Comparison · Claude vs GPT

Claude vs GPT token pricing

Headline per-million token prices do not reveal which model is cheaper for your histogram. This page focuses on paired comparisons you can defend in reviews.

Token rate comparison

Model Provider Input Output
Claude 3.5 Sonnet Anthropic $0.0030 / 1K in $0.0150 / 1K out
Claude 3.5 Haiku Anthropic $0.0008 / 1K in $0.0040 / 1K out
GPT-4o OpenAI $0.0025 / 1K in $0.0100 / 1K out
GPT-4o mini OpenAI $0.0002 / 1K in $0.0006 / 1K out

Input/output asymmetry

Both families charge more for output than input in most tiers. Long answers hurt either way.

Context behavior

Long system prompts are common in Claude apps—audit them for duplication and staleness.

Performance vs price

Run offline evals on refusal rates, formatting reliability, and latency—especially for customer chat.

Use-case guidance

GPT-4o mini / Haiku for triage, Sonnet / GPT-4o for customer replies, Opus / o-series for hardest reasoning—adjust to your SLOs.

Developer notes

SDK ergonomics differ; factor onboarding time for new hires when picking a primary vendor.

Optimization

Use structured outputs, trim tool payloads, and cache static instructions to shrink prompts.

FAQ: Claude vs GPT

Short answers mirror the structured data on this page for search engines and readers.

Which is cheaper for support bots?
Haiku vs mini depends on measured tokens—use identical prompts in the calculator.
Do prices move in lockstep?
No—each vendor adjusts independently. Refresh config regularly.
How important is tokenizer choice?
Very—identical English sentences can tokenize differently across models.
Can we hedge with both?
Yes, with a routing layer and clear fallback policies.

Compare Claude and GPT tokens

Align completion caps—GPT and Claude differ in verbosity on some prompts.

Prefilled for this page’s scenario. Pricing loads from config/models.php and /api/pricing.

Calculator

Cost = (prompt ÷ 1000 × Pin) + (completion ÷ 1000 × Pout), then × requests.

Usage presets

Multi-model comparison

Toggle models to compare the same workload. The cheapest option is highlighted.

Monthly cost simulator

Project from average daily requests (uses tokens above).

Uses primary model rates for projections.

Token estimator

Rough heuristic: ~4 characters ≈ 1 token for Latin text (indicative only).

Estimated tokens: 0 · Cost @ primary:

API budget planner

Set a monthly cap to see how many identical requests fit (primary model).

Max requests (approx):

Prompt optimization analyzer

Collapse whitespace and tighten wording to preview savings at the primary model.

Suggested shorter form:


                    

Token delta: 0 · Est. savings / 1k calls:

Fine-tuning cost sketch

Order-of-magnitude helper: training tokens × epochs × rate + storage.

Est. training + 1 mo storage:

Team usage calculator

Multiply per-person daily volume by team size (primary model).

Team monthly (22d):

Cost per feature

Price a single product surface (e.g., one chat turn or one generated article).

Uses prompt & completion tokens from the calculator for one invocation.

Cost per use: · Monthly @ that cadence:

Share & export

Serialize inputs in the URL hash or copy a text summary.

Calculation history

Stored in your browser only (LocalStorage).