Grok 4.5 vs Qwen3.5 397B A17B
Head-to-head specifications
| Metric | Grok 4.5 | Qwen3.5 397B A17B | Difference |
|---|---|---|---|
| Intelligence Index | 54.0 | 33.0 | +63.6% |
| Coding Index | 72.4 | 48.2 | +50.2% |
| Agentic Index | 45.7 | 19.8 | — |
| Context window | 922K tokens | 512K tokens | — |
| Blended price ($/1M tokens) | $0.87 | $0.65 | +33.8% |
| Output speed (tokens/s) | 118 | 58 | +103.4% |
| Access | Proprietary API | Open weights | — |
- Grok 4.5 leads overall capability (Intelligence Index 54.0 vs 33.0).
- Qwen3.5 397B A17B is the cheaper model to run at $0.65/1M blended tokens — about 1.3× cheaper.
- Grok 4.5 offers the larger context window (922K tokens), useful for long documents and codebases.
Verdict: Grok 4.5 or Qwen3.5 397B A17B?
Grok 4.5 advantages
- General intelligence (+39%)
- Coding ability (+33%)
- Agentic task performance (+57%)
- Context window (+44%)
- Output speed (+51%)
Qwen3.5 397B A17B advantages
- Affordability (+25%)
Which should you choose?
- Choose the Grok 4.5 if you need the strongest overall reasoning and accuracy.
- Choose the Qwen3.5 397B A17B if you want the lowest cost per token at scale.
- Choose the Grok 4.5 if coding and software development are your main workload.
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.
Grok 4.5 vs Qwen3.5 397B A17B: which should you choose?
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 397B A17B — Alibaba text model with an Intelligence Index of 33, a 512K-token context window and a blended price of $0.65/1M tokens (open weights).
Grok 4.5 vs Qwen3.5 397B A17B: Grok 4.5 scores higher on the Intelligence Index. Grok 4.5 leads overall capability (Intelligence Index 54.0 vs 33.0). Qwen3.5 397B A17B is the cheaper model to run at $0.65/1M blended tokens — about 1.3× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Grok 4.5 scores 54.0 versus 33.0. For software development, the Coding Index puts Grok 4.5 ahead (72.4 vs 48.2). On agentic, multi-step tool-use tasks, Grok 4.5 measures stronger. 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 58 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, Qwen3.5 397B A17B is the cheaper model to run ($0.65 vs $0.87 per 1M tokens). Grok 4.5 is proprietary api and Qwen3.5 397B A17B 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 Grok 4.5 better than the Qwen3.5 397B A17B?
Grok 4.5 is the clearly stronger overall choice, winning most of the dimensions that matter. Grok 4.5 leads overall capability (Intelligence Index 54.0 vs 33.0).
What is the main difference between the Grok 4.5 and the Qwen3.5 397B A17B?
Grok 4.5 leads overall capability (Intelligence Index 54.0 vs 33.0). Qwen3.5 397B A17B is the cheaper model to run at $0.65/1M blended tokens — about 1.3× 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 Grok 4.5 if you need the strongest overall reasoning and accuracy. Choose the Qwen3.5 397B A17B if you want the lowest cost per token at scale.
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.