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Claude Opus 5 vs GPT-6 Astra

vs

什麼情況選哪一個

選擇 claude-opus-5 可降低成本,其標準輸入 $5 與輸出 $25 的費率剛好是 gpt-6-astra 的一半,而快取讀取的 $0.5 價格則是四分之一。兩種模型皆具有 128000 的最大輸出限制並支援圖像輸入,但 gpt-6-astra 提供較大的 1050000 上下文視窗 (相較於 1000000)。如果您需要擴增的上下文容量或其較新的 2026-09-03 發布日期,請選擇 gpt-6-astra。

Benchmark 成績

領先高於平均沒有模型更高Claude Opus 5238 / 468 / 46GPT-6 Astra1728 / 2921 / 29

兩邊都有成績的有 19 項。

Claude Opus 5 GPT-6 Astra 其他有成績的模型 其他模型平均 ★ 沒有模型分數更高
DeepSWE 1.1
68.8%
74.1%
BioMysteryBench hard
49.4%
N/A
OSWorld 2.0 offline set, partial
70.2%
沒有模型分數更高 72.6%
ExploitBench (Cap%)
70%
沒有模型分數更高 100%
HealthBench Professional
59.8%
沒有模型分數更高 63.4%
Terminal-Bench-Science 0.1
29%
沒有模型分數更高 64.6%
Legal Agent Benchmark
6.7%
N/A
GPQA Diamond
93.4%
沒有模型分數更高 96%
BrowseComp
90.8%
沒有模型分數更高 91.5%
LVBench
75.4%
N/A

供應商公布: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai

定價

Claude Opus 5 GPT-6 Astra Δ
輸入 / 1M tokens $5 $10 0.5×
輸出 / 1M tokens $25 $50 0.5×
快取讀取 / 1M tokens $0.5 $1 0.5×
快取寫入 1.25x (5m) / 2x (1h) 不額外計費 -

費率取自網站建置時的即時目錄;各模型頁面都列有最新的價目。

兩者的相對位置:每 1M tokens 的輸入價格,涵蓋同一計費單位下全部 79 個聊天模型(對數尺度)

Claude Opus 5 · $5 GPT-6 Astra · $10
$0.05 · Qwen3 VL Flash $30 · GPT-5.4 Pro

功能

Claude Opus 5 GPT-6 Astra
工具使用 是 是
思考控制 可設定 可設定
結構化輸出 是 是
提示詞快取 顯式(由你標記前綴) 隱式(自動)
快取存活時間 5m default, 1h option 5-10m, up to 1h
最小快取前綴 1024 個 token 1024 個 token

規格

Claude Opus 5 GPT-6 Astra
輸入模態 文字 圖像 文字 圖像
輸出模態 文字 文字
發布日期 2026-07-24 2026-09-03
知識截止日期 2026-05 2026-04
上下文視窗 1M 1.1M
最大輸出 128K 128K
思考參數
  • thinking.type
  • output_config.effort
reasoning.effort
可接受的值
thinking.type
  • adaptive
  • disabled
effort
  • low
  • medium
  • high
  • xhigh
  • max
reasoning.effort
  • none
  • low
  • medium
  • high
  • xhigh
  • max
預設值

thinking on

effort
  • high (Claude API and Claude Code)
medium

規格摘錄自各供應商的文件;供應商沒有公布的項目就直接略過,不自行推測。 完整來源: Claude Opus 5 · GPT-6 Astra

同一段提示詞,兩個模型,經閘道實測

提示詞 Explain to a CFO, in exactly three sentences, why our LLM bill doubled after we enabled reasoning. 檢查 恰好 3 句

Claude Opus 5 通過 · 3 sentences

When reasoning is enabled, the model generates a hidden chain of "thinking" tokens before it writes the visible answer, and those internal tokens are billed at the output rate even though no one ever sees them — commonly 3–10x more tokens than the response itself. Because output tokens are priced several times higher than input tokens, that added volume hits us at our most expensive rate, which is why the bill roughly doubled while our request count stayed flat. The spend is controllable: we can cap the reasoning budget per call, route only genuinely hard requests to reasoning mode and cheaper models for the rest, and track cost per successfully completed task rather than cost per token, since a single correct answer usually beats three cheap retries plus human cleanup.

輸出 529 tok 延遲 9.0 s

GPT-6 Astra 通過 · 3 sentences

Enabling reasoning makes the model do more work per request, often generating internal reasoning tokens that are billed even though they do not appear in the final answer. If traffic, model choice, and pricing stayed the same, those extra tokens likely explain why our bill doubled—not longer answers or more users. We should verify the usage breakdown, then reserve higher reasoning levels for tasks where improved results justify the added cost.

輸出 283 tok (+196 思考) 延遲 17.2 s

是否照指令做(剛好三句,數得出來)、語氣是否貼合對象(寫給 CFO 看),以及下方 token 計量顯示出的隱藏思考計費落差。

提示詞 This function is supposed to return the longest run of consecutive equal items, but callers report it is off by one on single-element inputs and misses runs that end at the last index. Fix it and explain the bug in one paragraph. 檢查 修正後通過測試

Claude Opus 5 通過 · 8/8 cases

## Fixed version ```python def longest_run(xs): if not xs: return 0 best = 1 cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 else: cur = 1 if cur > best: best = cur return best ``` ## The bug The counters were tracking the wrong quantity: `cur` counted *matching adjacent pairs* rather than *elements in the current run*, and both counters started at `0` instead of `1`. Since a run of length *n* contains only *n − 1* adjacent equal pairs, every result came back one too small — `[1, 1]` returned 1, `[3, 3, 3]` returned 2 — and a single-element list returned 0 because the loop body never executed and `best` kept its initial value of `0`. […]

