Claude 3.5 Haiku vs GPT-3.5 Turbo — API Cost Calculator

Predict your real monthly bill. Toggle batch API and prompt caching to see how discounts and cache hits change the math for your exact workload. Pricing verified against official provider pages — May 2026.

Cost Calculator

🟠 Claude 3.5 Haiku Anthropic
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per month
⬛ GPT-3.5 Turbo OpenAI
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per month

Pricing snapshot (as of May 2026)

The table below shows per-1M-token rates sourced from the official Anthropic and OpenAI pricing pages, last verified on 21 May 2026. All figures are in USD.

Rate type Claude 3.5 Haiku GPT-3.5 Turbo
Input (standard) $0.80 $0.50
Output (standard) $4.00 $1.50
Input (batch) $0.4000 $0.2500
Output (batch) $2.0000 $0.7500
Cache write $1.0000 —
Cache read $0.0800 —
Context window 200K 16K

Sources: https://www.anthropic.com/pricing#api · https://platform.openai.com/docs/pricing

When Claude 3.5 Haiku is the better pick

Claude 3.5 Haiku is the stronger choice for agentic coding pipelines, long-context reasoning, and applications where prompt caching delivers outsized ROI. On SWE-bench Verified, Claude 3.5 Sonnet resolved over 49% of real-world GitHub issues — a benchmark lead that translates to real productivity gains in Cursor, Cline, and custom code-agent frameworks. Claude's 200K context window means you can pass entire codebases or legal documents without chunking. Most compellingly, the cache read rate of $0.08–$0.30 per million tokens makes large reusable system prompts dramatically cheaper than on any competing model: a 50K-token knowledge base system prompt reused 10,000 times per day costs roughly $150 vs. $1,500 without caching — a 90% reduction from a single optimisation.

When GPT-3.5 Turbo is the better pick

GPT-3.5 Turbo is the most cost-effective model in the OpenAI lineup, making it the go-to choice for high-volume, latency-sensitive workloads where quality requirements are moderate — think autocomplete suggestions, short-form content classification, FAQ answering over a narrow knowledge base, and lightweight chatbots. Its batch pricing (50% off) and 16K context window make it viable for nightly ETL pipelines and bulk document tagging at a fraction of the cost of GPT-4-class models.

Real-world example: 1M requests/month at 2K input + 500 output tokens

Assume a production workload of 1 million API calls per month, each consuming 2,000 input tokens and generating 500 output tokens. This is a realistic profile for a mid-size SaaS product with active users across time zones — a customer-support bot, a document-analysis pipeline, or an AI-assisted search feature.

Scenario A — Standard pricing, no optimisations:

At this volume and token mix, GPT-3.5 Turbo is 51% cheaper than the alternative on standard rates — a difference of $1,850.00/month. Over a full year that compounds to $22,200.00 in savings, which is meaningful even before factoring in batch or caching optimisations.

Scenario B — Batch API enabled (50% off, where supported):

  • Claude 3.5 Haiku batch: $1,800.00/month (saving $1,800.00 vs. standard)
  • GPT-3.5 Turbo batch: $875.00/month (saving $875.00 vs. standard)

The batch API is well-suited for nightly analytics pipelines, content moderation queues, data-labelling jobs, and any workload that can tolerate asynchronous processing with up to 24-hour turnaround. It is incompatible with real-time interactive use cases such as customer-facing chat or streaming completions.

Use the interactive calculator above to model your specific token mix, request volume, and caching strategy. Real production costs typically run 10–30% above median estimates due to prompt variability, retry logic, and usage spikes.

Migration considerations

Switching between Claude 3.5 Haiku and GPT-3.5 Turbo is not always a drop-in model swap. Differences in API shape, prompt conventions, tokeniser behaviour, and context-window limits can require non-trivial engineering work. Here is what to audit before migrating production traffic.

Frequently asked questions

Which is cheaper at 1M requests/month — Claude 3.5 Haiku or GPT-3.5 Turbo? +

At 1M requests/month with 2,000 input tokens and 500 output tokens per request, GPT-3.5 Turbo costs $1,750.00 versus Claude 3.5 Haiku at $3,600.00 — a difference of $1,850.00 per month (51%). Enabling the batch API (where available) cuts those figures by 50% for workloads that tolerate up to 24-hour turnaround.

Does Claude 3.5 Haiku or GPT-3.5 Turbo support batch API pricing? +

Both Claude 3.5 Haiku and GPT-3.5 Turbo support batch API pricing at 50% off standard rates, in exchange for up to 24-hour result latency. Claude 3.5 Haiku batch input is $0.4/1M and batch output is $2.00/1M. GPT-3.5 Turbo batch input is $0.25/1M and batch output is $0.75/1M. Batch is well-suited for nightly analytics, content moderation queues, embedding generation, and any workload that can tolerate asynchronous processing.

How does prompt caching compare between Claude 3.5 Haiku and GPT-3.5 Turbo? +

Claude 3.5 Haiku supports prompt caching with cache reads at $0.08/1M and cache writes at $1.00/1M. GPT-3.5 Turbo does not currently offer explicit prompt caching. Prompt caching delivers the largest savings when you have a large, stable system prompt reused across thousands of requests per day — a 50,000-token knowledge-base system prompt reused 10,000 times can cut input costs by 80–90%.

Which model has lower latency — Claude 3.5 Haiku or GPT-3.5 Turbo? +

Latency depends on region, time of day, request size, and infrastructure routing — not just model architecture. In general, smaller models (both models in this comparison) tend to return the first token faster because they require fewer compute cycles per forward pass. For latency-critical production workloads, benchmark with your own representative prompt and output length distribution using p50/p95/p99 metrics rather than synthetic averages. Provider infrastructure also varies: OpenAI has more global edge regions via Azure, while Google Vertex AI and Anthropic offer fewer but growing geographic options.

Can I trust this calculator for production budgeting? +

This calculator uses pricing verified against the official provider pricing pages as of May 2026. It is suitable for planning and estimating monthly spend. For production budgets, always cross-check against your provider dashboard, account for any committed-use discounts or enterprise pricing you have negotiated, and add a 10–20% buffer for unexpected usage spikes. Token counts in the calculator are per-request estimates — actual production variance (longer user queries, retry logic, error recovery) can push real costs 15–30% above median estimates.