Intelligence
12 scoreAnthropic · Flagships Analysis
Claude 3.5 Haiku
Intelligence, Performance & Price Analysis
Canonical slug: claude-3-5-haiku · Canonical model registry at build time
Input Price
$0.80 / 1M tokensOutput Price
$4.00 / 1M tokensExecutive Assessment
Routing Verdict & Tradeoffs
Ethen Routing Verdict
Capable everyday model
Claude 3.5 Haiku scores 12 on the Artificial Analysis Intelligence Index, placing it below average among other non-reasoning models in a similar price tier (median: 17). Claude 3.5 Haiku costs $0.80 per 1M input tokens (very competitive, median: $1.88) and $4.00 per 1M output tokens (better than average, median: $7.75), based on the median across providers serving the model.
Good for routine tasks; route complex reasoning and premium workloads to stronger models.
Task Fit Assessment
| Workload | Rating | Notes |
|---|---|---|
| Complex reasoning & agentic workflows | viable | Intelligence score 12 handles routine reasoning but may struggle with open-ended agentic tasks. |
| Cost-sensitive pipelines | viable | Output pricing at $4.00 is premium. Route high-volume simple tasks to cheaper alternatives. |
Cost Pressure Analysis
Medium
Pricing is moderate — input $0.80, output $4.00. Costs accumulate at volume but are manageable for valuable tasks.
Technical Specifications
Architecture and Limits
| Specification | Value | Validation Authority |
|---|---|---|
| Model type | Proprietary | inferred |
| Reasoning | No | faq |
| Input modalities | Claude 3.5 Haiku supports image input. | faq |
| Output modalities | Claude 3.5 Haiku supports text only output. | faq |
| Context window | 200k tokens | faq |
| Open weights / source | No, Claude 3.5 Haiku is proprietary. The model weights are not publicly available. | faq |
| Parameters | Claude 3.5 Haiku is a proprietary model and Anthropic has not disclosed the model size or parameter count. | faq |
| Knowledge cutoff | Claude 3.5 Haiku has a knowledge cutoff of July 2024. The model's training data includes information up to this date. | faq |
| API availability | Yes, Claude 3.5 Haiku is available via API through 2 providers. | 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.
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.
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.
Methodology
Methodology & Provenance
This page is rendered from the normalized profile and page JSON for Claude 3.5 Haiku.
Benchmark values are preserved as normalized; only layout, disclosure ordering, and typography are adjusted for readability.
Frequently Asked Questions
Model FAQs & Technical Disclosures
Claude 3.5 Haiku was released on October 22, 2024.
Claude 3.5 Haiku was created by Anthropic.
Claude 3.5 Haiku scores 12 on the Artificial Analysis Intelligence Index, placing it below average among other non-reasoning models in a similar price tier (median: 17).
Claude 3.5 Haiku costs $0.80 per 1M input tokens (very competitive, median: $1.88) and $4.00 per 1M output tokens (better than average, median: $7.75), based on the median across providers serving the model.
Claude 3.5 Haiku costs $0.80 per 1M input tokens and $4.00 per 1M output tokens (based on the median across providers serving the model). For a blended rate (7:2:1 cache hit/input/output ratio), this is $0.62 per 1M tokens. Pricing may vary by provider.
No, Claude 3.5 Haiku is not a reasoning model. It provides direct responses without extended chain-of-thought reasoning.
Claude 3.5 Haiku supports image input.
Claude 3.5 Haiku supports text only output.
Yes, Claude 3.5 Haiku supports image input and can analyze, describe, and answer questions about images.
No, Claude 3.5 Haiku is not multimodal. It only supports image input.
Claude 3.5 Haiku has a context window of 200k tokens. This determines how much text and conversation history the model can process in a single request.
No, Claude 3.5 Haiku is proprietary. The model weights are not publicly available.
Claude 3.5 Haiku is a proprietary model and Anthropic has not disclosed the model size or parameter count.
Claude 3.5 Haiku achieves a score of 12 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.
Claude 3.5 Haiku has a knowledge cutoff of July 2024. The model's training data includes information up to this date.
Yes, Claude 3.5 Haiku is available via API through 2 providers.
Claude 3.5 Haiku is available through 2 API providers.