o3 vs Qwen3.5 27B
Head-to-head specifications
| Metric | o3 | Qwen3.5 27B | Difference |
|---|---|---|---|
| Intelligence Index | 31.0 | 31.0 | — |
| Context window | 256K tokens | 512K tokens | — |
| Blended price ($/1M tokens) | $0.90 | $0.42 | +114.3% |
| Output speed (tokens/s) | 115 | 74 | +55.4% |
| Access | Proprietary API | Open weights | — |
- o3 leads overall capability (Intelligence Index 31.0 vs 31.0).
- Qwen3.5 27B is the cheaper model to run at $0.42/1M blended tokens — about 2.1× cheaper.
- Qwen3.5 27B offers the larger context window (512K tokens), useful for long documents and codebases.
Verdict: o3 or Qwen3.5 27B?
o3 advantages
- Output speed (+36%)
Qwen3.5 27B advantages
- Context window (+50%)
- Affordability (+53%)
Which should you choose?
- Choose the o3 if low latency and fast generation matter for your application.
- 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 (2.1× 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 vs Qwen3.5 27B: which should you choose?
o3 — OpenAI multimodal model with an Intelligence Index of 31, a 256K-token context window and a blended price of $0.9/1M 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 vs Qwen3.5 27B: o3 scores higher on the Intelligence Index. o3 leads overall capability (Intelligence Index 31.0 vs 31.0). Qwen3.5 27B is the cheaper model to run at $0.42/1M blended tokens — about 2.1× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the o3 scores 31.0 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, o3 generates faster (115 vs 74 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 $0.90 per 1M tokens). o3 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 better than the Qwen3.5 27B?
Qwen3.5 27B takes the overall edge, though o3 wins in specific areas worth weighing. o3 leads overall capability (Intelligence Index 31.0 vs 31.0).
What is the main difference between the o3 and the Qwen3.5 27B?
o3 leads overall capability (Intelligence Index 31.0 vs 31.0). Qwen3.5 27B is the cheaper model to run at $0.42/1M blended tokens — about 2.1× cheaper.
Which is better value?
Qwen3.5 27B offers more intelligence per dollar (2.1× 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 if low latency and fast generation matter for your application. 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.