GPT-5.6 Sol vs Qwen3.6 Max Preview
Head-to-head specifications
| Metric | GPT-5.6 Sol | Qwen3.6 Max Preview | Difference |
|---|---|---|---|
| Intelligence Index | 59.0 | 40.0 | +47.5% |
| Context window | 1M tokens | 400K tokens | — |
| Blended price ($/1M tokens) | $1.54 | $0.70 | +120.0% |
| Output speed (tokens/s) | 57 | 42 | +35.7% |
| Access | Proprietary API | Open weights | — |
- GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 40.0).
- Qwen3.6 Max Preview is the cheaper model to run at $0.70/1M blended tokens — about 2.2× cheaper.
- GPT-5.6 Sol offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: GPT-5.6 Sol or Qwen3.6 Max Preview?
GPT-5.6 Sol advantages
- General intelligence (+32%)
- Context window (+60%)
- Output speed (+26%)
Qwen3.6 Max Preview advantages
- Affordability (+55%)
Which should you choose?
- Choose the GPT-5.6 Sol if you need the strongest overall reasoning and accuracy.
- Choose the Qwen3.6 Max Preview if you want the lowest cost per token at scale.
- Choose the GPT-5.6 Sol if you work with long documents or large codebases.
Value for money
Qwen3.6 Max Preview offers more intelligence per dollar (1.5× 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.
GPT-5.6 Sol vs Qwen3.6 Max Preview: which should you choose?
GPT-5.6 Sol — OpenAI multimodal model with an Intelligence Index of 59, a 1M-token context window and a blended price of $1.54/1M tokens.
Qwen3.6 Max Preview — Alibaba multimodal model with an Intelligence Index of 40, a 400K-token context window and a blended price of $0.7/1M tokens (open weights).
GPT-5.6 Sol vs Qwen3.6 Max Preview: GPT-5.6 Sol scores higher on the Intelligence Index. GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 40.0). Qwen3.6 Max Preview is the cheaper model to run at $0.70/1M blended tokens — about 2.2× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5.6 Sol scores 59.0 versus 40.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The GPT-5.6 Sol 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 Sol generates faster (57 vs 42 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, Qwen3.6 Max Preview is the cheaper model to run ($0.70 vs $1.54 per 1M tokens). GPT-5.6 Sol is proprietary api and Qwen3.6 Max Preview is open weights. 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 Sol better than the Qwen3.6 Max Preview?
GPT-5.6 Sol takes the overall edge, though Qwen3.6 Max Preview wins in specific areas worth weighing. GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 40.0).
What is the main difference between the GPT-5.6 Sol and the Qwen3.6 Max Preview?
GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 40.0). Qwen3.6 Max Preview is the cheaper model to run at $0.70/1M blended tokens — about 2.2× cheaper.
Which is better value?
Qwen3.6 Max Preview offers more intelligence per dollar (1.5× 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 GPT-5.6 Sol if you need the strongest overall reasoning and accuracy. Choose the Qwen3.6 Max Preview if you want the lowest cost per token at scale.
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.