Anthropic's fast, low-latency Haiku model that matches Claude 3 Opus on many benchmarks at a fraction of the cost.
Modality:
Text
model ID
anthropic/claude-3-5-haiku-20241022
Output Speed *
N/A
tok/s
Intelligence Index *
N/A
/ 100
Context Window *
200000
tokens
Input price
4.8
Anytoken
Output price
24
Anytoken
Claude 3.5 Haiku: Fast, Low-Latency Inference That Punches Above Its Tier
Claude 3.5 Haiku (2024-10-22) is Anthropic's fast, cost-efficient model in the Claude 3.5 family, positioned below the upgraded 3.5 Sonnet. It was designed to keep the previous Haiku generation's speed while delivering substantially higher intelligence — Anthropic reports it matches Claude 3 Opus, their prior flagship, on many evaluations. With a 200K-token context window, low time-to-first-token, improved instruction following, and more accurate tool use, it fits user-facing products, specialized sub-agent tasks, and high-volume data-driven workloads where responsiveness and unit economics matter more than frontier reasoning.
Start building with Claude 3.5 Haiku through the AnyAPI.ai API.
Performance
Where Claude 3.5 Haiku Earns Its Place: Speed With Unexpected Coding Strength
Claude 3.5 Haiku's defining trait is delivering meaningful intelligence at Haiku-class speed. Anthropic reports it scores 40.6% on SWE-bench Verified — a coding result that outpaced the original Claude 3.5 Sonnet and GPT-4o at launch, and matches Claude 3 Opus on many evaluations despite being the smallest model in its generation. For developers this means the fast tier is no longer purely a lightweight fallback: it can handle real coding suggestions, data extraction, and tool-driven sub-agent tasks in production. The consequence is a practical middle ground between cheap lightweight models and expensive frontier systems.
Benchmarks
Independent Measurements: Latency, Throughput and Coding
Independent testing from Artificial Analysis measured Claude 3.5 Haiku at roughly 65 output tokens per second with a time to first token near 0.80 seconds — low latency but throughput below the average for its class. On coding, Anthropic's reported 40.6% on SWE-bench Verified places it well above the lightweight tier, though clearly beneath the upgraded 3.5 Sonnet (49.0%). Third-party comparisons also note higher verbosity than Sonnet and weaker performance on knowledge-heavy tasks like GPQA Diamond. Treat these as measurements under specific conditions, not official specifications.
Output Speed
*
N/A
tok/s
Intelligence Index
*
N/A
/ 100
MMLU *
Broad world knowledge and problem-solving
0
%
GPQA *
PhD-level scientific reasoning across physics, biology, chemistry.
0
%
HLE *
Adherence to multi-step structured instructions.
0
%
LiveCodeBench *
Tool-calling reliability in long agentic loops.
0
%
Technical Specifications
What the model supports
Claude 3.5 Haiku provides a 200,000-token context window with a maximum of 8,192 output tokens per response — a large intake budget but a modest generation ceiling. It launched text-only; Anthropic added image input on 25 February 2025, so the 2024-10-22 model now accepts text and images and outputs text. It supports tool calling, prompt caching, and batch processing, but has no extended-thinking mode. The knowledge cutoff is July 2024. The 8,192-token output cap is the specification with the greatest production impact: long-form generation must be chunked.