輸出 1094 tok 延遲 16.1 s

GPT-6 Astra 通過 · 8/8 cases

```python def longest_run(xs): if not xs: return 0 best = cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 else: cur = 1 best = max(best, cur) return best ``` The original code counts equal adjacent pairs rather than items, so every nonempty run is undercounted by one: a single-element input returns `0`, and a run of three equal items returns `2`. Starting and resetting `cur` to `1` counts the first item of each run, while the empty-input check preserves a result of `0`. Updating `best` after each iteration includes runs ending at the last index without needing a final flush; the original also updated during trailing runs, but undercounted them.

輸出 321 tok (+131 思考) 延遲 13.5 s

修正是否真的正確(跑得起來)、說明的資訊密度,以及在範圍明確的任務上的 token 效率。

提示詞 Extract a JSON object with fields {company, quarter, revenue_usd_m, growth_pct, guidance} from this text. Use null for anything not stated; add no other fields. 檢查 有效的 JSON,完全符合 schema

Claude Opus 5 通過 · 5/5 fields, guidance "Withheld pending Q4 close"

```json { "company": "Meridian Robotics", "quarter": "Q3", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "Withheld pending Q4 close" } ``` Notes on judgment calls: - **quarter**: "September quarter" is labeled Q3 because the text refers to an upcoming "Q4 close," implying a calendar-aligned fiscal year. No fiscal year is given. - **revenue_usd_m**: 412 is stated as constant-currency and hedged ("call it"), so it's approximate; the currency is not explicitly stated as USD. - **growth_pct**: "up eleven and change" means slightly more than 11, so 11 is a floor rather than an exact figure.

輸出 539 tok 延遲 7.6 s

GPT-6 Astra 通過 · 5/5 fields, guidance "Formal guidance is withheld pending the "

{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": null, "growth_pct": "eleven and change", "guidance": "Formal guidance is withheld pending the Q4 close." }

輸出 329 tok (+267 思考) 延遲 16.9 s

是否遵守 schema(不自創欄位)、面對幻覺誘因的表現(題目明確不給 guidance),以及結構化輸出路徑的差異。

提示詞 Write a 120-word product announcement for an API gateway feature that caches prompts across providers. Forbidden words: "seamless", "unlock", "game-changer", "revolutionize", "empower". Exactly one sentence must be a question. 檢查 120 詞,0 個禁用詞

Claude Opus 5 通過 · 123 words, 0 banned, 1 question

**Cross-Provider Prompt Caching** We're introducing Cross-Provider Prompt Caching in the API Gateway. Repeated system prompts, long documents, and few-shot examples are stored once at the gateway layer and reused across OpenAI, Anthropic, Google, and self-hosted models. Instead of paying full input token costs on every request, your application sends a cache reference, and the gateway rehydrates the context before forwarding it downstream. Why does that matter? Teams running high-volume agents and retrieval pipelines typically see input token spend fall 40 to 70 percent, with median latency dropping by several hundred milliseconds. Caches are scoped per project, encrypted at rest, and invalidated automatically when a prompt template changes. Enable it with a single header, and see the docs for TTL tuning and per-route controls.

輸出 1593 tok 延遲 19.1 s

GPT-6 Astra 通過 · 120 words, 0 banned, 1 question

Introducing Cross Provider Prompt Cache, a new API gateway feature that reuses eligible prompt content across supported AI providers. Why pay to process the same context every time? Configure caching once at the gateway, then route requests between models while keeping shared instructions, reference material, and conversation prefixes ready for reuse. Caching controls let teams set expiration windows, isolate tenants, and exclude sensitive content. Cache analytics show hit rates, estimated savings, and latency trends, helping developers tune performance with confidence. Existing routing policies continue to work, so adoption fits your current architecture. Start with a single application, measure the results, and expand as needed. Available today in the dashboard and API, with documentation and examples to guide your first deployment.

輸出 665 tok (+516 思考) 延遲 19.3 s

是否遵守限制(字數上限、禁用詞清單、只能有一句問句)、文字風格的特徵,以及長度控制。

改一行程式碼就能在兩者之間切換

下面每個頁籤都列了這兩個模型 ID,要改的只有醒目標示的那兩行。端點、金鑰和請求格式都不變。

from openai import OpenAI

client = OpenAI(
    base_url="https://synthorai.io/v1",
    api_key="sk-syn-...",
)

resp = client.chat.completions.create(
    model="claude-opus-5",
    # model="gpt-6-astra",  # 取消這一行的註解,並把上一行註解掉
    messages=[{"role": "user", "content": "Summarize this diff"}],
    reasoning_effort="medium",
)
print(resp.choices[0].message.content)

取得 API 金鑰 →

常見問題

Claude Opus 5 和 GPT-6 Astra 哪個比較便宜?

以「輸入 / 1M tokens」來看,Claude Opus 5 比較便宜($5 對 $10,相差 2.0×)。其他項目的結果可能相反,完整價目請看上表;實際成本要看你的用量組合。

可以只串接一次,就對 Claude Opus 5 和 GPT-6 Astra 做 A/B 測試嗎?

可以。兩個模型都走同一個 OpenAI 相容端點,用的也是同一把 API 金鑰,切換時只要改一行裡的模型名稱字串。你可以把一部分流量分別導到兩邊,再直接比較帳單。

Claude Opus 5 與 GPT-6 Astra 支援提示詞快取嗎?

支援。兩者的快取讀取費率都低於輸入費率,所以前綴已經進快取的工作負載,實際成本會比官網價算出來的低。確切的快取讀取價格請見上方定價表。

相關比較