GPT-5.6 Terra vs Claude Opus 4.8
Head-to-head specifications
| Metric | GPT-5.6 Terra | Claude Opus 4.8 | Difference |
|---|---|---|---|
| Intelligence Index | 55.0 | 56.0 | -1.8% |
| Coding Index | 76.7 | 74.3 | +3.2% |
| Agentic Index | 47.4 | 47.2 | — |
| Context window | 1M tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $1.14 | $1.38 | -17.4% |
| Output speed (tokens/s) | 138 | 53 | +160.4% |
| Access | Proprietary API | Proprietary API | — |
- Claude Opus 4.8 leads overall capability (Intelligence Index 56.0 vs 55.0).
- GPT-5.6 Terra is the cheaper model to run at $1.14/1M blended tokens — about 1.2× cheaper.
Verdict: GPT-5.6 Terra or Claude Opus 4.8?
GPT-5.6 Terra advantages
- Affordability (+17%)
- Output speed (+62%)
Claude Opus 4.8 advantages
- No decisive advantage on the tracked metrics.
Which should you choose?
- Choose the GPT-5.6 Terra if you want the lowest cost per token at scale.
- Choose the GPT-5.6 Terra if low latency and fast generation matter for your application.
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.
GPT-5.6 Terra vs Claude Opus 4.8: 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 4.8 — Anthropic multimodal model with an Intelligence Index of 56, a 1M-token context window and a blended price of $1.38/1M tokens.
GPT-5.6 Terra 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 55.0). GPT-5.6 Terra is the cheaper model to run at $1.14/1M blended tokens — about 1.2× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Claude Opus 4.8 scores 56.0 versus 55.0. For software development, the Coding Index puts GPT-5.6 Terra ahead (76.7 vs 74.3). 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 53 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 $1.38 per 1M tokens). GPT-5.6 Terra 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 GPT-5.6 Terra better than the Claude Opus 4.8?
GPT-5.6 Terra is the clearly stronger overall choice, winning most of the dimensions that matter. Claude Opus 4.8 leads overall capability (Intelligence Index 56.0 vs 55.0).
What is the main difference between the GPT-5.6 Terra and the Claude Opus 4.8?
Claude Opus 4.8 leads overall capability (Intelligence Index 56.0 vs 55.0). GPT-5.6 Terra is the cheaper model to run at $1.14/1M blended tokens — about 1.2× 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 want the lowest cost per token at scale. Choose the GPT-5.6 Terra 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.