Verified October 2026

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GPT-5.4 nano vs Claude Haiku 4.5

A straight cost comparison of GPT-5.4 nano (OpenAI) and Claude Haiku 4.5 (Anthropic), per 1,000,000 tokens, verified October 2026.

$0.20GPT-5.4 nano input / 1M
$1Claude Haiku 4.5 input / 1M
77%Blended price gap
GPT-5.4 nanoCheaper blended

Rate-by-rate comparison

USD per 1M tokens · October 2026
MetricGPT-5.4 nanoClaude Haiku 4.5Winner
Input / 1M$0.20$1GPT-5.4 nano
Cached input / 1M$0.02$0.10GPT-5.4 nano
Cache write / 1M—$1.25GPT-5.4 nano
Output / 1M$1.25$5GPT-5.4 nano
Context window400,000200,000GPT-5.4 nano
Batch APIYesYesTie
Long-context tierNoneNone—

For per-token metrics, the lower number wins. For context window and batch support, the higher or broader option wins. A dash means the provider does not publish that rate: a missing cache-write fee means writes are not billed per token, while a missing cached-input rate means cache hits bill at the full input rate.

Blended cost: 1M input + 250K output

ModelBlended costDifference
GPT-5.4 nano$0.512Cheaper
Claude Haiku 4.5$2.25+$1.74

Ten workloads, two bills

The same two models, priced across ten workloads that differ only in their input:output ratio. Nothing else changes — no retries, no system prompt, no middleware.

WorkloadGPT-5.4 nanoClaude Haiku 4.5Winner
Classification
100K reqs · 500 in / 10 out
$11.25$55GPT-5.4 nano
Data extraction
10K reqs · 2K in / 200 out
$6.50$30GPT-5.4 nano
Support chat
1K reqs · 4K in / 600 out
$1.55$7GPT-5.4 nano
Code review
2K reqs · 8K in / 1.5K out
$6.95$31GPT-5.4 nano
Summarisation
1K reqs · 20K in / 1K out
$5.25$25GPT-5.4 nano
Agent loop
5K reqs · 12K in / 2K out
$24.50$110GPT-5.4 nano
Translation
500 reqs · 15K in / 15K out
$10.88$45GPT-5.4 nano
Long-document QA
200 reqs · 120K in / 800 out
$5$24.80GPT-5.4 nano
Cached RAG
1K reqs · 50K in (45K cached) / 1K out
$3.16$14.55GPT-5.4 nano
Nightly batch
10K reqs · 10K in / 2K out
$22.50$100GPT-5.4 nano

GPT-5.4 nano wins 10 of 10, Claude Haiku 4.5 wins 0 of 10. The winner is set by the token mix, not by the headline rate.

Where the two bills cross over

GPT-5.4 nano is cheaper on both sides of the mix: $0.20 vs $1 per million input tokens, and $1.25 vs $5 per million output. No input:output ratio reverses that, so on price alone the other model never becomes the better choice — it has to win on context, latency or output quality.

Cost at four scales

The same support-chat workload (4K input, 600 output per request) at four monthly volumes.

Requests / monthGPT-5.4 nanoClaude Haiku 4.5Difference
100$0.155$0.70GPT-5.4 nano cheaper by $0.545
1,000$1.55$7GPT-5.4 nano cheaper by $5.45
10,000$15.50$70GPT-5.4 nano cheaper by $54.50
100,000$155$700GPT-5.4 nano cheaper by $545

What caching does to each bill

Caching only helps the part of the prompt that repeats. This is the support-chat workload (1,000 requests, 4,000 input tokens each) with an increasing share of that prompt served from a warm cache, written once on the first request.

Cached share of the promptGPT-5.4 nanoClaude Haiku 4.5Winner
0%$1.55$7GPT-5.4 nano
50%$1.19$5.20GPT-5.4 nano
80%$0.975$4.12GPT-5.4 nano
95%$0.867$3.58GPT-5.4 nano

Both models publish a cached-read rate, so the discount is comparable: GPT-5.4 nano reads cache at $0.02 per million, Claude Haiku 4.5 at $0.10.

Which one should you pick?

GPT-5.4 nano wins on classification, data extraction, support chat, code review, summarisation, agent loop, translation, long-document qa, cached rag, nightly batch, while Claude Haiku 4.5 wins on none of the ten workloads above.

Beyond price, the two differ on input rate ($0.20 vs $1); output rate ($1.25 vs $5); cached-input rate ($0.02 vs $0.10); cache-write rate (— vs $1.25); context window (400,000 vs 200,000).

Batch mode is available on GPT-5.4 nano and Claude Haiku 4.5, which halves the nightly job. Both publish cached-input discounts, so a stable system prompt is worth caching on either.

FAQ

Is GPT-5.4 nano cheaper than Claude Haiku 4.5?

On a blended 1M-input-plus-250K-output workload, GPT-5.4 nano is cheaper by 77% ($0.512 vs $2.25). The gap changes for cache-heavy prompts and for batch jobs, as the workload table shows.

Which handles longer prompts, GPT-5.4 nano or Claude Haiku 4.5?

GPT-5.4 nano has the larger context window (400,000 vs 200,000 tokens). Neither model publishes a long-context surcharge.

Do both models support prompt caching?

GPT-5.4 nano bills cached reads at $0.02 per 1M; Claude Haiku 4.5 at $0.10.

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