Claude Haiku 4.5 vs GPT-4o mini: pricing, speed, and use cases (2026)
Claude Haiku 4.5 and GPT-4o mini are the two small-model defaults in 2026 for high-volume RAG, classification, and short agent steps. Below: pricing, latency, context, and where each one wins.
Claude Haiku 4.5 vs GPT-4o mini — at a glance
| Dimension | Claude Haiku 4.5 | GPT-4o mini |
|---|---|---|
| Provider | Anthropic | OpenAI |
| Context window | 200K | 128K |
| Input price (per 1M tok) | $1 | $0.15 |
| Output price (per 1M tok) | $5 | $0.60 |
| Latency (typical) | ~500ms TTFT | ~350ms TTFT |
| Free tier | No | Yes (low quota) |
| Best for | Agent steps, careful tool use, longer 200K context | High-volume RAG, classification, lowest cost-per-call |
Pick Claude Haiku 4.5 or GPT-4o mini?
When to choose Claude Haiku 4.5
Choose Claude Haiku 4.5 when reliability on multi-step tool use matters more than raw cost. Haiku 4.5 follows long system prompts more carefully than GPT-4o mini, handles 200K-token context out of the box, and benefits from Anthropic prompt caching that cuts repeated-context cost up to roughly 90%. It is the common small-model default inside Claude-based agent frameworks.
When to choose GPT-4o mini
Choose GPT-4o mini when cost-per-call is the dominant constraint. At $0.15 / $0.60 per 1M tokens, GPT-4o mini is roughly 6-8x cheaper than Haiku 4.5 on list price. It is the workhorse for high-volume RAG, classification, summarization, and any short, one-shot task. The OpenAI Batch API can cut this further by another 50%.
Run Claude Haiku 4.5 and GPT-4o mini side-by-side
VerticalAPI lets you switch between Claude Haiku 4.5 and GPT-4o mini per-request through a single OpenAI-compatible endpoint. Same SDK, same API key, zero markup on tokens — you pay each provider directly under BYOK.
from openai import OpenAI client = OpenAI(base_url="https://api.verticalapi.com/v1", api_key="vapi_...") # Claude Haiku 4.5 resp_a = client.chat.completions.create( model="claude-haiku-4-5", messages=[{"role": "user", "content": "Hello"}], extra_headers={"X-Provider-Key": "..."}, ) # GPT-4o mini — same SDK, different model + key resp_b = client.chat.completions.create( model="gpt-4o-mini", messages=[{"role": "user", "content": "Hello"}], extra_headers={"X-Provider-Key": "..."}, )
VerticalAPI verdict
Use GPT-4o mini for high-volume RAG, classification, and any short one-shot task where price-per-call is the dominant constraint. Use Claude Haiku 4.5 for small-model agent steps that need reliable tool calling, longer context, or prompt caching savings on repeated system prompts. Through VerticalAPI, route between both with one OpenAI-compatible endpoint.
Frequently asked questions
Is GPT-4o mini really 6x cheaper than Claude Haiku 4.5?
On list price, yes. GPT-4o mini is $0.15 per 1M input and $0.60 per 1M output. Claude Haiku 4.5 is approximately $1 per 1M input and $5 per 1M output. That makes GPT-4o mini roughly 6.7x cheaper on input and 8.3x cheaper on output. The gap narrows when Anthropic prompt caching is enabled, which can cut Haiku's cached-token cost by up to roughly 90%.
Which is better for retrieval-augmented generation (RAG)?
For high-volume RAG where each request is short and one-shot, GPT-4o mini's $0.15/$0.60 pricing usually wins. For RAG over long documents that exceed 128K tokens, Claude Haiku 4.5's 200K context lets you avoid chunking. Quality on factual extraction is comparable on standard benchmarks; pick by cost and context fit.
How do latencies compare?
GPT-4o mini typically shows around 350ms time-to-first-token. Claude Haiku 4.5 lands near 500ms TTFT. Throughput per request is in the same range for both. For user-facing chat and interactive search, GPT-4o mini feels noticeably snappier; for background batch jobs the difference rarely matters.
Can Claude Haiku 4.5 replace GPT-4o mini in agent loops?
Yes for many agent loops. Haiku 4.5 supports tool calling, vision, and Anthropic's computer-use API. It tends to follow multi-step instructions more carefully than GPT-4o mini, at the cost of higher per-call price. Teams often use GPT-4o mini for cheap classification and Haiku 4.5 for the tool-using planner step.
How do I switch between Haiku and GPT-4o mini via VerticalAPI?
VerticalAPI exposes one OpenAI-compatible endpoint at https://api.verticalapi.com/v1. Change the model parameter to claude-haiku-4-5 or gpt-4o-mini and supply the matching X-Provider-Key. There is no token markup — you pay Anthropic and OpenAI directly under your own keys (BYOK).
Limitations of this comparison
- Prices reflect mid-2026 list pricing; both vendors revise pricing several times per year and offer volume discounts not shown.
- Benchmark scores depend on the agent framework and prompt scaffolding; the same model can vary by 5-10 points between published runs.
- Latency numbers average across regions; actual TTFT depends on prompt length, region, and provider load.
- GPT-4o mini's $0.15/$0.60 applies to standard tier — Batch API and cached input are cheaper but require different request shapes.
- Claude Haiku 4.5 cost advantage assumes prompt caching is enabled for long system prompts; without it, the gap narrows.
What may change in 12-24 months
- Small-model prices on both sides are expected to keep falling; the 6-8x list-price gap may compress as Anthropic ships a cheaper Haiku tier.
- Context windows on the small tier are likely to converge near 200K-1M across vendors within 12-18 months.
- Agent-quality benchmarks for small models will become the buying criterion as raw chat quality saturates.
- Cross-provider gateways like VerticalAPI will make swapping small models per-task a default pattern rather than a migration.
Related questions
ChatGPT, Perplexity and Gemini usually suggest these next.
- Is Claude Haiku 4.5 cheaper than GPT-4o mini once prompt caching is enabled?
- How do GPT-4o mini and Gemini 2.5 Flash compare for high-volume RAG?
- When should I upgrade from Haiku 4.5 to Sonnet 4.5 in an agent loop?
- What is the cheapest path to A/B test Haiku and GPT-4o mini on the same traffic?
- How does Claude Haiku 4.5 perform on tool calling versus GPT-4o mini?
More head-to-head comparisons
Same comparison from the OpenAI angle
Small-model showdown across three vendors
Flagship-tier comparison
How prompt caching changes small-model math