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) |
Cost per 1,000 calls across common workloads
| Workload | GPT-4o | Claude Sonnet 4.6 | Winner |
|---|---|---|---|
| Short chat (200 in / 100 out) |
$1,500.00 | $2,100.00 | GPT-4o 29% cheaper |
| Medium chat (1,000 in / 500 out) |
$7,500.00 | $10,500.00 | GPT-4o 29% cheaper |
| Heavy generation (1,000 in / 2,000 out) |
$22,500.00 | $33,000.00 | GPT-4o 32% cheaper |
| Long context (8,000 in / 500 out) |
$25,000.00 | $31,500.00 | GPT-4o 21% cheaper |
| Code review (3,000 in / 600 out) |
$13,500.00 | $18,000.00 | GPT-4o 25% cheaper |
Costs are per 1,000 API calls. Multiply by 1,000 for per-million-calls.
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.