GPT-4o vs Claude Sonnet 4.6
| Spec | GPT-4o | Claude Sonnet 4.6 |
|---|---|---|
| Provider | OpenAI | Anthropic |
| Input price (per 1M) | $2.50 | $3.00 |
| Output price (per 1M) | $10.00 | $15.00 |
| Context window | 128,000 | 200,000 |
| Tokenizer accuracy | exact (uses official tokenizer) | exact (uses official tokenizer) |
Verdict
For most workloads, the choice is cost vs. instruction-following nuance. GPT-4o is 17% cheaper on input and 33% cheaper on output. Claude Sonnet often wins on careful instruction-following, longer-form writing, and complex reasoning. Test both with your actual prompts before committing.
Cost example
For a 1,000-token prompt with a 200-token reply:
GPT-4o: 1000 × $2.50/M + 200 × $10/M = $0.0045 per call
Claude Sonnet: 1000 × $3.00/M + 200 × $15/M = $0.0060 per callSonnet costs ~33% more per call at this ratio. The gap widens as your output share grows: at a 50/50 input/output split, Sonnet costs 50% more.
For 1,000,000 calls per month: $4,500 vs $6,000 — a $1,500/month difference.
Tokenizer note
GPT-4o uses o200k_base. Claude uses Anthropic's proprietary tokenizer (closed source, accessed via the count_tokens API). For typical English text, both produce similar counts — usually within 2-3% of each other. For code or non-English text, the gap can grow to 10%+, which materially changes which model wins on cost for those workloads.
This calculator shows the exact count for both — use it with your real prompts to see which tokenizer is more efficient on your specific text.
When GPT-4o wins
- Cost-sensitive English chat workloads at scale.
- Tool use / function calling — OpenAI's structured outputs are the most reliable in the industry.
- Vision — GPT-4o's image understanding is mature and well-tested.
- JSON-mode strict outputs for parsing pipelines.
When Claude Sonnet wins
- Careful instruction-following on prompts with multiple constraints.
- Long-form writing with consistent voice.
- Code review and refactoring on complex existing codebases (vs. greenfield generation, where GPT-4o is competitive).
- Refusal calibration — Sonnet is less prone to over-refusal on edge cases.
- 200k context window vs GPT-4o's 128k for long-document workloads.
How to decide
Run a labeled eval set on both with your actual prompts. The 33% cost difference matters at scale; quality differences matter at every scale. Don't pick by price card alone.