Claude Sonnet 5 vs o3
Head-to-head specifications
| Metric | Claude Sonnet 5 | o3 | Difference |
|---|---|---|---|
| Intelligence Index | 53.0 | 31.0 | +71.0% |
| Context window | 1M tokens | 256K tokens | — |
| Blended price ($/1M tokens) | $0.90 | $0.90 | — |
| Output speed (tokens/s) | 71 | 115 | -38.3% |
| Access | Proprietary API | Proprietary API | — |
- Claude Sonnet 5 leads overall capability (Intelligence Index 53.0 vs 31.0).
- Both cost about the same to run (~$0.90/1M blended tokens), so capability and speed should decide.
- Claude Sonnet 5 offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: Claude Sonnet 5 or o3?
Claude Sonnet 5 advantages
- General intelligence (+42%)
- Context window (+74%)
o3 advantages
- Output speed (+38%)
Which should you choose?
- Choose the Claude Sonnet 5 if you need the strongest overall reasoning and accuracy.
- Choose the o3 if low latency and fast generation matter for your application.
- Choose the Claude Sonnet 5 if you work with long documents or large codebases.
Value for money
Claude Sonnet 5 offers more intelligence per dollar (1.7× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Claude Sonnet 5 vs o3: which should you choose?
Claude Sonnet 5 — Anthropic multimodal model with an Intelligence Index of 53, a 1M-token context window and a blended price of $0.9/1M tokens.
o3 — OpenAI multimodal model with an Intelligence Index of 31, a 256K-token context window and a blended price of $0.9/1M tokens.
Claude Sonnet 5 vs o3: Claude Sonnet 5 scores higher on the Intelligence Index. Claude Sonnet 5 leads overall capability (Intelligence Index 53.0 vs 31.0). Both cost about the same to run (~$0.90/1M blended tokens), so capability and speed should decide.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Claude Sonnet 5 scores 53.0 versus 31.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The Claude Sonnet 5 accepts up to 1 million tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, o3 generates faster (115 vs 71 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, Claude Sonnet 5 is the cheaper model to run ($0.90 vs $0.90 per 1M tokens). Claude Sonnet 5 is proprietary api and o3 is proprietary api. Open-weight models can be self-hosted, trading per-call cost for infrastructure you manage; for production also weigh rate limits, throughput and data-residency requirements.
The verdict
Both are credible choices in the ai model comparison space; the specification table above lays out every metric so you can weigh the trade-offs that matter to you. Pick the one whose strengths line up with how you will actually use it.
Frequently asked questions
Is the Claude Sonnet 5 better than the o3?
Claude Sonnet 5 takes the overall edge, though o3 wins in specific areas worth weighing. Claude Sonnet 5 leads overall capability (Intelligence Index 53.0 vs 31.0).
What is the main difference between the Claude Sonnet 5 and the o3?
Claude Sonnet 5 leads overall capability (Intelligence Index 53.0 vs 31.0). Both cost about the same to run (~$0.90/1M blended tokens), so capability and speed should decide.
Which is better value?
Claude Sonnet 5 offers more intelligence per dollar (1.7× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Which should I choose?
Choose the Claude Sonnet 5 if you need the strongest overall reasoning and accuracy. Choose the o3 if low latency and fast generation matter for your application.
Methodology
Large language models are compared on independent leaderboard metrics: an Intelligence Index (a composite of reasoning and knowledge evaluations), Coding and Agentic indices where measured, community arena Elo, maximum context window, a blended API price per million tokens (weighted across cache-hit, input and output rates), and measured output speed in tokens per second. Where a model ships multiple reasoning-effort variants, we report its strongest variant. Benchmarks capture only part of real-world quality, which also depends on tool use, latency, safety and task fit — and this space moves quickly, so figures reflect the leaderboard snapshot on the page date.