Claude 3 Family: Haiku, Sonnet and Opus Compared

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Tres esferas luminosas de diferentes tamaños representando modelos en cascada de capacidades

Anthropic launched the Claude 3 family on March 4, 2024: three models — Haiku, Sonnet, Opus — on the same date, each with different cost/performance trade-off. A month in, adoption is clear: Claude 3 Opus competes head-to-head with GPT-4 Turbo on many benchmarks, and Haiku is one of the cheapest models with decent quality. This article covers differences by tier, when to pick each, and family positioning vs OpenAI.

The Three Tiers

Haiku — fastest and cheapest:

  • Price: $0.25 / 1M input tokens, $1.25 / 1M output.
  • Context: 200k tokens.
  • Latency: ~400ms first token.
  • Use: classification, simple extraction, fast chat.

Sonnet — balanced:

  • Price: $3 / 1M input, $15 / 1M output.
  • Context: 200k tokens.
  • Quality: near GPT-4 on many tasks.
  • Use: enterprise RAG, agents, document analysis.

Opus — most capable:

  • Price: $15 / 1M input, $75 / 1M output.
  • Context: 200k tokens.
  • Quality: competitive with GPT-4 Turbo.
  • Use: complex reasoning, research, advanced coding.

All three have 200k context — no context trade-off by price as before.

Published Benchmarks

Key:

Benchmark Opus Sonnet Haiku GPT-4 Turbo
MMLU 86.8 79.0 75.2 86.4
GSM8K (math) 95.0 92.3 88.9 92.0
HumanEval (code) 84.9 73.0 75.9 85.4
HellaSwag 95.4 89.0 85.9 95.3

Opus is in GPT-4 Turbo’s class. Sonnet closes at ~GPT-3.5+ quality but at lower price than GPT-4. Haiku is the surprise: very competitive for its price.

When to Pick Each

Haiku fits:

  • Mass text classification.
  • Simple structured extraction (JSON output).
  • Tier-1 support chat.
  • Content moderation.
  • Short-document summaries.

Price 12x lower than Sonnet makes it feasible for high volume.

Sonnet fits:

  • Typical enterprise RAG.
  • Agents with tools.
  • Long-document analysis.
  • Decent creative-content generation.
  • Translations.

It’s the default workhorse.

Opus fits:

  • Complex multi-step reasoning.
  • Research and synthesis.
  • Hard coding challenges.
  • Legal/medical analysis.
  • Any case where a cheaper-model error costs.

Price justifies only when extra quality matters much.

200k Context: The Differentiator

Both — Anthropic (Claude 3) and Google (Gemini 1.5 Pro 1M) — make long context accessible. OpenAI stays at 128k for GPT-4 Turbo.

Cases where 200k suffices (without needing Gemini 1.5):

  • Books of ~150 pages or ~75k words.
  • Mid-size codebases.
  • Hour-long audio transcripts.
  • Extensive technical reports.

For millions of tokens, Gemini 1.5 Pro still leads. For hundreds of thousands, Claude 3 is competitive.

API and Access

  • Anthropic API direct.
  • Amazon Bedrock offers Claude 3.
  • Google Cloud Vertex AI too.
  • Azure no (OpenAI exclusive on Azure).

SDK support: official Python, Node, Go. Native OpenAI-style compat lacking, but libraries like LiteLLM unify.

Multimodal: Integrated Vision

All three Claude 3 accept images in prompts:

import anthropic
client = anthropic.Anthropic()

response = client.messages.create(
    model="claude-3-opus-20240229",
    max_tokens=1024,
    messages=[{
        "role": "user",
        "content": [
            {"type": "image", "source": {"type": "base64", "media_type": "image/png", "data": img_data}},
            {"type": "text", "text": "What's in this image?"}
        ]
    }]
)

Claude 3 Opus vision is very strong at:

  • Document OCR.
  • Chart description.
  • Technical diagram analysis.
  • Data extraction from invoices/forms.

No image generation (left to DALL-E, Midjourney, Stable Diffusion).

Vs GPT-4 Turbo: Practical Differences

After months of parallel use:

  • Factual accuracy: Opus and GPT-4 Turbo tie.
  • Instruction following: Claude 3 usually better on complex instructions.
  • Reasoning: Opus excellent, GPT-4 Turbo excellent, call it a tie.
  • Coding: similar.
  • Refusal rate: Claude historically more cautious, Claude 3 less rigid.
  • Tone: Claude tends to be more verbose by default.

No universal winner. Test with your cases.

Function Calling

Claude 3 has tool use (function calling). Different from OpenAI’s syntax but covers same cases. Uses XML tags internally:

<function_calls>
<invoke name="get_weather">
<parameter name="location">Madrid</parameter>
</invoke>
</function_calls>

SDKs abstract this. For complex agents, both (OpenAI and Anthropic) are viable.

Limitations

Honestly:

  • Aggressive rate limits at start of Pro plan.
  • Data residency: US by default (Bedrock has regional options).
  • No fine-tuning: Anthropic doesn’t offer custom fine-tune (only via enterprise contracts).
  • Limited free tier: for experimentation, you pay from first request above tier.

Multi-Tier Usage Strategy

Productive pattern:

  1. Classify intent with Haiku (cheap).
  2. Process simple case with Sonnet (default).
  3. Escalate to Opus only if Sonnet fails or requires high precision.

This “tiered routing” with LiteLLM or custom logic minimises total cost without sacrificing quality where it matters.

Conclusion

The Claude 3 family closes the gap Anthropic had vs OpenAI in top-tier capability. Opus is a real option for frontier tasks. Sonnet is the pragmatic default. Haiku opens high-volume cases with decent quality. 200k context across all three is a concrete advantage vs GPT-4 Turbo. The Anthropic-vs-OpenAI choice is no longer “who has the best” but “which fits your case and provider preference”. Available on Bedrock and Vertex expands options for European compliance. Worth having both in your strategy, not just one.

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