o3-pro vs Qwen3.5 27B
Head-to-head specifications
| Metric | o3-pro | Qwen3.5 27B | Difference |
|---|---|---|---|
| Intelligence Index | 33.0 | 31.0 | +6.5% |
| Context window | 258K tokens | 512K tokens | — |
| Blended price ($/1M tokens) | $1.87 | $0.42 | +345.2% |
| Output speed (tokens/s) | 42 | 74 | -43.2% |
| Access | Proprietary API | Open weights | — |
- o3-pro leads overall capability (Intelligence Index 33.0 vs 31.0).
- Qwen3.5 27B is the cheaper model to run at $0.42/1M blended tokens — about 4.5× cheaper.
- Qwen3.5 27B offers the larger context window (512K tokens), useful for long documents and codebases.
Verdict: o3-pro or Qwen3.5 27B?
o3-pro advantages
- General intelligence (+6%)
Qwen3.5 27B advantages
- Context window (+50%)
- Affordability (+78%)
- Output speed (+43%)
Which should you choose?
- Choose the o3-pro if you need the strongest overall reasoning and accuracy.
- Choose the Qwen3.5 27B if you work with long documents or large codebases.
Value for money
Qwen3.5 27B offers more intelligence per dollar (4.2× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
o3-pro vs Qwen3.5 27B: which should you choose?
o3-pro — OpenAI multimodal model with an Intelligence Index of 33, a 258K-token context window and a blended price of $1.87/1M tokens.
Qwen3.5 27B — Alibaba multimodal model with an Intelligence Index of 31, a 512K-token context window and a blended price of $0.42/1M tokens (open weights).
o3-pro vs Qwen3.5 27B: o3-pro scores higher on the Intelligence Index. o3-pro leads overall capability (Intelligence Index 33.0 vs 31.0). Qwen3.5 27B is the cheaper model to run at $0.42/1M blended tokens — about 4.5× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the o3-pro scores 33.0 versus 31.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The Qwen3.5 27B accepts up to 512K tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, Qwen3.5 27B generates faster (74 vs 42 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, Qwen3.5 27B is the cheaper model to run ($0.42 vs $1.87 per 1M tokens). o3-pro is proprietary api and Qwen3.5 27B 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 o3-pro better than the Qwen3.5 27B?
Qwen3.5 27B takes the overall edge, though o3-pro wins in specific areas worth weighing. o3-pro leads overall capability (Intelligence Index 33.0 vs 31.0).
What is the main difference between the o3-pro and the Qwen3.5 27B?
o3-pro leads overall capability (Intelligence Index 33.0 vs 31.0). Qwen3.5 27B is the cheaper model to run at $0.42/1M blended tokens — about 4.5× cheaper.
Which is better value?
Qwen3.5 27B offers more intelligence per dollar (4.2× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
Which should I choose?
Choose the o3-pro if you need the strongest overall reasoning and accuracy. Choose the Qwen3.5 27B 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.