Verified Specifications —
Claude 3.5 Haiku (2024-10-22)
*
Input modalities
Text
Context window
200000
tokens
Maximum output tokens
0
Reasoning
No
Knowledge cutoff
October 2024
Pricing (standard)
4.8
AnyTokens in
/
24
AnyTokens out
Quickstart
import requests
url = "https://api.anyapi.ai/v1/chat/completions"
payload = {
"stream": False,
"tool_choice": "auto",
"logprobs": False,
"model": "Model_Name",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}
headers = {
"Authorization": "Bearer AnyAPI_API_KEY",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.json())import requests url = "https://api.anyapi.ai/v1/chat/completions" payload = { "stream": False, "tool_choice": "auto", "logprobs": False, "model": "Model_Name", "messages": [ { "role": "user", "content": "Hello" } ]
}
headers = { "Authorization": "Bearer AnyAPI_API_KEY", "Content-Type": "application/json"
} response = requests.post(url, json=payload, headers=headers) print(response.json())
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Copy
Code is copied
const url = 'https://api.anyapi.ai/v1/chat/completions';
const options = {
method: 'POST',
headers: {Authorization: 'Bearer AnyAPI_API_KEY', 'Content-Type': 'application/json'},
body: '{"stream":false,"tool_choice":"auto","logprobs":false,"model":"Model_Name","messages":[{"role":"user","content":"Hello"}]}'
};
try {
const response = await fetch(url, options);
const data = await response.json();
console.log(data);
} catch (error) {
console.error(error);
}const url = 'https://api.anyapi.ai/v1/chat/completions';
const options = { method: 'POST', headers: {Authorization: 'Bearer AnyAPI_API_KEY', 'Content-Type': 'application/json'}, body: '{"stream":false,"tool_choice":"auto","logprobs":false,"model":"Model_Name","messages":[{"role":"user","content":"Hello"}]}'
}; try { const response = await fetch(url, options); const data = await response.json(); console.log(data);
} catch (error) { console.error(error);
}
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Copy
Code is copied
curl --request POST \
--url https://api.anyapi.ai/v1/chat/completions \
--header 'Authorization: Bearer AnyAPI_API_KEY' \
--header 'Content-Type: application/json' \
--data '{
"stream": false,
"tool_choice": "auto",
"logprobs": false,
"model": "Model_Name",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}'curl --request POST \ --url https://api.anyapi.ai/v1/chat/completions \ --header 'Authorization: Bearer AnyAPI_API_KEY' \ --header 'Content-Type: application/json' \ --data '{ "stream": false, "tool_choice": "auto", "logprobs": false, "model": "Model_Name", "messages": [ { "role": "user", "content": "Hello" } ] }'
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Code is copied
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Limitations & Trade-offs
1
8,192-token output cap. Claude 3.5 Haiku can process a 200K-token context but generate at most 8,192 tokens per response. Long documents, full reports, or large code files must be split across multiple calls with continuation logic. If your workload depends on single-pass long-form generation, a model with a higher output ceiling is a better fit.
2
No reasoning mode. Claude 3.5 Haiku is a non-reasoning model that produces direct responses without extended chain-of-thought. For multi-step math, complex planning, or hard logical problems, its intelligence trails reasoning models and the upgraded 3.5 Sonnet — independent tests show a notable gap on knowledge-heavy benchmarks like GPQA Diamond. Route genuinely hard reasoning tasks to a stronger model.
3
Pricing rose above the previous Haiku tier. Anthropic raised the price relative to Claude 3 Haiku to reflect its higher intelligence, so it is no longer the cheapest option in the family. For simple, ultra-high-volume classification or extraction where the extra capability isn't needed, cheaper lightweight models — including Claude 3 Haiku — deliver better unit economics.
4
Superseded by newer models. This 2024-10-22 model has been effectively replaced in Anthropic's lineup by later Haiku generations, and some third-party benchmark providers now mark it deprecated and no longer track new providers. It remains available via API, but teams starting fresh should weigh whether a newer Haiku offers better performance for the same role.
Best-Fit Workloads
Where this model earns its place
01
User-facing product features
Anthropic explicitly positions Claude 3.5 Haiku for user-facing products thanks to its low time-to-first-token and improved instruction following. Chat assistants, in-app helpers, and interactive suggestions feel responsive because generation begins quickly. The 8,192-token output ceiling is rarely a constraint for conversational turns, making this a strong default for latency-sensitive interfaces.
02
Specialized sub-agents and tool use
With more accurate tool calling and fast responses, Claude 3.5 Haiku suits specialized sub-agent roles inside larger agentic systems — routing, retrieval, function invocation, or narrow decision steps. Its coding competence (40.6% SWE-bench Verified) means sub-agents can also handle light code manipulation. Reserve orchestration of genuinely complex reasoning for a stronger model and use Haiku for the high-frequency worker steps.
03
High-volume data extraction and classification
Anthropic recommends Claude 3.5 Haiku for data extraction, labeling, and content moderation. It can generate personalized experiences from large volumes of records — purchase history, pricing, or inventory data — where speed and consistent instruction following matter across many requests. Prompt caching further improves economics when the same context is reused, though for the simplest tasks a cheaper model may suffice.
04
Real-time coding assistance
Its combination of fast inference and unexpectedly strong coding makes Claude 3.5 Haiku viable for inline code suggestions and quick refactors where responsiveness is critical. It outperformed several larger models at launch on SWE-bench Verified. For deep, multi-file agentic coding or complex architecture reasoning, the upgraded 3.5 Sonnet or a reasoning model remains the better choice.
Integration
01
One API integration
02
Easy model switching
03
Flexible for production
04