Claude Sonnet 4.6 vs GPT-5.6 Terra
Head-to-head specifications
| Metric | Claude Sonnet 4.6 | GPT-5.6 Terra | Difference |
|---|---|---|---|
| Intelligence Index | 47.0 | 55.0 | -14.5% |
| Coding Index | 63.0 | 76.7 | -17.9% |
| Agentic Index | 40.8 | 47.4 | — |
| Context window | 1M tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $1.20 | $1.14 | +5.3% |
| Output speed (tokens/s) | 54 | 138 | -60.9% |
| Access | Proprietary API | Proprietary API | — |
- GPT-5.6 Terra leads overall capability (Intelligence Index 55.0 vs 47.0).
- GPT-5.6 Terra is the cheaper model to run at $1.14/1M blended tokens — about 1.1× cheaper.
Verdict: Claude Sonnet 4.6 or GPT-5.6 Terra?
Claude Sonnet 4.6 advantages
- No decisive advantage on the tracked metrics.
GPT-5.6 Terra advantages
- General intelligence (+15%)
- Coding ability (+18%)
- Agentic task performance (+14%)
- Affordability (+5%)
- Output speed (+61%)
Which should you choose?
- Choose the GPT-5.6 Terra if you need the strongest overall reasoning and accuracy.
Value for money
GPT-5.6 Terra 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 4.6 vs GPT-5.6 Terra: which should you choose?
Claude Sonnet 4.6 — Anthropic multimodal model with an Intelligence Index of 47, a 1M-token context window and a blended price of $1.2/1M 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 4.6 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 47.0). GPT-5.6 Terra is the cheaper model to run at $1.14/1M blended tokens — about 1.1× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5.6 Terra scores 55.0 versus 47.0. For software development, the Coding Index puts GPT-5.6 Terra ahead (76.7 vs 63.0). 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 4.6 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 54 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, GPT-5.6 Terra is the cheaper model to run ($1.14 vs $1.20 per 1M tokens). Claude Sonnet 4.6 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 4.6 better than the GPT-5.6 Terra?
GPT-5.6 Terra is the clearly stronger overall choice, winning most of the dimensions that matter. GPT-5.6 Terra leads overall capability (Intelligence Index 55.0 vs 47.0).
What is the main difference between the Claude Sonnet 4.6 and the GPT-5.6 Terra?
GPT-5.6 Terra leads overall capability (Intelligence Index 55.0 vs 47.0). GPT-5.6 Terra is the cheaper model to run at $1.14/1M blended tokens — about 1.1× cheaper.
Which is better value?
GPT-5.6 Terra 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 GPT-5.6 Terra 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.