GPT-5 vs Qwen3 Max Thinking (Preview)
Head-to-head specifications
| Metric | GPT-5 | Qwen3 Max Thinking (Preview) | Difference |
|---|---|---|---|
| Intelligence Index | 35.0 | 28.0 | +25.0% |
| Context window | 922K tokens | 512K tokens | — |
| Blended price ($/1M tokens) | $0.79 | $0.90 | -12.2% |
| Output speed (tokens/s) | 99 | 55 | +80.0% |
| Access | Proprietary API | Open weights | — |
- GPT-5 leads overall capability (Intelligence Index 35.0 vs 28.0).
- GPT-5 is the cheaper model to run at $0.79/1M blended tokens — about 1.1× cheaper.
- GPT-5 offers the larger context window (922K tokens), useful for long documents and codebases.
Verdict: GPT-5 or Qwen3 Max Thinking (Preview)?
GPT-5 advantages
- General intelligence (+20%)
- Context window (+44%)
- Affordability (+12%)
- Output speed (+44%)
Qwen3 Max Thinking (Preview) advantages
- No decisive advantage on the tracked metrics.
Which should you choose?
- Choose the GPT-5 if you need the strongest overall reasoning and accuracy.
- Choose the GPT-5 if you work with long documents or large codebases.
Value for money
GPT-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 vs Qwen3 Max Thinking (Preview): which should you choose?
GPT-5 — OpenAI multimodal model with an Intelligence Index of 35, a 922K-token context window and a blended price of $0.79/1M tokens.
Qwen3 Max Thinking (Preview) — Alibaba text model with an Intelligence Index of 28, a 512K-token context window and a blended price of $0.9/1M tokens (open weights).
GPT-5 vs Qwen3 Max Thinking (Preview): GPT-5 scores higher on the Intelligence Index. GPT-5 leads overall capability (Intelligence Index 35.0 vs 28.0). GPT-5 is the cheaper model to run at $0.79/1M blended tokens — about 1.1× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5 scores 35.0 versus 28.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The GPT-5 accepts up to 922K tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, GPT-5 generates faster (99 vs 55 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, GPT-5 is the cheaper model to run ($0.79 vs $0.90 per 1M tokens). GPT-5 is proprietary api and Qwen3 Max Thinking (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 better than the Qwen3 Max Thinking (Preview)?
GPT-5 is the clearly stronger overall choice, winning most of the dimensions that matter. GPT-5 leads overall capability (Intelligence Index 35.0 vs 28.0).
What is the main difference between the GPT-5 and the Qwen3 Max Thinking (Preview)?
GPT-5 leads overall capability (Intelligence Index 35.0 vs 28.0). GPT-5 is the cheaper model to run at $0.79/1M blended tokens — about 1.1× cheaper.
Which is better value?
GPT-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 if you need the strongest overall reasoning and accuracy. Choose the GPT-5 if you work with long documents or large codebases.
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.