for reasoning models, and output speed

End-to-End Response Time

Seconds to output 500 tokens, including reasoning model 'thinking' time · Lower is better

Seconds to receive a 500 token response. Key components:

Input time: Time to receive the first response token
Thinking time (only for reasoning models): Time reasoning models spend outputting tokens to reason prior to providing an answer. Amount of tokens based on the average reasoning tokens across a diverse set of 60 prompts (methodology details).
Answer time: Time to generate 500 output tokens, based on output speed

Figures represent performance of the model's first-party API or the median across providers where a first-party API is not available.

Frequently Asked Questions

Common questions about Claude Sonnet 5 (Low)

Claude Sonnet 5 (Low) was released on June 30, 2026.

Claude Sonnet 5 (Low) was created by Anthropic.

Claude Sonnet 5 (Low) scores 24 on the Artificial Analysis Intelligence Index, placing it below average among other reasoning models in a similar price tier (median: 26).

Claude Sonnet 5 (Low) generates output at 61.3 tokens per second (based on Anthropic's API), which is below average compared to other reasoning models in a similar price tier (median: 79.3 t/s).

Claude Sonnet 5 (Low) has a time to first token (TTFT) of 1.01s (based on Anthropic's API), which is very competitive compared to other reasoning models in a similar price tier (median: 3.99s).

Claude Sonnet 5 (Low) costs $2.00 per 1M input tokens (better than average, median: $2.00) and $10.00 per 1M output tokens (better than average, median: $10.00), based on Anthropic's API.

Claude Sonnet 5 (Low) costs $2.00 per 1M input tokens and $10.00 per 1M output tokens (based on Anthropic's API). For a blended rate (7:2:1 cache hit/input/output ratio), this is $1.54 per 1M tokens. Pricing may vary by provider. Compare provider pricing

When evaluated on the Intelligence Index, Claude Sonnet 5 (Low) generated 25M output tokens, which is very competitive compared to other reasoning models in a similar price tier (median: 82M).

Yes, Claude Sonnet 5 (Low) is a reasoning model. It uses extended thinking or chain-of-thought reasoning to work through complex problems before providing an answer.

Claude Sonnet 5 (Low) supports text and image input.

Claude Sonnet 5 (Low) supports text output.

Yes, Claude Sonnet 5 (Low) supports image input and can analyze, describe, and answer questions about images.

Yes, Claude Sonnet 5 (Low) is multimodal. It can process text and image input and generate text output.

Claude Sonnet 5 (Low) has a context window of 1.0M tokens. This determines how much text and conversation history the model can process in a single request.

No, Claude Sonnet 5 (Low) is proprietary. The model weights are not publicly available.

Claude Sonnet 5 (Low) is a proprietary model and Anthropic has not disclosed the model size or parameter count.

Claude Sonnet 5 (Low) achieves a score of 24 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.

Yes, Claude Sonnet 5 (Low) is available via API through 1 provider. Compare API providers

Claude Sonnet 5 (Low) is available through 1 API provider. Compare providers

Claude Sonnet 5 (Low) Intelligence, Performance & Price Analysis

Model summary

Comparison Summary

Technical specifications

227 models in this class

Intelligence

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Artificial Analysis Intelligence Index

Open Weights

Intelligence Evaluations

Intelligence Evaluation Relevance

Artificial Analysis Intelligence Index

AA-Briefcase v1.1Updated

AA-Briefcase Elo

AA-Briefcase Elo

AA-Omniscience

AA-Omniscience Index

AA-Omniscience Index

Intelligence Index Comparisons

Intelligence Index vs. Cost per Intelligence Index Task

Cost per Intelligence Index Task

Artificial Analysis Intelligence Index

Token Use

Output Tokens per Intelligence Index Task

Output Tokens per Intelligence Index Task

Cost

Cost per Intelligence Index Task

Cost per Intelligence Index Task

Cost to Run Artificial Analysis Intelligence Index

Cost to Run Artificial Analysis Intelligence Index

Pricing: Cache Hit, Input, and Output

Cache Hit

Context Window

Context Window

Context Window for RAG

Context Window

Speed

Output Speed

Output Speed

Model Performance Representation

Time per Intelligence Index Task

Time per Intelligence Index Task

Latency

Latency: Time To First Answer Token

Time to First Answer Token

End-to-End Response Time

End-to-End Response Time

End-to-End Response Time

Model Performance Representation

Frequently Asked Questions

When was Claude Sonnet 5 (Low) released?

Who created Claude Sonnet 5 (Low)?

How intelligent is Claude Sonnet 5 (Low)?

How fast is Claude Sonnet 5 (Low)?

What is the latency of Claude Sonnet 5 (Low)?

How much does Claude Sonnet 5 (Low) cost?

What is Claude Sonnet 5 (Low) API pricing?

How verbose is Claude Sonnet 5 (Low)?

Is Claude Sonnet 5 (Low) a reasoning model?

What input modalities does Claude Sonnet 5 (Low) support?

What output modalities does Claude Sonnet 5 (Low) support?

Can Claude Sonnet 5 (Low) process images?

Is Claude Sonnet 5 (Low) multimodal?

What is the context window of Claude Sonnet 5 (Low)?

Is Claude Sonnet 5 (Low) open source?

How many parameters does Claude Sonnet 5 (Low) have?

How does Claude Sonnet 5 (Low) perform on benchmarks?

Is Claude Sonnet 5 (Low) available via API?

Where can I use Claude Sonnet 5 (Low)?