Claude Sonnet 5 vs Claude Opus 4.8
Head-to-head specifications
| Metric | Claude Sonnet 5 | Claude Opus 4.8 | Difference |
|---|---|---|---|
| Intelligence Index | 53.0 | 56.0 | -5.4% |
| Coding Index | 71.5 | 74.3 | -3.8% |
| Agentic Index | 46.7 | 47.2 | — |
| Context window | 1M tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $0.90 | $1.38 | -34.8% |
| Output speed (tokens/s) | 71 | 53 | +34.0% |
| Access | Proprietary API | Proprietary API | — |
- Claude Opus 4.8 leads overall capability (Intelligence Index 56.0 vs 53.0).
- Claude Sonnet 5 is the cheaper model to run at $0.90/1M blended tokens — about 1.5× cheaper.
Verdict: Claude Sonnet 5 or Claude Opus 4.8?
Claude Sonnet 5 advantages
- Affordability (+35%)
- Output speed (+25%)
Claude Opus 4.8 advantages
- General intelligence (+5%)
Which should you choose?
- Choose the Claude Sonnet 5 if you want the lowest cost per token at scale.
- Choose the Claude Opus 4.8 if you need the strongest overall reasoning and accuracy.
- Choose the Claude Sonnet 5 if low latency and fast generation matter for your application.
Value for money
Claude Sonnet 5 offers more intelligence per dollar (1.5× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Claude Sonnet 5 vs Claude Opus 4.8: 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.
Claude Opus 4.8 — Anthropic multimodal model with an Intelligence Index of 56, a 1M-token context window and a blended price of $1.38/1M tokens.
Claude Sonnet 5 vs Claude Opus 4.8: Claude Opus 4.8 scores higher on the Intelligence Index. Claude Opus 4.8 leads overall capability (Intelligence Index 56.0 vs 53.0). Claude Sonnet 5 is the cheaper model to run at $0.90/1M blended tokens — about 1.5× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Claude Opus 4.8 scores 56.0 versus 53.0. For software development, the Coding Index puts Claude Opus 4.8 ahead (74.3 vs 71.5). On agentic, multi-step tool-use tasks, Claude Opus 4.8 measures stronger. 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, Claude Sonnet 5 generates faster (71 vs 53 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 $1.38 per 1M tokens). Claude Sonnet 5 is proprietary api and Claude Opus 4.8 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 Claude Opus 4.8?
These two are closely matched — the right pick comes down to which specific strengths you value and the price you actually pay. Claude Opus 4.8 leads overall capability (Intelligence Index 56.0 vs 53.0).
What is the main difference between the Claude Sonnet 5 and the Claude Opus 4.8?
Claude Opus 4.8 leads overall capability (Intelligence Index 56.0 vs 53.0). Claude Sonnet 5 is the cheaper model to run at $0.90/1M blended tokens — about 1.5× cheaper.
Which is better value?
Claude Sonnet 5 offers more intelligence per dollar (1.5× 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 want the lowest cost per token at scale. Choose the Claude Opus 4.8 if you need the strongest overall reasoning and accuracy.
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.