GPT-5 vs Claude Opus 4.8
| Spec | GPT-5 | Claude Opus 4.8 |
|---|---|---|
| Provider | OpenAI | Anthropic |
| Input price (per 1M) | $1.25 | $5.00 |
| Output price (per 1M) | $10.00 | $25.00 |
| Context window | 400,000 | 200,000 |
| Tokenizer accuracy | exact (uses official tokenizer) | exact (uses official tokenizer) |
Cost per 1,000 calls across common workloads
| Workload | GPT-5 | Claude Opus 4.8 | Winner |
|---|---|---|---|
| Short chat (200 in / 100 out) |
$1,250.00 | $3,500.00 | GPT-5 64% cheaper |
| Medium chat (1,000 in / 500 out) |
$6,250.00 | $17,500.00 | GPT-5 64% cheaper |
| Heavy generation (1,000 in / 2,000 out) |
$21,250.00 | $55,000.00 | GPT-5 61% cheaper |
| Long context (8,000 in / 500 out) |
$15,000.00 | $52,500.00 | GPT-5 71% cheaper |
| Code review (3,000 in / 600 out) |
$9,750.00 | $30,000.00 | GPT-5 68% cheaper |
Costs are per 1,000 API calls. Multiply by 1,000 for per-million-calls.
Verdict
GPT-5 is the new frontier-tier default. It matches Opus 4.8 on most reasoning benchmarks at a fraction of the per-call cost, with a larger context window. Opus 4.8 still wins on careful long-form writing, code review on novel architectures, and tasks where you specifically prefer Anthropic's tone, but the price gap makes those wins expensive.
Cost example
For a 1,000-token prompt with a 200-token reply:
GPT-5: 1000 × $1.25/M + 200 × $10/M = $0.00325 per call
Claude Opus 4.8: 1000 × $5/M + 200 × $25/M = $0.01000 per callOpus costs ~3× more per call at this prompt/output ratio. For prompts with longer outputs (4k+ tokens), Opus's higher output price widens the gap further.
At 1M calls/month: $3,250 vs $10,000, a $6,750 difference.
Context windows
- GPT-5: 400,000 tokens
- Claude Opus 4.8: 200,000 tokens
GPT-5 has double the context window. For workflows that need a whole codebase or a long document set in one call, this matters; for typical 10-30k-token prompts, both are more than enough.
Tokenizer effect
Claude Opus 4.8's tokenizer produces ~35% more tokens than GPT-5's o200k_base on the same English text. So when comparing real-world cost on identical input:
- Effective Opus input cost per character of English: ~$6.75/M tokens of GPT-equivalent input
- That's 5.4× GPT-5's effective rate, not 4×
The tokenizer gap matters more than the per-token price gap suggests, especially for English-heavy workloads.
Capability differences in 2026
Where GPT-5 leads:
- Per-token and per-output-character cost
- Context window (400k vs 200k)
- Math and computational reasoning (GPT-5 noticeably better on competition math)
- Tool use and function calling (more reliable on complex agent workflows)
Where Opus 4.8 leads:
- Long-form writing quality. Opus's prose still feels more polished
- Code review on unfamiliar architectures, better at identifying subtle issues
- Adherence to complex stylistic instructions
- Refusal/safety behavior is more nuanced (matters for some product surfaces)
The capability gap has narrowed substantially in 2026. Five years ago, "best at reasoning" and "best at writing" picked the same model. Now they often don't, and GPT-5 is on the reasoning side of that split.
When to choose each
Use GPT-5 when:
- You need frontier-tier reasoning at production economics
- You're running multi-step agent workflows with tool use
- Your context regularly exceeds 200k tokens
- Cost-per-call is a meaningful constraint
Use Claude Opus 4.8 when:
- The output is long-form writing that humans will read directly
- You're reviewing code or architecture for subtle issues
- You prefer Anthropic's tone for customer-facing surfaces
- Per-call cost is a non-issue (premium-tier consumer products, enterprise tools)
Count tokens on GPT-5 → · Count tokens on Claude Opus →