Claude Sonnet 5 vs GPT-5.6 Terra
Head-to-head specifications
| Metric | Claude Sonnet 5 | GPT-5.6 Terra | Difference |
|---|---|---|---|
| Intelligence Index | 53.0 | 55.0 | -3.6% |
| Coding Index | 71.5 | 76.7 | -6.8% |
| Agentic Index | 46.7 | 47.4 | — |
| Context window | 1M tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $0.90 | $1.14 | -21.1% |
| Output speed (tokens/s) | 71 | 138 | -48.6% |
| Access | Proprietary API | Proprietary API | — |
- GPT-5.6 Terra leads overall capability (Intelligence Index 55.0 vs 53.0).
- Claude Sonnet 5 is the cheaper model to run at $0.90/1M blended tokens — about 1.3× cheaper.
Verdict: Claude Sonnet 5 or GPT-5.6 Terra?
Claude Sonnet 5 advantages
- Affordability (+21%)
GPT-5.6 Terra advantages
- Coding ability (+7%)
- Output speed (+49%)
Which should you choose?
- Choose the Claude Sonnet 5 if you want the lowest cost per token at scale.
- Choose the GPT-5.6 Terra if coding and software development are your main workload.
Value for money
Claude Sonnet 5 offers more intelligence per dollar (1.2× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Claude Sonnet 5 vs GPT-5.6 Terra: 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.
GPT-5.6 Terra — OpenAI multimodal model with an Intelligence Index of 55, a 1M-token context window and a blended price of $1.14/1M tokens.
Claude Sonnet 5 vs GPT-5.6 Terra: GPT-5.6 Terra scores higher on the Intelligence Index. GPT-5.6 Terra leads overall capability (Intelligence Index 55.0 vs 53.0). Claude Sonnet 5 is the cheaper model to run at $0.90/1M blended tokens — about 1.3× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5.6 Terra scores 55.0 versus 53.0. For software development, the Coding Index puts GPT-5.6 Terra ahead (76.7 vs 71.5). On agentic, multi-step tool-use tasks, GPT-5.6 Terra 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, GPT-5.6 Terra generates faster (138 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 $1.14 per 1M tokens). Claude Sonnet 5 is proprietary api and GPT-5.6 Terra 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 GPT-5.6 Terra?
GPT-5.6 Terra takes the overall edge, though Claude Sonnet 5 wins in specific areas worth weighing. GPT-5.6 Terra leads overall capability (Intelligence Index 55.0 vs 53.0).
What is the main difference between the Claude Sonnet 5 and the GPT-5.6 Terra?
GPT-5.6 Terra leads overall capability (Intelligence Index 55.0 vs 53.0). Claude Sonnet 5 is the cheaper model to run at $0.90/1M blended tokens — about 1.3× cheaper.
Which is better value?
Claude Sonnet 5 offers more intelligence per dollar (1.2× 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 GPT-5.6 Terra if coding and software development are your main workload.
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.