Grok 4.20 0309 vs GPT-5.5
Head-to-head specifications
| Metric | Grok 4.20 0309 | GPT-5.5 | Difference |
|---|---|---|---|
| Intelligence Index | 27.0 | 55.0 | -50.9% |
| Context window | 2M tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $0.72 | $1.54 | -53.2% |
| Access | Proprietary API | Proprietary API | — |
- GPT-5.5 leads overall capability (Intelligence Index 55.0 vs 27.0).
- Grok 4.20 0309 is the cheaper model to run at $0.72/1M blended tokens — about 2.1× cheaper.
- Grok 4.20 0309 offers the larger context window (2M tokens), useful for long documents and codebases.
Verdict: Grok 4.20 0309 or GPT-5.5?
Grok 4.20 0309 advantages
- Context window (+50%)
- Affordability (+53%)
GPT-5.5 advantages
- General intelligence (+51%)
Which should you choose?
- Choose the Grok 4.20 0309 if you work with long documents or large codebases.
- Choose the GPT-5.5 if you need the strongest overall reasoning and accuracy.
- Choose the Grok 4.20 0309 if you want the lowest cost per token at scale.
Value for money
Grok 4.20 0309 offers more intelligence per dollar (1.1× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Grok 4.20 0309 vs GPT-5.5: which should you choose?
Grok 4.20 0309 — xAI multimodal model with an Intelligence Index of 27, a 2M-token context window and a blended price of $0.72/1M tokens.
GPT-5.5 — OpenAI multimodal model with an Intelligence Index of 55, a 1M-token context window and a blended price of $1.54/1M tokens.
Grok 4.20 0309 vs GPT-5.5: GPT-5.5 scores higher on the Intelligence Index. GPT-5.5 leads overall capability (Intelligence Index 55.0 vs 27.0). Grok 4.20 0309 is the cheaper model to run at $0.72/1M blended tokens — about 2.1× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5.5 scores 55.0 versus 27.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The Grok 4.20 0309 accepts up to 2 million tokens per request, which sets how much documentation, transcript or code it can reason over at once.
Pricing and access
At blended per-token rates, Grok 4.20 0309 is the cheaper model to run ($0.72 vs $1.54 per 1M tokens). Grok 4.20 0309 is proprietary api and GPT-5.5 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 Grok 4.20 0309 better than the GPT-5.5?
These two are closely matched — the right pick comes down to which specific strengths you value and the price you actually pay. GPT-5.5 leads overall capability (Intelligence Index 55.0 vs 27.0).
What is the main difference between the Grok 4.20 0309 and the GPT-5.5?
GPT-5.5 leads overall capability (Intelligence Index 55.0 vs 27.0). Grok 4.20 0309 is the cheaper model to run at $0.72/1M blended tokens — about 2.1× cheaper.
Which is better value?
Grok 4.20 0309 offers more intelligence per dollar (1.1× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Which should I choose?
Choose the Grok 4.20 0309 if you work with long documents or large codebases. Choose the GPT-5.5 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.