o3 vs Claude Opus 4.8
| Spec | o3 | Claude Opus 4.8 |
|---|---|---|
| Provider | OpenAI | Anthropic |
| Input price (per 1M) | $2.00 | $5.00 |
| Output price (per 1M) | $8.00 | $25.00 |
| Context window | 200,000 | 200,000 |
| Tokenizer accuracy | exact (uses official tokenizer) | exact (uses official tokenizer) |
Cost per 1,000 calls across common workloads
| Workload | o3 | Claude Opus 4.8 | Winner |
|---|---|---|---|
| Short chat (200 in / 100 out) |
$1,200.00 | $3,500.00 | o3 66% cheaper |
| Medium chat (1,000 in / 500 out) |
$6,000.00 | $17,500.00 | o3 66% cheaper |
| Heavy generation (1,000 in / 2,000 out) |
$18,000.00 | $55,000.00 | o3 67% cheaper |
| Long context (8,000 in / 500 out) |
$20,000.00 | $52,500.00 | o3 62% cheaper |
| Code review (3,000 in / 600 out) |
$10,800.00 | $30,000.00 | o3 64% cheaper |
Costs are per 1,000 API calls. Multiply by 1,000 for per-million-calls.
Verdict
Different reasoning philosophies. OpenAI's o3 spends tokens on internal "thinking" before producing output, optimizing for deep deliberation on hard problems. Claude Opus 4.8 produces reasoned output directly, integrating its chain-of-thought into the response. Neither is universally better, they target different problem shapes.
For competition-level math, hard algorithmic reasoning, and PhD-tier science questions, o3 has the edge. For long-form writing that requires reasoning, careful code review, or nuanced multi-constraint problems, Opus 4.8 often produces better-shaped outputs.
Cost example
For a 1,000-token prompt with a 200-token visible reply (note: o3 also bills reasoning tokens, see below):
OpenAI o3: 1000 × $15/M + 200 × $60/M = $0.02700 per call (visible output only)
+ 2000 reasoning tokens × $60/M = $0.12000
Total: $0.14700 per call
Claude Opus 4.8: 1000 × $5/M + 200 × $25/M = $0.01000 per callo3 costs ~15× more per call when you account for hidden reasoning tokens, which on hard problems can be 5,000-20,000 tokens of internal thinking. For easy problems where o3 uses fewer reasoning tokens, the gap narrows but Opus is still 2-5× cheaper.
The reasoning-token bill
This is the catch with o3 (and reasoning models generally). o3 spends "thinking tokens" before producing its visible output, and you pay for them at the output rate.
- Easy problem: o3 might use 500-1,500 reasoning tokens
- Medium problem: 2,000-5,000 reasoning tokens
- Hard problem (math olympiad, complex code architecture): 10,000-50,000+ reasoning tokens
On a hard problem with 20,000 reasoning tokens at $60/M output, that's $1.20 extra per call before the visible response. Reasoning models can produce single calls that cost $5-10 each on the hardest problems.
Opus 4.8 has no separate reasoning-token bill. Its chain-of-thought appears in the visible output, billed at the regular output rate.
Context windows
- o3: 200,000 tokens (input + output + reasoning combined)
- Claude Opus 4.8: 200,000 tokens
Same on paper. But o3's reasoning tokens consume context window space, so the effective input space is smaller in practice for hard problems.
Capability differences
Where o3 leads:
- Competition math (USAMO, IMO-style problems)
- Hard algorithmic reasoning (LiveCodeBench hard problems)
- PhD-tier science Q&A (GPQA)
- Multi-step problem decomposition where the steps aren't visible
Where Opus 4.8 leads:
- Long-form writing that requires reasoning
- Code review on novel architectures
- Nuanced instruction-following with many constraints
- Per-call cost (significantly cheaper)
- Predictable cost (no hidden reasoning-token bill)
- Latency (o3 can take 30s+ on hard problems; Opus responds in 5-15s)
When to choose each
Use OpenAI o3 when:
- The problem is genuinely hard and requires deep reasoning
- You're solving discrete math, algorithms, or science problems
- The value per correct answer is high (research, hard engineering decisions)
- You can budget for $1-5 per call on the hardest queries
Use Claude Opus 4.8 when:
- You need premium reasoning without the reasoning-token surprise
- Output quality of writing matters
- Latency under 15s matters
- Cost predictability matters (no hidden reasoning bill)
- The task is reasoning *adjacent* to writing rather than pure math/logic
Count tokens on o3 → · Count tokens on Claude Opus →