Claude Haiku vs GPT-3.5-Turbo: price, speed, context window, and code examples in 2026. Which budget LLM API is faster and cheaper for production?
GPT-3.5-Turbo has been the default budget LLM API for two years. Claude Haiku is the challenger. Here's a practical 2026 comparison for production decisions.
| Feature | Claude Haiku 4.5 | GPT-3.5-Turbo (OpenAI) |
|---|---|---|
| Input price | $0.80 / M tokens | $0.50 / M tokens |
| Output price | $4 / M tokens | $1.50 / M tokens |
| Prompt caching | Yes — 90% off cached input ($0.08/M) | No native caching |
| Context window | 200,000 tokens | 16,385 tokens |
| Tool use | Yes | Yes |
| Vision / image | Yes | Yes (gpt-3.5-turbo-vision) |
| Batch API | Yes (50% discount) | Yes (50% discount) |
| Python SDK | pip install anthropic | pip install openai |
A chatbot with a 2,000-token system prompt serving 100,000 requests/month:
| Claude Haiku (with caching) | GPT-3.5-Turbo (no caching) | |
|---|---|---|
| System prompt cost | $0.08 × 2K × 100K / 1M = $16 | $0.50 × 2K × 100K / 1M = $100 |
| Per-message (500 in, 200 out) | $0.80×500K/1M + $4×200K/1M = $1.20 | $0.50×500K/1M + $1.50×200K/1M = $0.55 |
| Monthly total | $17.20 | $100.55 |
Caching turns a 38% token-cost disadvantage into an 83% cost advantage.
import anthropic
client = anthropic.Anthropic()
response = client.messages.create(
model="claude-haiku-4-5",
max_tokens=256,
system=[{
"type": "text",
"text": "You are a sentiment classifier. Reply with one word: POSITIVE, NEGATIVE, or NEUTRAL.",
"cache_control": {"type": "ephemeral"} # cache the system prompt
}],
messages=[
{"role": "user", "content": "I love this product! Best purchase this year."}
]
)
print(response.content[0].text) # → POSITIVE
print(f"Cache hit: {response.usage.cache_read_input_tokens} tokens cached")from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="gpt-3.5-turbo",
max_tokens=256,
messages=[
{"role": "system", "content": "You are a sentiment classifier. Reply with one word: POSITIVE, NEGATIVE, or NEUTRAL."},
{"role": "user", "content": "I love this product! Best purchase this year."}
]
)
print(response.choices[0].message.content) # → POSITIVE
# No caching — full input billed every request| Use case | Pick Claude Haiku | Pick GPT-3.5-Turbo |
|---|---|---|
| Long system prompt reused across calls | ✅ Caching = 90% savings | ❌ No caching |
| Large document processing (>16K tokens) | ✅ 200K context | ❌ Truncation required |
| Short-message chat, no repeat context | ❌ Slightly higher token cost | ✅ Cheaper per-token |
| OpenAI SDK already integrated | ❌ SDK swap needed | ✅ No change |
| High-volume batch processing | ✅ 50% Batch API + caching | ✅ 50% Batch API |
For cost estimation across models and scenarios, see the Claude API Cost Calculator.