Intelligence
34 scoreAnthropic · Flagships Analysis
Claude Sonnet 4.6 (Non-reasoning, Low Effort)
Intelligence, Performance & Price Analysis
Canonical slug: claude-sonnet-4-6-non-reasoning-low-effort · Canonical model registry at build time
Speed
46.9 output tokens/secLatency
1.21s TTFTInput Price
$3.00 / 1M tokensOutput Price
$15.00 / 1M tokensExecutive Assessment
Routing Verdict & Tradeoffs
Ethen Routing Verdict
Frontier research / complex reasoning
Claude Sonnet 4.6 (Non-reasoning, Low Effort) scores 34 (estimated) on the Artificial Analysis Intelligence Index, placing it well above average among other non-reasoning models in a similar price tier (median: 17). Claude Sonnet 4.6 (Non-reasoning, Low Effort) generates output at 46.9 tokens per second (based on Anthropic's API), which is at the lower end compared to other non-reasoning models in a similar price tier (median: 64.2 t/s). Claude Sonnet 4.6 (Non-reasoning, Low Effort) costs $3.00 per 1M input tokens (somewhat higher than average, median: $1.88) and $15.00 per 1M output tokens (somewhat higher than average, median: $7.75), based on Anthropic's API.
Reserve this model for the hardest requests; simpler or repetitive work should stay on cheaper routes.
Task Fit Assessment
| Workload | Rating | Notes |
|---|---|---|
| Complex reasoning & agentic workflows | optimal | Intelligence score 34 places this model among top performers. Suitable for multi-step analysis and agentic loops. |
| High-volume chat & customer-facing | viable | Output speed may be a bottleneck for real-time chat at scale. |
| Latency-sensitive applications | optimal | TTFT 1.21s — adequate latency for most interactive use cases. |
| Cost-sensitive pipelines | viable | Output pricing at $15.00 is premium. Route high-volume simple tasks to cheaper alternatives. |
Cost Pressure Analysis
High
Pricing is premium — input $3.00, output $15.00. This model is expensive for high-volume or output-heavy workloads.
Technical Specifications
Architecture and Limits
| Specification | Value | Validation Authority |
|---|---|---|
| Model type | Proprietary | inferred |
| Reasoning | No | faq |
| Input modalities | Claude Sonnet 4.6 (Non-reasoning, Low Effort) supports text and image input. | faq |
| Output modalities | Claude Sonnet 4.6 (Non-reasoning, Low Effort) supports text output. | faq |
| Context window | Claude Sonnet 4.6 (Non-reasoning, Low Effort) has a context window of 1.0M tokens. This determines how much text and conversation history the model can process in a single request. | faq |
| Open weights / source | No, Claude Sonnet 4.6 (Non-reasoning, Low Effort) is proprietary. The model weights are not publicly available. | faq |
| Parameters | Claude Sonnet 4.6 (Non-reasoning, Low Effort) is a proprietary model and Anthropic has not disclosed the model size or parameter count. | faq |
| API availability | Yes, Claude Sonnet 4.6 (Non-reasoning, Low Effort) is available via API through 1 provider. | faq |
Evidence charts
Profile visualizations
Charts are the same canonical evidence cards previously published for this slug, contained inside the D18D page grammar.
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. · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Artificial Analysis Intelligence Index by Open Weights / Proprietary
Artificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Artificial Analysis Intelligence Index
Artificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Intelligence
Artificial Analysis Intelligence Index · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Output Speed
Output tokens per second · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Speed should be read together with latency and cost; fast output does not always mean better user-perceived performance.
Speed
Output tokens per second · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Speed should be read together with latency and cost; fast output does not always mean better user-perceived performance.
End-to-End Response Time
Seconds to output 500 tokens, including reasoning model 'thinking' time · Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Latency: Time To First Answer Token
Seconds to first answer token received · Accounts for reasoning model 'thinking' time · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Latency includes thinking / reasoning time when applicable. Lower is better for interactive use cases.
Context Window
Context window: tokens limit · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Larger context windows enable complex multi-turn tasks but may increase latency and cost for long inputs.
Cost per Intelligence Index Task
Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Cost per Task
Weighted average cost (USD) per Intelligence Index task · Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Cost to Run Artificial Analysis Intelligence Index
Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Pricing: Cache Hit, Input, and Output
Price (USD per M Tokens) · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Time per Intelligence Index Task
Weighted average decode time (minutes) per task; excludes TTFT and overhead time · Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Methodology
Methodology & Provenance
This page is rendered from the normalized profile and page JSON for Claude Sonnet 4.6 (Non-reasoning, Low Effort).
Benchmark values are preserved as normalized; only layout, disclosure ordering, and typography are adjusted for readability.
Frequently Asked Questions
Model FAQs & Technical Disclosures
Claude Sonnet 4.6 (Non-reasoning, Low Effort) was released on February 17, 2026.
Claude Sonnet 4.6 (Non-reasoning, Low Effort) was created by Anthropic.
Claude Sonnet 4.6 (Non-reasoning, Low Effort) scores 34 (estimated) on the Artificial Analysis Intelligence Index, placing it well above average among other non-reasoning models in a similar price tier (median: 17).
Claude Sonnet 4.6 (Non-reasoning, Low Effort) generates output at 46.9 tokens per second (based on Anthropic's API), which is at the lower end compared to other non-reasoning models in a similar price tier (median: 64.2 t/s).
Claude Sonnet 4.6 (Non-reasoning, Low Effort) has a time to first token (TTFT) of 1.21s (based on Anthropic's API), which is better than average compared to other non-reasoning models in a similar price tier (median: 1.56s).
Claude Sonnet 4.6 (Non-reasoning, Low Effort) costs $3.00 per 1M input tokens (somewhat higher than average, median: $1.88) and $15.00 per 1M output tokens (somewhat higher than average, median: $7.75), based on Anthropic's API.
Claude Sonnet 4.6 (Non-reasoning, Low Effort) costs $3.00 per 1M input tokens and $15.00 per 1M output tokens (based on Anthropic's API). For a blended rate (7:2:1 cache hit/input/output ratio), this is $2.31 per 1M tokens. Pricing may vary by provider.
No, Claude Sonnet 4.6 (Non-reasoning, Low Effort) is not a reasoning model. It provides direct responses without extended chain-of-thought reasoning.
Claude Sonnet 4.6 (Non-reasoning, Low Effort) supports text and image input.
Claude Sonnet 4.6 (Non-reasoning, Low Effort) supports text output.
Yes, Claude Sonnet 4.6 (Non-reasoning, Low Effort) supports image input and can analyze, describe, and answer questions about images.
Yes, Claude Sonnet 4.6 (Non-reasoning, Low Effort) is multimodal. It can process text and image input and generate text output.
Claude Sonnet 4.6 (Non-reasoning, Low Effort) 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 4.6 (Non-reasoning, Low Effort) is proprietary. The model weights are not publicly available.
Claude Sonnet 4.6 (Non-reasoning, Low Effort) is a proprietary model and Anthropic has not disclosed the model size or parameter count.
Claude Sonnet 4.6 (Non-reasoning, Low Effort) achieves a score of 34 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.
Yes, Claude Sonnet 4.6 (Non-reasoning, Low Effort) is available via API through 1 provider.
Claude Sonnet 4.6 (Non-reasoning, Low Effort) is available through 1 API provider.