Qwen3 Max vs GPT-5.6 Sol
Head-to-head specifications
| Metric | Qwen3 Max | GPT-5.6 Sol | Difference |
|---|---|---|---|
| Intelligence Index | 28.0 | 59.0 | -52.5% |
| Context window | 512K tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $0.91 | $1.54 | -40.9% |
| Output speed (tokens/s) | 59 | 57 | +3.5% |
| Access | Open weights | Proprietary API | — |
- GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 28.0).
- Qwen3 Max is the cheaper model to run at $0.91/1M blended tokens — about 1.7× cheaper.
- GPT-5.6 Sol offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: Qwen3 Max or GPT-5.6 Sol?
Qwen3 Max advantages
- Affordability (+41%)
GPT-5.6 Sol advantages
- General intelligence (+53%)
- Context window (+49%)
Which should you choose?
- Choose the Qwen3 Max if you want the lowest cost per token at scale.
- Choose the GPT-5.6 Sol if you need the strongest overall reasoning and accuracy.
Value for money
GPT-5.6 Sol offers more intelligence per dollar (1.2× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Qwen3 Max vs GPT-5.6 Sol: which should you choose?
Qwen3 Max — Alibaba text model with an Intelligence Index of 28, a 512K-token context window and a blended price of $0.91/1M tokens (open weights).
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 Max vs GPT-5.6 Sol: GPT-5.6 Sol scores higher on the Intelligence Index. GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 28.0). Qwen3 Max is the cheaper model to run at $0.91/1M blended tokens — about 1.7× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5.6 Sol scores 59.0 versus 28.0. 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, Qwen3 Max generates faster (59 vs 57 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, Qwen3 Max is the cheaper model to run ($0.91 vs $1.54 per 1M tokens). Qwen3 Max is open weights and GPT-5.6 Sol 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 Max better than the GPT-5.6 Sol?
GPT-5.6 Sol takes the overall edge, though Qwen3 Max wins in specific areas worth weighing. GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 28.0).
What is the main difference between the Qwen3 Max and the GPT-5.6 Sol?
GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 28.0). Qwen3 Max is the cheaper model to run at $0.91/1M blended tokens — about 1.7× cheaper.
Which is better value?
GPT-5.6 Sol 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 Qwen3 Max if you want the lowest cost per token at scale. Choose the GPT-5.6 Sol 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.