GPT-6 Astra vs Claude Opus 4.8
GPT-6 Astra wins on Overall, Coding, Multimodal. Every numeric field is compared below, with a worked monthly-cost example and a pick rule for each use case.
Verdict at a glance
Choose GPT-6 Astra if you…
- Best-in-class computer use
- Frontier research & reasoning
- Autonomous long-horizon tasks
- Strongest cybersecurity (gated)
- Best for: Computer use, Deep research, Cybersecurity
Choose Claude Opus 4.8 if you…
- Previous flagship still capable
- Well-proven stability
- Best for: Deep reasoning, Long documents, Coding
Head-to-head aggregate scores
Scores are 0–100, aggregated from public benchmark information and independently weighted across three leaderboards. Rank is out of 26 tracked models.
Aggregated from public sources and independently weighted; methodology on the Terms page. Scores within 3 points are treated as statistically tied.
Specs & pricing — every field side by side
List API prices in USD per 1M tokens. The highlighted cell is the stronger value/capability on that row.
| Dimension | GPT-6 Astra | Claude Opus 4.8 | Verdict |
|---|---|---|---|
| Vendor | OpenAI (US) | Anthropic (US) | Different vendors |
| Released | 2026.09 | 2026.05 | GPT-6 Astra is newer |
| Overall (rank) | 96 · #1 | 81 · #7 | GPT-6 Astra +15 |
| Coding | 97 · #3 | 87 · #7 | GPT-6 Astra +10 |
| Multimodal | 90 · #7 | 89 · #8 | GPT-6 Astra +1 |
| Context window | 1.05M | 1M | GPT-6 Astra larger |
| Max output | 128K | 128K | Tie |
| Effective-context | 96 | 96 | Tie |
| Input $/1M | $10 | $5 | Claude Opus 4.8 cheaper |
| Output $/1M | $50 | $25 | Claude Opus 4.8 cheaper |
| Cache discount | 50% off | 90% off | Claude Opus 4.8 deeper |
| Speed | ~35 tok/s | ~40 tok/s | Claude Opus 4.8 faster |
| TTFT | 1.1s | 1.0s | Claude Opus 4.8 snappier |
| Function calling | 94 | 88 | GPT-6 Astra ahead |
| Refusal rate | ~13% | ~9% | Claude Opus 4.8 less restrictive |
| English | 99 | 94 | GPT-6 Astra |
| Chinese | 72 | 78 | Claude Opus 4.8 |
| Modalities | text, image | text, image | Same |
| Open weights | No | No | Both closed |
| Fine-tuning | No | No | |
| Free tier | No free API tier; Fast mode delivers up to ~2x throughput | No free tier | |
| SOC2 / no-train | yes / yes | yes / yes | |
| Private deployment | No | No | Neither |
Fields drawn from vendor public documentation and the Modelspectra 26-model dataset; speed varies with network, concurrency and prompt length. Verify current pricing before purchase.
Dimension-by-dimension analysis
Reasoning & overall intelligence
GPT-6 Astra leads the overall aggregate by 15 points (96 vs 81). That is a meaningful, not marginal, edge on hard, multi-step reasoning, while Claude Opus 4.8 remains a strong generalist that is not out of its depth on routine work.
Agentic coding
This is a clear gap: GPT-6 Astra scores 97 against 87. On multi-file edits, SWE-style tickets and long-horizon agent loops GPT-6 Astra needs fewer correction turns; Claude Opus 4.8 is still competent for scripts and assisted completion but trails as task complexity rises.
Multimodal
GPT-6 Astra leads multimodal 90 vs 89. Neither emits native video, so the comparison is about parsing images and documents, not generation.
Speed & latency
Claude Opus 4.8 is faster in interactive use: ~40 tok/s with 1.0s TTFT versus ~35 tok/s with 1.1s TTFT (about 1.1× the throughput). For conversational UIs where perceived responsiveness drives retention, that edge is a real product factor even when raw reasoning is lower.
Context: window vs usable recall
Nominal windows are 1.05M for GPT-6 Astra and 1M for Claude Opus 4.8. Effective-context scores point the same way as window size — GPT-6 Astra is ahead on usable recall (96 vs 96), so prefer it for long-document work where details cannot be missed.
Price & total cost
Claude Opus 4.8 is the cheaper API at $5/$25 versus GPT-6 Astra at $10/$50 per 1M input/output tokens — list input is about 2.0× lower. Cache discounts (GPT-6 Astra 50% vs Claude Opus 4.8 90%) shift the effective bill, worked out below.
Chinese vs English
English: GPT-6 Astra 99 vs Claude Opus 4.8 94. Chinese: 72 vs 78. Both are US-based models; for Chinese-first workloads also compare domestic models on the leaderboard.
Tool use & ecosystem
Function-calling score: GPT-6 Astra 94 vs Claude Opus 4.8 88, so GPT-6 Astra has the edge on structured tool use. Neither offers standard fine-tuning. Factor in existing SDK/plugin familiarity — switching cost often outweighs a few-point tool-use gap.
Cost worked example — same workload, real token math
Assume a production workload of 100M input + 30M output tokens per month, with 90% of input tokens served from cache. Figures use public list prices.
| Scenario · per month | GPT-6 Astra | Claude Opus 4.8 | Gap |
|---|---|---|---|
| List priceno cache applied | $2,500100M in × $10 + 30M out × $50 | $1,250100M in × $5 + 30M out × $25 | 2.00×gap |
| With caching90% of inputs cache-hit | $2,05090M cached in × $5 + 10M in × $10 + 30M out × $50 | $84590M cached in × $0.5 + 10M in × $5 + 30M out × $25 | 2.43×gap |
Illustrative model; your input/output mix and cache-hit ratio change the result. Prices are list rates before any enterprise agreement.