GPT-5.5 vs Qwen3 Max (Preview)
Head-to-head specifications
| Metric | GPT-5.5 | Qwen3 Max (Preview) | Difference |
|---|---|---|---|
| Intelligence Index | 55.0 | 24.0 | +129.2% |
| Context window | 1M tokens | 512K tokens | — |
| Blended price ($/1M tokens) | $1.54 | $0.91 | +69.2% |
| Output speed (tokens/s) | 67 | 56 | +19.6% |
| Access | Proprietary API | Open weights | — |
- GPT-5.5 leads overall capability (Intelligence Index 55.0 vs 24.0).
- Qwen3 Max (Preview) is the cheaper model to run at $0.91/1M blended tokens — about 1.7× cheaper.
- GPT-5.5 offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: GPT-5.5 or Qwen3 Max (Preview)?
GPT-5.5 advantages
- General intelligence (+56%)
- Context window (+49%)
- Output speed (+16%)
Qwen3 Max (Preview) advantages
- Affordability (+41%)
Which should you choose?
- Choose the GPT-5.5 if you need the strongest overall reasoning and accuracy.
- Choose the Qwen3 Max (Preview) if you want the lowest cost per token at scale.
- Choose the GPT-5.5 if you work with long documents or large codebases.
Value for money
GPT-5.5 offers more intelligence per dollar (1.4× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
GPT-5.5 vs Qwen3 Max (Preview): which should you choose?
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 Max (Preview) — Alibaba text model with an Intelligence Index of 24, a 512K-token context window and a blended price of $0.91/1M tokens (open weights).
GPT-5.5 vs Qwen3 Max (Preview): GPT-5.5 scores higher on the Intelligence Index. GPT-5.5 leads overall capability (Intelligence Index 55.0 vs 24.0). Qwen3 Max (Preview) 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-5.5 scores 55.0 versus 24.0. 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 56 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, Qwen3 Max (Preview) is the cheaper model to run ($0.91 vs $1.54 per 1M tokens). GPT-5.5 is proprietary api and Qwen3 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-5.5 better than the Qwen3 Max (Preview)?
GPT-5.5 takes the overall edge, though Qwen3 Max (Preview) wins in specific areas worth weighing. GPT-5.5 leads overall capability (Intelligence Index 55.0 vs 24.0).
What is the main difference between the GPT-5.5 and the Qwen3 Max (Preview)?
GPT-5.5 leads overall capability (Intelligence Index 55.0 vs 24.0). Qwen3 Max (Preview) is the cheaper model to run at $0.91/1M blended tokens — about 1.7× cheaper.
Which is better value?
GPT-5.5 offers more intelligence per dollar (1.4× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Which should I choose?
Choose the GPT-5.5 if you need the strongest overall reasoning and accuracy. Choose the Qwen3 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.