Qwen3.5 Omni Plus vs Grok 4.5
Head-to-head specifications
| Metric | Qwen3.5 Omni Plus | Grok 4.5 | Difference |
|---|---|---|---|
| Intelligence Index | 32.0 | 54.0 | -40.7% |
| Context window | 400K tokens | 922K tokens | — |
| Blended price ($/1M tokens) | $0.63 | $0.87 | -27.6% |
| Output speed (tokens/s) | 53 | 118 | -55.1% |
| Access | Open weights | Proprietary API | — |
- Grok 4.5 leads overall capability (Intelligence Index 54.0 vs 32.0).
- Qwen3.5 Omni Plus is the cheaper model to run at $0.63/1M blended tokens — about 1.4× cheaper.
- Grok 4.5 offers the larger context window (922K tokens), useful for long documents and codebases.
Verdict: Qwen3.5 Omni Plus or Grok 4.5?
Qwen3.5 Omni Plus advantages
- Affordability (+28%)
Grok 4.5 advantages
- General intelligence (+41%)
- Context window (+57%)
- Output speed (+55%)
Which should you choose?
- Choose the Qwen3.5 Omni Plus if you want the lowest cost per token at scale.
- Choose the Grok 4.5 if you need the strongest overall reasoning and accuracy.
Value for money
Grok 4.5 offers more intelligence per dollar (1.2× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Qwen3.5 Omni Plus vs Grok 4.5: 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).
Grok 4.5 — xAI multimodal model with an Intelligence Index of 54, a 922K-token context window and a blended price of $0.87/1M tokens.
Qwen3.5 Omni Plus vs Grok 4.5: Grok 4.5 scores higher on the Intelligence Index. Grok 4.5 leads overall capability (Intelligence Index 54.0 vs 32.0). Qwen3.5 Omni Plus is the cheaper model to run at $0.63/1M blended tokens — about 1.4× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Grok 4.5 scores 54.0 versus 32.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The Grok 4.5 accepts up to 922K tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, Grok 4.5 generates faster (118 vs 53 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, Qwen3.5 Omni Plus is the cheaper model to run ($0.63 vs $0.87 per 1M tokens). Qwen3.5 Omni Plus is open weights and Grok 4.5 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 Grok 4.5?
Grok 4.5 takes the overall edge, though Qwen3.5 Omni Plus wins in specific areas worth weighing. Grok 4.5 leads overall capability (Intelligence Index 54.0 vs 32.0).
What is the main difference between the Qwen3.5 Omni Plus and the Grok 4.5?
Grok 4.5 leads overall capability (Intelligence Index 54.0 vs 32.0). Qwen3.5 Omni Plus is the cheaper model to run at $0.63/1M blended tokens — about 1.4× cheaper.
Which is better value?
Grok 4.5 offers more intelligence per dollar (1.2× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Which should I choose?
Choose the Qwen3.5 Omni Plus if you want the lowest cost per token at scale. Choose the Grok 4.5 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.