Qwen3.6 27B vs GPT-5.5
Head-to-head specifications
| Metric | Qwen3.6 27B | GPT-5.5 | Difference |
|---|---|---|---|
| Intelligence Index | 32.0 | 55.0 | -41.8% |
| Coding Index | 46.6 | 74.9 | -37.8% |
| Agentic Index | 23.3 | 44.9 | — |
| Context window | 512K tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $0.65 | $1.54 | -57.8% |
| Output speed (tokens/s) | 54 | 67 | -19.4% |
| Access | Open weights | Proprietary API | — |
- GPT-5.5 leads overall capability (Intelligence Index 55.0 vs 32.0).
- Qwen3.6 27B is the cheaper model to run at $0.65/1M blended tokens — about 2.4× cheaper.
- GPT-5.5 offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: Qwen3.6 27B or GPT-5.5?
Qwen3.6 27B advantages
- Affordability (+58%)
GPT-5.5 advantages
- General intelligence (+42%)
- Coding ability (+38%)
- Agentic task performance (+48%)
- Context window (+49%)
- Output speed (+19%)
Which should you choose?
- Choose the Qwen3.6 27B if you want the lowest cost per token at scale.
- Choose the GPT-5.5 if you need the strongest overall reasoning and accuracy.
Value for money
Qwen3.6 27B offers more intelligence per dollar (1.4× 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.
Qwen3.6 27B vs GPT-5.5: which should you choose?
Qwen3.6 27B — Alibaba multimodal model with an Intelligence Index of 32, a 512K-token context window and a blended price of $0.65/1M tokens (open weights).
GPT-5.5 — OpenAI multimodal model with an Intelligence Index of 55, a 1M-token context window and a blended price of $1.54/1M tokens.
Qwen3.6 27B vs GPT-5.5: GPT-5.5 scores higher on the Intelligence Index. GPT-5.5 leads overall capability (Intelligence Index 55.0 vs 32.0). Qwen3.6 27B is the cheaper model to run at $0.65/1M blended tokens — about 2.4× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5.5 scores 55.0 versus 32.0. For software development, the Coding Index puts GPT-5.5 ahead (74.9 vs 46.6). On agentic, multi-step tool-use tasks, GPT-5.5 measures stronger. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The GPT-5.5 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-5.5 generates faster (67 vs 54 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, Qwen3.6 27B is the cheaper model to run ($0.65 vs $1.54 per 1M tokens). Qwen3.6 27B is open weights and GPT-5.5 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 Qwen3.6 27B better than the GPT-5.5?
GPT-5.5 is the clearly stronger overall choice, winning most of the dimensions that matter. GPT-5.5 leads overall capability (Intelligence Index 55.0 vs 32.0).
What is the main difference between the Qwen3.6 27B and the GPT-5.5?
GPT-5.5 leads overall capability (Intelligence Index 55.0 vs 32.0). Qwen3.6 27B is the cheaper model to run at $0.65/1M blended tokens — about 2.4× cheaper.
Which is better value?
Qwen3.6 27B offers more intelligence per dollar (1.4× 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 Qwen3.6 27B if you want the lowest cost per token at scale. Choose the GPT-5.5 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.