GPT-5.6 Terra vs Claude Opus 4.7
Head-to-head specifications
| Metric | GPT-5.6 Terra | Claude Opus 4.7 | Difference |
|---|---|---|---|
| Intelligence Index | 55.0 | 54.0 | +1.9% |
| Coding Index | 76.7 | 73.6 | +4.2% |
| Agentic Index | 47.4 | 44.4 | — |
| Context window | 1M tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $1.14 | $1.43 | -20.3% |
| Output speed (tokens/s) | 138 | 47 | +193.6% |
| Access | Proprietary API | Proprietary API | — |
- GPT-5.6 Terra leads overall capability (Intelligence Index 55.0 vs 54.0).
- GPT-5.6 Terra is the cheaper model to run at $1.14/1M blended tokens — about 1.3× cheaper.
Verdict: GPT-5.6 Terra or Claude Opus 4.7?
GPT-5.6 Terra advantages
- Coding ability (+4%)
- Agentic task performance (+6%)
- Affordability (+20%)
- Output speed (+66%)
Claude Opus 4.7 advantages
- No decisive advantage on the tracked metrics.
Which should you choose?
- Choose the GPT-5.6 Terra if coding and software development are your main workload.
- Choose the GPT-5.6 Terra if you build agents or multi-step tool-use workflows.
Value for money
GPT-5.6 Terra offers more intelligence per dollar (1.3× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
GPT-5.6 Terra vs Claude Opus 4.7: which should you choose?
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 Opus 4.7 — Anthropic multimodal model with an Intelligence Index of 54, a 1M-token context window and a blended price of $1.43/1M tokens.
GPT-5.6 Terra vs Claude Opus 4.7: GPT-5.6 Terra scores higher on the Intelligence Index. GPT-5.6 Terra leads overall capability (Intelligence Index 55.0 vs 54.0). GPT-5.6 Terra is the cheaper model to run at $1.14/1M 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 54.0. For software development, the Coding Index puts GPT-5.6 Terra ahead (76.7 vs 73.6). 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 GPT-5.6 Terra 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 47 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 $1.43 per 1M tokens). GPT-5.6 Terra is proprietary api and Claude Opus 4.7 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 GPT-5.6 Terra better than the Claude Opus 4.7?
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 54.0).
What is the main difference between the GPT-5.6 Terra and the Claude Opus 4.7?
GPT-5.6 Terra leads overall capability (Intelligence Index 55.0 vs 54.0). GPT-5.6 Terra is the cheaper model to run at $1.14/1M blended tokens — about 1.3× cheaper.
Which is better value?
GPT-5.6 Terra offers more intelligence per dollar (1.3× 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 coding and software development are your main workload. Choose the GPT-5.6 Terra if you build agents or multi-step tool-use workflows.
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.