Claude Haiku 4.5 vs GPT-5.4 mini
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.
Anthropic
Claude Haiku 4.5
OpenAI
GPT-5.4 mini
Both are sub-$6/1M workhorses with identical 200K context; GPT-5.4 mini wins on raw rates, Haiku 4.5 wins on prompt-cache economics and tooling fit, so the deciding factor is whether your traffic reuses a large fixed prefix.
On list price the gap is real but narrow. GPT-5.4 mini at 0.75 / 4.50 undercuts Haiku 4.5 at 1.00 / 5.00, but the absolute spread is smaller than it looks. A balanced 1M-in + 1M-out job costs $5.25 on mini vs $6.00 on Haiku — a 75-cent, ~12% gap. Skew toward input-heavy work (RAG, classification, extraction: 1M in + 100K out) and it shrinks to $1.20 vs $1.50. Run both through the migration simulator with your actual in/out ratio before trusting any headline number; output dominates because it costs 5-6x input on both models.
Two factors quietly erase most of mini's edge. First, tokenization: Claude encodes English at roughly 0.8x the GPT token count, so Haiku's $5.00 output rate is effectively ~$4.00 per GPT-equivalent token — actually below mini's $4.50 for the same text. Second, caching. Anthropic charges a cache-write premium ($1.25 vs $1.00 input) then reads at $0.10, and the premium pays for itself after a single reuse (the $0.25 write surcharge is recovered by one $0.90-per-1M read saving). If you hammer a fat system prompt or shared document across many calls, Haiku's read economics are excellent; mini reads cheaper still at $0.075 with no write fee, but only if your prefix is stable. See prompt-caching ROI for the breakeven math on your reuse pattern.
Both expose a 50%-off batch API (Haiku $0.50/$2.50, mini $0.375/$2.25) for async work, and both cap at 200K context — so neither wins on window size here; step up to GPT-5.4 (400K) or Sonnet (1M) if you need it. On capability, treat these as peers and run your own eval: Haiku tends to fit cleanly into Anthropic tool-use and agent loops, while mini slots into the OpenAI ecosystem and a 32,768 max-output ceiling. Don't pick on a benchmark you didn't run on your own data.
Cost Calculator
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 Haiku 4.5 | GPT-5.4 mini |
|---|---|---|
| Input (standard) | $1.00 | $0.75 |
| Output (standard) | $5.00 | $4.50 |
| Input (batch) | $0.5000 | $0.3750 |
| Output (batch) | $2.5000 | $2.2500 |
| Cache write | $1.2500 | — |
| Cache read | $0.1000 | $0.0750 |
| Context window | 200K | 200K |
Sources: https://platform.claude.com/docs/en/about-claude/pricing · https://developers.openai.com/api/docs/pricing
When Claude Haiku 4.5 is the better pick
Choose Claude Haiku 4.5 when you reuse a large fixed prefix across many calls (caching pays back after one read), when you're already in Anthropic tooling or agent loops, or when your traffic is English-heavy enough that the ~0.8x token count flips the effective output rate in Haiku's favor.
- Input rate: $1.0000/1M tokens (standard)
- Output rate: $5.0000/1M tokens (standard)
- Batch API available: 50% off — input $0.5000/1M, output $2.5000/1M
- Prompt caching: reads at $0.1000/1M, writes at $1.2500/1M
- Context window: 200K tokens
When GPT-5.4 mini is the better pick
Choose GPT-5.4 mini for stateless, high-volume calls with little prefix reuse where the lower raw rates and zero cache-write fee compound, or when you're standardized on the OpenAI stack and want the cheapest credible workhorse there.
- Input rate: $0.7500/1M tokens (standard)
- Output rate: $4.5000/1M tokens (standard)
- Batch API available: 50% off — input $0.3750/1M, output $2.2500/1M
- Prompt caching: reads at $0.0750/1M (automatic, no write fee)
- Context window: 200K tokens
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:
- Claude Haiku 4.5: (2,000 × $1.0000 + 500 × $5.0000) ÷ 1,000,000 × 1,000,000 = $4,500.00/month
- GPT-5.4 mini: (2,000 × $0.7500 + 500 × $4.5000) ÷ 1,000,000 × 1,000,000 = $3,750.00/month
At this volume and token mix, GPT-5.4 mini is 17% cheaper than the alternative on standard rates — a difference of $750.00/month. Over a full year that compounds to $9,000.00 in savings, which is meaningful even before factoring in batch or caching optimisations.
Scenario B — Batch API enabled (50% off, where supported):
- Claude Haiku 4.5 batch: $2,250.00/month (saving $2,250.00 vs. standard)
- GPT-5.4 mini batch: $1,875.00/month (saving $1,875.00 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 Haiku 4.5 and GPT-5.4 mini 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.
- Move your top-level
systemparameter intomessages[0]withrole: "system". - Switch from XML-style prompt delimiters to markdown formatting — GPT-4o is tuned on headers, bullet points, and code fences.
- Remove
cache_controlbreakpoints from your request body; OpenAI applies prompt caching automatically on eligible repeated prefixes with no explicit configuration. - Mind the context-window change between the two models (shown in the pricing table above): if it shrinks, reintroduce chunking or RAG for long payloads; if it grows, you can simplify that logic.
- Always test on your own production distribution rather than relying solely on public benchmarks, which measure average performance across diverse tasks that may not match your use case.
Frequently asked questions
Which is cheaper at 1M requests/month — Claude Haiku 4.5 or GPT-5.4 mini?
At 1M requests/month with 2,000 input tokens and 500 output tokens per request, GPT-5.4 mini costs $3,750.00 versus Claude Haiku 4.5 at $4,500.00 — a difference of $750.00 per month (17%). Enabling the batch API (where available) cuts those figures by 50% for workloads that tolerate up to 24-hour turnaround.
Does Claude Haiku 4.5 or GPT-5.4 mini support batch API pricing?
Both Claude Haiku 4.5 and GPT-5.4 mini support batch API pricing at 50% off standard rates, in exchange for up to 24-hour result latency. Claude Haiku 4.5 batch input is $0.5/1M and batch output is $2.50/1M. GPT-5.4 mini batch input is $0.375/1M and batch output is $2.25/1M. 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 Haiku 4.5 and GPT-5.4 mini?
Claude Haiku 4.5 supports prompt caching with cache reads at $0.1/1M and cache writes at $1.25/1M. GPT-5.4 mini supports prompt caching with cache reads at $0.075/1M (no separate write fee). 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 Haiku 4.5 or GPT-5.4 mini?
Latency depends on region, time of day, request size, and infrastructure routing — not just model architecture. In general, smaller models (the lower-priced model in this pair) 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.