Qwen3.5 Omni Plus vs GPT-5.1 Codex mini
Head-to-head specifications
| Metric | Qwen3.5 Omni Plus | GPT-5.1 Codex mini | Difference |
|---|---|---|---|
| Intelligence Index | 32.0 | 32.0 | — |
| Context window | 400K tokens | 922K tokens | — |
| Blended price ($/1M tokens) | $0.63 | $0.37 | +70.3% |
| Output speed (tokens/s) | 53 | 205 | -74.1% |
| Access | Open weights | Proprietary API | — |
- Qwen3.5 Omni Plus leads overall capability (Intelligence Index 32.0 vs 32.0).
- GPT-5.1 Codex mini is the cheaper model to run at $0.37/1M blended tokens — about 1.7× cheaper.
- GPT-5.1 Codex mini offers the larger context window (922K tokens), useful for long documents and codebases.
Verdict: Qwen3.5 Omni Plus or GPT-5.1 Codex mini?
Qwen3.5 Omni Plus advantages
- No decisive advantage on the tracked metrics.
GPT-5.1 Codex mini advantages
- Context window (+57%)
- Affordability (+41%)
- Output speed (+74%)
Which should you choose?
- Choose the GPT-5.1 Codex mini if you work with long documents or large codebases.
Value for money
GPT-5.1 Codex mini offers more intelligence per dollar (1.7× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Qwen3.5 Omni Plus vs GPT-5.1 Codex mini: which should you choose?
Qwen3.5 Omni Plus — Alibaba multimodal model with an Intelligence Index of 32, a 400K-token context window and a blended price of $0.63/1M tokens (open weights).
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.
Qwen3.5 Omni Plus vs GPT-5.1 Codex mini: Qwen3.5 Omni Plus scores higher on the Intelligence Index. Qwen3.5 Omni Plus leads overall capability (Intelligence Index 32.0 vs 32.0). GPT-5.1 Codex mini is the cheaper model to run at $0.37/1M blended tokens — about 1.7× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Qwen3.5 Omni Plus scores 32.0 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 53 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 $0.63 per 1M tokens). Qwen3.5 Omni Plus is open weights and GPT-5.1 Codex mini 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 Qwen3.5 Omni Plus better than the GPT-5.1 Codex mini?
GPT-5.1 Codex mini is the clearly stronger overall choice, winning most of the dimensions that matter. Qwen3.5 Omni Plus leads overall capability (Intelligence Index 32.0 vs 32.0).
What is the main difference between the Qwen3.5 Omni Plus and the GPT-5.1 Codex mini?
Qwen3.5 Omni Plus leads overall capability (Intelligence Index 32.0 vs 32.0). GPT-5.1 Codex mini is the cheaper model to run at $0.37/1M blended tokens — about 1.7× cheaper.
Which is better value?
GPT-5.1 Codex mini offers more intelligence per dollar (1.7× 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.
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.