GPT-6 Astra vs Step 5 Preview
Verdict
- GPT-6 Astra (high) leads on quality: 71.9 vs 65.0.
- GPT-6 Astra (high) is stronger in agents & tools, coding, composite, knowledge, maths, multimodal.
- Step 5 Preview is stronger in long context, reasoning.
- Step 5 Preview is 14× cheaper ($1.43 vs $20.00 per 1M blended).
| Metric | GPT-6 Astra (high) | Step 5 Preview |
|---|---|---|
| BenchLeader Index | 71.9 | 65.0 |
| Agents & tools score | 64.9 | – |
| Coding score | 72.8 | – |
| Composite score | 93.8 | 84.4 |
| Knowledge score | 85.1 | 72.0 |
| Long context score | 66.5 | 70.8 |
| Maths score | 79.5 | – |
| Multimodal score | 71.2 | 60.8 |
| Reasoning score | 81.9 | 82.1 |
| Blended price $/M | $20.00 | $1.43 |
| Output speed | 47 tok/s | – |
| Time to first answer | 49.2 s | – |
| Context window | 1.1M | 1M |
| FrontierMath Tier 4 | 97.6% | – |
| Terminal-Bench | 57.9% | – |
| SciCode | 55.4% | – |
| WeirdML | 92.9% | – |
| AA Intelligence Index | 51.0 | 43.6 |
| AA-LCR | 80.0% | 88.3% |
| MMMU-Pro | 86.4% | 76.4% |
| AA-Omniscience | 43.7 | 16.4 |
| GPQA Diamond (AA) | 95.0% | – |
| Humanity's Last Exam (AA) | 53.1% | 46.5% |
| SciCode (AA) | 55.4% | 58.9% |
| ARC-AGI-1 | 98.5% | – |
| ARC-AGI-2 | 92.1% | – |
| ARC-AGI-3 | 99.9% | – |
| CritPt | 28.9% | 20.9% |
| GDPval (AA) | 51.5% | 53.6% |
| τ²-Bench Banking (AA) | 40.0% | – |
| MirrorCode | 46.7% | – |
| DeepSWE | 73.2% | – |
Data as of 2026-09-19. Best configuration of each model; every score links to its source on the model pages.
GPT-6 Astra vs Step 5 Preview: questions
- Is GPT-6 Astra better than Step 5 Preview?
- GPT-6 Astra (high) leads on quality: 71.9 vs 65.0. The BenchLeader Index combines every independent quality benchmark; GPT-6 Astra (high) is ahead overall as of 2026-09-19, but check the category scores for your use.
- Which is cheaper, GPT-6 Astra or Step 5 Preview?
- Step 5 Preview is cheaper: $1.43 against $20.00 per million tokens, blended at three input tokens per output token.
- Which has the larger context window?
- GPT-6 Astra accepts more context: 1.1M against 1M tokens.