Qwen3.7 Plus vs GPT-5.6 Terra
Head-to-head specifications
| Metric | Qwen3.7 Plus | GPT-5.6 Terra | Difference |
|---|---|---|---|
| Intelligence Index | 39.0 | 55.0 | -29.1% |
| Coding Index | 55.9 | 76.7 | -27.1% |
| Agentic Index | 20.8 | 47.4 | — |
| Context window | 1M tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $0.26 | $1.14 | -77.2% |
| Output speed (tokens/s) | 53 | 138 | -61.6% |
| Access | Open weights | Proprietary API | — |
- GPT-5.6 Terra leads overall capability (Intelligence Index 55.0 vs 39.0).
- Qwen3.7 Plus is the cheaper model to run at $0.26/1M blended tokens — about 4.4× cheaper.
Verdict: Qwen3.7 Plus or GPT-5.6 Terra?
Qwen3.7 Plus advantages
- Affordability (+77%)
GPT-5.6 Terra advantages
- General intelligence (+29%)
- Coding ability (+27%)
- Agentic task performance (+56%)
- Output speed (+62%)
Which should you choose?
- Choose the Qwen3.7 Plus if you want the lowest cost per token at scale.
- Choose the GPT-5.6 Terra if you need the strongest overall reasoning and accuracy.
Value for money
Qwen3.7 Plus offers more intelligence per dollar (3.1× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
Qwen3.7 Plus vs GPT-5.6 Terra: which should you choose?
Qwen3.7 Plus — Alibaba multimodal model with an Intelligence Index of 39, a 1M-token context window and a blended price of $0.26/1M tokens (open weights).
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.
Qwen3.7 Plus 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 39.0). Qwen3.7 Plus is the cheaper model to run at $0.26/1M blended tokens — about 4.4× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5.6 Terra scores 55.0 versus 39.0. For software development, the Coding Index puts GPT-5.6 Terra ahead (76.7 vs 55.9). 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 Qwen3.7 Plus 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, Qwen3.7 Plus is the cheaper model to run ($0.26 vs $1.14 per 1M tokens). Qwen3.7 Plus is open weights 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 Qwen3.7 Plus better than the GPT-5.6 Terra?
GPT-5.6 Terra takes the overall edge, though Qwen3.7 Plus wins in specific areas worth weighing. GPT-5.6 Terra leads overall capability (Intelligence Index 55.0 vs 39.0).
What is the main difference between the Qwen3.7 Plus and the GPT-5.6 Terra?
GPT-5.6 Terra leads overall capability (Intelligence Index 55.0 vs 39.0). Qwen3.7 Plus is the cheaper model to run at $0.26/1M blended tokens — about 4.4× cheaper.
Which is better value?
Qwen3.7 Plus offers more intelligence per dollar (3.1× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
Which should I choose?
Choose the Qwen3.7 Plus if you want the lowest cost per token at scale. 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.