GPT-6.1 Terra vs Qwen3.8 Flash

vs

什麼情況選哪一個

輸入不超過 272000 token 時,qwen3.8-flash 的輸入輸出價格較低,並支援影片輸入。gpt-6.1-terra 定位於視覺推理與電腦操作,選擇前應先用實際任務確認較高費用是否值得。

Benchmark 成績

GPT-6.1 Terra:供應商沒有公布 benchmark 成績。

高於平均沒有模型更高Qwen3.8 Flash12 / 163 / 16
GPT-6.1 Terra Qwen3.8 Flash 其他有成績的模型 其他模型平均 ★ 沒有模型分數更高
SWE-Bench Pro
N/A
62.5%
OSWorld 2.0 partial
N/A
52.3%
JobBench
N/A
55.7%
GPQA Diamond
N/A
91.7%
ERQA
N/A
沒有模型分數更高 72.3%
Agents' Last Exam Pass
N/A
24.3%
LVBench
N/A
76.6%

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

定價

GPT-6.1 Terra Qwen3.8 Flash Δ
輸入 / 1M tokens $1 $0.15 6.7×
輸出 / 1M tokens $5 $0.47 11×
快取讀取 / 1M tokens $0.05 $0.016 3.1×
快取寫入 1.25x (30m) / 2.5x (12h) 1.25x -

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 80 個聊天模型(對數尺度)

GPT-6.1 Terra · $1 Qwen3.8 Flash · $0.15
$0.05 · Qwen3 VL Flash $30 · GPT-5.4 Pro

功能

GPT-6.1 Terra Qwen3.8 Flash
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 隱式 + 顯式 隱式 + 顯式
快取存活時間 at least 30m by default, 12h option on the Responses API explicit: 5m, reset on hit
最小快取前綴 1,024 個 token 1,024 個 token

規格

GPT-6.1 Terra Qwen3.8 Flash
輸入模態 文字 圖像 文字 圖像 影片
輸出模態 文字 文字
發布日期 2026-10-26 2026-08-27
知識截止日期 2026-06 -
上下文視窗 1.1M 1M
最大輸出 128K 131K
思考參數 reasoning.effort
  • enable_thinking
  • thinking_budget
  • preserve_thinking
可接受的值
reasoning.effort
  • none
  • low
  • medium
  • high
  • xhigh
  • max
enable_thinking
  • true
  • false
thinking_budget
  • in tokens
preserve_thinking
  • true
  • false
預設值 medium

on

qwen3.8-flash is hybrid thinking with thinking enabled by default, thinking_budget defaults to the 262,144-token chain maximum, and preserve_thinking is off

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: GPT-6.1 Terra · Qwen3.8 Flash

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

GPT-6.1 Terra 通過 · 3 sentences

Enabling reasoning caused the model to process additional internal tokens before producing each answer, even though those tokens are not visible to users. Those tokens are generally billable, increasing the cost per request and potentially doubling the bill even if usage stayed the same. To confirm the cause, compare request counts, model pricing, and token usage—including reasoning tokens—before and after the change.

輸出 196 tok (+115 思考) 延遲 4.6 s

Qwen3.8 Flash 通過 · 3 sentences

Enabling reasoning makes the model spend additional output tokens to plan and validate answers before responding, and those tokens are billed. It also increases compute and latency per request, so each task consumes more capacity than a non-reasoning call. As a result, billable tokens and compute roughly doubled, directly driving the higher invoice.

輸出 330 tok (+262 思考) 延遲 8.9 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

GPT-6.1 Terra 通過 · 8/8 cases

```python def longest_run(xs): if len(xs) == 0: 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 counted matching *adjacent pairs* rather than items, so every nonempty run was undercounted by one, including a single-element input. It did update `best` for matches at the last index, but still undercounted those runs. The fix starts each run at length one and updates `best` as the run grows.

輸出 331 tok (+172 思考) 延遲 6.3 s

Qwen3.8 Flash 通過 · 8/8 cases

```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 best = max(best, cur) return best ``` The bug is that the original version counts adjacent equal pairs rather than items in a run, starting both `cur` and `best` at `0`, and it resets `cur` to `0` after a mismatch, thereby discarding the first element of the new run. That makes single-element inputs return `0`, and because every run is effectively measured in “steps” between equal elements, the last element is not counted—particularly noticeable when the longest run ends at the final index.

輸出 2123 tok (+1933 思考) 延遲 43.8 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

GPT-6.1 Terra 通過 · 5/5 fields, guidance "Withheld pending the Q4 close"

{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": null, "growth_pct": null, "guidance": "Withheld pending the Q4 close" }

輸出 317 tok (+261 思考) 延遲 6.9 s

Qwen3.8 Flash 通過 · 5/5 fields, guidance null

{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null }

輸出 519 tok (+463 思考) 延遲 10.5 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

GPT-6.1 Terra 通過 · 120 words, 0 banned, 1 question

Meet Prompt Cache, a new API gateway feature that stores reusable prompts across providers, helping teams reduce latency and control costs without changing how they build. When a request matches a cached prompt, the gateway retrieves the stored result instead of sending the same work again. Need to route traffic between models or vendors? Prompt Cache works across supported providers, giving your applications a consistent caching layer as your stack evolves. Set expiration policies, monitor cache performance, and choose which requests qualify through familiar gateway controls. Your team can spend less time repeating requests and more time improving the experiences customers rely on. Start using Prompt Cache today and make every request work harder for your application and your budget.

輸出 579 tok (+440 思考) 延遲 9.1 s

Qwen3.8 Flash 通過 · 120 words, 0 banned, 1 question

We are announcing prompt caching across providers in our API gateway. Store approved prompt outputs once and reuse them across supported model providers, cutting latency, cost, and duplicate token spend. The feature matches identical requests, checks validity, and returns cached results while preserving routing controls. Developers keep existing endpoints; the gateway manages storage, invalidation, and provider differences. This reduces noisy repeat calls, improves steady responses, and frees teams to focus on better agent workflows. OpenAI, Anthropic, Google, Mistral, and custom routes are supported. Cache hits appear in analytics with latency, token, and cost reductions visible. Check retention and privacy rules before enabling it. Ready to add cache controls to your gateway? Enable it in settings and watch spend drop now.

輸出 5958 tok (+5805 思考) 延遲 88.8 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="gpt-6.1-terra",
    # model="qwen3.8-flash",  # 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

GPT-6.1 Terra 和 Qwen3.8 Flash 哪個比較便宜?

以「輸入 / 1M tokens」來看,Qwen3.8 Flash 比較便宜($0.15 對 $1,相差 6.7×)。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 GPT-6.1 Terra 和 Qwen3.8 Flash 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。

GPT-6.1 Terra 與 Qwen3.8 Flash 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