GPT-5.5 vs Grok 4.3
Compare GPT-5.5 vs Grok 4.3 API pricing, token costs, context windows, and latency. See which model is cheaper and better for your stack.
GPT-5.5 charges $5.00/1M input and $30.00/1M output tokens, compared to Grok 4.3 at $1.25/1M input and $2.50/1M output. Grok 4.3 is 4.0x cheaper on input tokens. In reasoning benchmarks, Neither rated holds the higher AA Intelligence Index rating. For latency-critical streaming, GPT-5.5 delivers 79 t/s (39.0s TTFT) while Grok 4.3 provides 79 t/s (23.0s TTFT).
Full Metric & Specification Comparison
| Metric | GPT-5.5 | Grok 4.3 | Advantage |
|---|---|---|---|
| Provider | OpenAI | xAI | - |
| Input Token Cost (1M) | $5.000 | $1.250 | Grok 4.3 |
| Output Token Cost (1M) | $30.000 | $2.500 | Grok 4.3 |
| Context Window | 922k tokens | 1,000k tokens | Grok 4.3 |
| AA Intelligence Index | Unrated | Unrated | Unrated |
| Streaming Latency | Moderate | Moderate | - |
Key Architectural Takeaways
- Input Token Cost: GPT-5.5 is $5.00/1M vs Grok 4.3 at $1.25/1M
- Output Token Cost: GPT-5.5 is $30.00/1M vs Grok 4.3 at $2.50/1M
- Context Window: GPT-5.5 supports 922k tokens vs Grok 4.3 with 1,000k tokens
- Benchmark Intelligence Rating: not rated for one or both models
- Inference Latency: GPT-5.5 is rated Moderate vs Grok 4.3 rated Moderate
Calculate Your Team's Exact Bill Difference
Drag the sliders below to see real-time cost divergences based on your daily token consumption.
Deploying Grok 4.3 over GPT-5.5 saves $262.50/mo ($3150/yr) at this volume.
Frequently Asked Questions
Is GPT-5.5 cheaper than Grok 4.3?
Grok 4.3 is significantly more economical, charging $1.25/1M input compared to $5.00/1M for GPT-5.5.
Which model should I choose for production?
If your primary constraint is API cost and high query throughput, choose Grok 4.3. If you require peak reasoning accuracy and complex tool use, Neither rated is recommended.
How does context window compare between GPT-5.5 and Grok 4.3?
Grok 4.3 offers the larger context window (1,000k tokens), allowing you to pass significantly larger codebases or documents without chunking.
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