GPT-5 mini vs Qwen3 Coder Next
Head-to-head specifications
| Metric | GPT-5 mini | Qwen3 Coder Next | Difference |
|---|---|---|---|
| Intelligence Index | 32.0 | 26.0 | +23.1% |
| Coding Index | 15.6 | 36.2 | -56.9% |
| Agentic Index | 19.4 | 8.8 | — |
| Context window | 922K tokens | 400K tokens | — |
| Blended price ($/1M tokens) | $0.26 | $0.40 | -35.0% |
| Output speed (tokens/s) | 93 | 94 | -1.1% |
| Access | Proprietary API | Open weights | — |
- GPT-5 mini leads overall capability (Intelligence Index 32.0 vs 26.0).
- GPT-5 mini is the cheaper model to run at $0.26/1M blended tokens — about 1.5× cheaper.
- GPT-5 mini offers the larger context window (922K tokens), useful for long documents and codebases.
Verdict: GPT-5 mini or Qwen3 Coder Next?
GPT-5 mini advantages
- General intelligence (+19%)
- Agentic task performance (+55%)
- Context window (+57%)
- Affordability (+35%)
Qwen3 Coder Next advantages
- Coding ability (+57%)
Which should you choose?
- Choose the GPT-5 mini if you need the strongest overall reasoning and accuracy.
- Choose the Qwen3 Coder Next if coding and software development are your main workload.
- Choose the GPT-5 mini if you build agents or multi-step tool-use workflows.
Value for money
GPT-5 mini offers more intelligence per dollar (1.9× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
GPT-5 mini vs Qwen3 Coder Next: which should you choose?
GPT-5 mini — OpenAI multimodal model with an Intelligence Index of 32, a 922K-token context window and a blended price of $0.26/1M tokens.
Qwen3 Coder Next — Alibaba text model with an Intelligence Index of 26, a 400K-token context window and a blended price of $0.4/1M tokens (open weights).
GPT-5 mini vs Qwen3 Coder Next: GPT-5 mini scores higher on the Intelligence Index. GPT-5 mini leads overall capability (Intelligence Index 32.0 vs 26.0). GPT-5 mini is the cheaper model to run at $0.26/1M blended tokens — about 1.5× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5 mini scores 32.0 versus 26.0. For software development, the Coding Index puts Qwen3 Coder Next ahead (36.2 vs 15.6). On agentic, multi-step tool-use tasks, GPT-5 mini measures stronger. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The GPT-5 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, Qwen3 Coder Next generates faster (94 vs 93 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, GPT-5 mini is the cheaper model to run ($0.26 vs $0.40 per 1M tokens). GPT-5 mini is proprietary api and Qwen3 Coder Next 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 mini better than the Qwen3 Coder Next?
GPT-5 mini takes the overall edge, though Qwen3 Coder Next wins in specific areas worth weighing. GPT-5 mini leads overall capability (Intelligence Index 32.0 vs 26.0).
What is the main difference between the GPT-5 mini and the Qwen3 Coder Next?
GPT-5 mini leads overall capability (Intelligence Index 32.0 vs 26.0). GPT-5 mini is the cheaper model to run at $0.26/1M blended tokens — about 1.5× cheaper.
Which is better value?
GPT-5 mini offers more intelligence per dollar (1.9× 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 mini if you need the strongest overall reasoning and accuracy. Choose the Qwen3 Coder Next if coding and software development are your main workload.
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.