Claude Sonnet 4.5 vs GPT-4o mini
| Spec | Claude Sonnet 4.6 | GPT-4o mini |
|---|---|---|
| Provider | Anthropic | OpenAI |
| Input price (per 1M) | $3.00 | $0.15 |
| Output price (per 1M) | $15.00 | $0.60 |
| Context window | 200,000 | 128,000 |
| Tokenizer accuracy | exact (uses official tokenizer) | exact (uses official tokenizer) |
Cost per 1,000 calls across common workloads
| Workload | Claude Sonnet 4.6 | GPT-4o mini | Winner |
|---|---|---|---|
| Short chat (200 in / 100 out) |
$2,100.00 | $90.00 | GPT-4o mini 96% cheaper |
| Medium chat (1,000 in / 500 out) |
$10,500.00 | $450.00 | GPT-4o mini 96% cheaper |
| Heavy generation (1,000 in / 2,000 out) |
$33,000.00 | $1,350.00 | GPT-4o mini 96% cheaper |
| Long context (8,000 in / 500 out) |
$31,500.00 | $1,500.00 | GPT-4o mini 95% cheaper |
| Code review (3,000 in / 600 out) |
$18,000.00 | $810.00 | GPT-4o mini 96% cheaper |
Costs are per 1,000 API calls. Multiply by 1,000 for per-million-calls.
Verdict
GPT-4o mini for volume, Claude Sonnet 4.5 for quality. These aren't the same tier of model, Sonnet 4.5 is Anthropic's mid-tier "smart enough for everything" workhorse, mini is OpenAI's cheap-and-fast utility model. The 20× price gap reflects a real capability gap.
If you're processing tens of millions of calls and the task is well-defined (classification, extraction, summarization of short text), GPT-4o mini almost always wins on TCO. If your prompts require multi-step reasoning, careful writing, or hard instruction-following, Sonnet 4.5 is worth the markup.
Cost example
For a 1,000-token prompt with a 200-token reply:
Claude Sonnet 4.5: 1000 × $3/M + 200 × $15/M = $0.0060 per call
GPT-4o mini: 1000 × $0.15/M + 200 × $0.60/M = $0.000270 per callSonnet costs ~22× more per call. At 1M calls/month: $6,000 vs $270, a $5,730 difference.
Capability gap
Where Sonnet 4.5 outperforms mini:
- Multi-step reasoning. Sonnet's chain-of-thought is materially cleaner on 4+ step problems
- Long-context recall, both have ~200k / ~128k windows, but Sonnet holds detail across the context better
- Code generation on novel problems, mini is fine for refactors and known patterns; Sonnet handles algorithmic problems and careful API design more reliably
- Nuanced writing, marketing copy, technical writing, long-form arguments, Sonnet noticeably better
- Following complex instructions, prompts with 5+ simultaneous constraints
Where mini is competitive or better:
- Classification with 10 or fewer classes, both at >95% accuracy on most benchmarks
- Structured extraction from text. JSON-mode outputs, named entity recognition
- Short summarization, under 500-word inputs
- Q&A retrieval, when given the relevant context directly
Context windows
- Claude Sonnet 4.5: 200,000 tokens
- GPT-4o mini: 128,000 tokens
Both more than enough for typical work. Sonnet's larger window helps for whole-book or multi-document tasks; mini's is plenty for typical RAG.
Tokenizers
Claude's tokenizer (post-Opus 4.8 update) produces ~30% more tokens for the same English text than OpenAI's o200k_base. So when comparing total cost on identical input, multiply Sonnet's per-token price effective by ~1.3.
When to choose each
Use GPT-4o mini when:
- You're processing at scale (>100k calls/month)
- The task is well-defined and benchmarked
- You don't need careful reasoning chains
- Cost is the binding constraint
Use Claude Sonnet 4.5 when:
- Quality of output matters more than per-call cost
- You're doing writing, analysis, or careful code
- Volume is moderate (<10k calls/day)
- You've A/B tested and mini's output is meaningfully worse
Count tokens on Claude Sonnet → · Count tokens on GPT-4o mini →