GPT-5.1 Codex mini vs Qwen3.6 Max Preview
Head-to-head specifications
| Metric | GPT-5.1 Codex mini | Qwen3.6 Max Preview | Difference |
|---|---|---|---|
| Intelligence Index | 32.0 | 40.0 | -20.0% |
| Context window | 922K tokens | 400K tokens | — |
| Blended price ($/1M tokens) | $0.37 | $0.70 | -47.1% |
| Output speed (tokens/s) | 205 | 42 | +388.1% |
| Access | Proprietary API | Open weights | — |
- Qwen3.6 Max Preview leads overall capability (Intelligence Index 40.0 vs 32.0).
- GPT-5.1 Codex mini is the cheaper model to run at $0.37/1M blended tokens — about 1.9× cheaper.
- GPT-5.1 Codex mini offers the larger context window (922K tokens), useful for long documents and codebases.
Verdict: GPT-5.1 Codex mini or Qwen3.6 Max Preview?
GPT-5.1 Codex mini advantages
- Context window (+57%)
- Affordability (+47%)
- Output speed (+80%)
Qwen3.6 Max Preview advantages
- General intelligence (+20%)
Which should you choose?
- Choose the GPT-5.1 Codex mini if you work with long documents or large codebases.
- Choose the Qwen3.6 Max Preview if you need the strongest overall reasoning and accuracy.
- Choose the GPT-5.1 Codex mini if you want the lowest cost per token at scale.
Value for money
GPT-5.1 Codex mini offers more intelligence per dollar (1.5× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
GPT-5.1 Codex mini vs Qwen3.6 Max Preview: which should you choose?
GPT-5.1 Codex mini — OpenAI multimodal model with an Intelligence Index of 32, a 922K-token context window and a blended price of $0.37/1M 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-5.1 Codex mini vs Qwen3.6 Max Preview: Qwen3.6 Max Preview scores higher on the Intelligence Index. Qwen3.6 Max Preview leads overall capability (Intelligence Index 40.0 vs 32.0). GPT-5.1 Codex mini is the cheaper model to run at $0.37/1M blended tokens — about 1.9× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Qwen3.6 Max Preview scores 40.0 versus 32.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The GPT-5.1 Codex mini 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-5.1 Codex mini generates faster (205 vs 42 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, GPT-5.1 Codex mini is the cheaper model to run ($0.37 vs $0.70 per 1M tokens). GPT-5.1 Codex mini 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-5.1 Codex mini better than the Qwen3.6 Max Preview?
GPT-5.1 Codex mini takes the overall edge, though Qwen3.6 Max Preview wins in specific areas worth weighing. Qwen3.6 Max Preview leads overall capability (Intelligence Index 40.0 vs 32.0).
What is the main difference between the GPT-5.1 Codex mini and the Qwen3.6 Max Preview?
Qwen3.6 Max Preview leads overall capability (Intelligence Index 40.0 vs 32.0). GPT-5.1 Codex mini is the cheaper model to run at $0.37/1M blended tokens — about 1.9× cheaper.
Which is better value?
GPT-5.1 Codex mini offers more intelligence per dollar (1.5× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Which should I choose?
Choose the GPT-5.1 Codex mini if you work with long documents or large codebases. Choose the Qwen3.6 Max Preview 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.