Qwen3.5 Omni Flash vs GPT-5.6 Sol
Head-to-head specifications
| Metric | Qwen3.5 Omni Flash | GPT-5.6 Sol | Difference |
|---|---|---|---|
| Intelligence Index | 24.0 | 59.0 | -59.3% |
| Context window | 400K tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $0.17 | $1.54 | -89.0% |
| Output speed (tokens/s) | 249 | 57 | +336.8% |
| Access | Open weights | Proprietary API | — |
- GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 24.0).
- Qwen3.5 Omni Flash is the cheaper model to run at $0.17/1M blended tokens — about 9.1× cheaper.
- GPT-5.6 Sol offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: Qwen3.5 Omni Flash or GPT-5.6 Sol?
Qwen3.5 Omni Flash advantages
- Affordability (+89%)
- Output speed (+77%)
GPT-5.6 Sol advantages
- General intelligence (+59%)
- Context window (+60%)
Which should you choose?
- Choose the Qwen3.5 Omni Flash if you want the lowest cost per token at scale.
- Choose the GPT-5.6 Sol if you need the strongest overall reasoning and accuracy.
- Choose the Qwen3.5 Omni Flash if low latency and fast generation matter for your application.
Value for money
Qwen3.5 Omni Flash offers more intelligence per dollar (3.7× 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.
Qwen3.5 Omni Flash vs GPT-5.6 Sol: which should you choose?
Qwen3.5 Omni Flash — Alibaba multimodal model with an Intelligence Index of 24, a 400K-token context window and a blended price of $0.17/1M tokens (open weights).
GPT-5.6 Sol — OpenAI multimodal model with an Intelligence Index of 59, a 1M-token context window and a blended price of $1.54/1M tokens.
Qwen3.5 Omni Flash vs GPT-5.6 Sol: GPT-5.6 Sol scores higher on the Intelligence Index. GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 24.0). Qwen3.5 Omni Flash is the cheaper model to run at $0.17/1M blended tokens — about 9.1× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5.6 Sol scores 59.0 versus 24.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The GPT-5.6 Sol accepts up to 1 million tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, Qwen3.5 Omni Flash generates faster (249 vs 57 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, Qwen3.5 Omni Flash is the cheaper model to run ($0.17 vs $1.54 per 1M tokens). Qwen3.5 Omni Flash is open weights and GPT-5.6 Sol 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 Flash better than the GPT-5.6 Sol?
These two are closely matched — the right pick comes down to which specific strengths you value and the price you actually pay. GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 24.0).
What is the main difference between the Qwen3.5 Omni Flash and the GPT-5.6 Sol?
GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 24.0). Qwen3.5 Omni Flash is the cheaper model to run at $0.17/1M blended tokens — about 9.1× cheaper.
Which is better value?
Qwen3.5 Omni Flash offers more intelligence per dollar (3.7× 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 Qwen3.5 Omni Flash if you want the lowest cost per token at scale. Choose the GPT-5.6 Sol 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.