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GPT-5.6 Sol vs Qwen3.8 Max

vs

什麼情況選哪一個

這兩者形態接近——都接受文字和影像輸入、回傳文字,都帶對話、視覺、程式碼、工具和推理——所以差別主要在價格和旋鈕:qwen3.8-max 輸入 $2、輸出 $6,對比 gpt-5.6-sol 的 $5 和 $30,輸入便宜 2.5 倍、輸出便宜 5 倍,快取讀取 $0.25 對 $0.5。想要略大的 1050000 token 脈絡,或為延遲敏感呼叫關閉思考的能力時選 gpt-5.6-sol;983616 token 視窗和 131072 最大輸出夠用的大批量工作選 qwen3.8-max。

Benchmark 成績

領先高於平均沒有模型更高GPT-5.6 Sol1595 / 12128 / 121Qwen3.8 Max531 / 408 / 40

兩邊都有成績的有 20 項。

GPT-5.6 Sol Qwen3.8 Max 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
64.6%
67.7%
BioMysteryBench hard
44.7%
N/A
OSWorld-Verified
83%
N/A
Cybergym
84.5%
78.5%
HealthBench
57%
沒有模型分數更高 60.2%
JobBench
45.4%
53.4%
Harvey Lab-AA
87.2%
N/A
GPQA Diamond
94.6%
92.6%
Agents' Last Exam
53.6%
27%
LVBench
82.1%
N/A

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

定價

GPT-5.6 Sol Qwen3.8 Max Δ
輸入 / 1M tokens $5 $2 2.5×
輸出 / 1M tokens $30 $6 5×
快取讀取 / 1M tokens $0.5 $0.25 2×
快取寫入 不額外計費 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 76 個聊天模型(對數尺度)

GPT-5.6 Sol · $5 Qwen3.8 Max · $2
$0.05 · Qwen3 VL Flash $30 · GPT-5.4 Pro

功能

GPT-5.6 Sol Qwen3.8 Max
工具使用 是 是
思考控制 可設定 是,但供應商未公布調整參數
結構化輸出 是 是
提示詞快取 隱式(自動) 隱式 + 顯式
快取存活時間 5-10m, up to 1h explicit: 5m, reset on hit
最小快取前綴 1024 個 token 1024 個 token

規格

GPT-5.6 Sol Qwen3.8 Max
輸入模態 文字 圖像 文字 圖像
輸出模態 文字 文字
發布日期 2026-07-09 2026-08-03
知識截止日期 2026-02 -
上下文視窗 1.1M 984K
最大輸出 128K 131K
思考參數 reasoning.effort -
可接受的值
reasoning.effort
  • none
  • low
  • medium
  • high
  • xhigh
  • max
-
預設值 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: GPT-5.6 Sol · Qwen3.8 Max

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-5.6 Sol 通過 · 3 sentences

Enabling reasoning makes the model generate additional internal tokens before producing an answer, so each request consumes more billable compute. Reasoning may also increase response length, latency, tool calls, and retries, further raising per-request cost. In short, usage volume may be unchanged, but the cost per task increased enough to double the bill.

輸出 111 tok (+34 思考) 延遲 2.3 s

Qwen3.8 Max 通過 · 3 sentences

Enabling reasoning makes the model produce additional hidden steps before responding, and those tokens are billable. It also tends to lengthen each interaction because the model works through more possibilities before settling on an answer. Therefore, the bill doubled mainly due to higher compute and token usage per request, not necessarily because the number of requests doubled.

輸出 378 tok (+305 思考) 延遲 8.6 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-5.6 Sol 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 function counted equal *adjacencies* rather than items, so a run of length one produced `0` and every longer run was undercounted by one. Initializing `cur` and `best` to `1` for nonempty input makes them represent item counts, resetting `cur` to `1` starts a new run at the current item, and updating `best` on every iteration ensures a run reaching the final index is included.

輸出 316 tok (+135 思考) 延遲 5.2 s

Qwen3.8 Max 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 bug is that the original code starts `best` and `cur` at `0`, so it counts adjacent equal *transitions* rather than the number of items in the run. A run of length `n` has only `n - 1` equal-neighbor transitions, so single-element inputs return `0`, and runs that reach the end are undercounted by one. Initializing the current run to `1` for the first element, resetting it to `1` on a break, and updating `best` from that count fixes the off-by-one.

輸出 1616 tok (+1411 思考) 延遲 34.7 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-5.6 Sol 通過 · 5/5 fields, guidance "withheld pending the Q4 close"

{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":"11+","guidance":"withheld pending the Q4 close"}

輸出 194 tok (+147 思考) 延遲 3.2 s

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

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

輸出 1199 tok (+1141 思考) 延遲 24.4 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-5.6 Sol 通過 · 120 words, 0 banned, 1 question

Today, we’re introducing Provider Prompt Cache, a new API gateway feature that reuses prompt prefixes across supported AI providers, reducing latency, token costs, and duplicated processing. Teams can define cache policies once, route requests dynamically, and preserve provider flexibility without rewriting application logic. Switching models during testing or failover? The gateway identifies eligible prompt segments, applies provider-specific caching controls, and reports hits, misses, savings, and expiration details through unified logs and metrics. Configurable TTLs, tenant isolation, encryption, and cache-bypass options help teams balance performance, privacy, and freshness for every workload. Provider Prompt Cache is available today in beta through the dashboard and API, with SDK examples and migration guidance included. […]

輸出 733 tok (+564 思考) 延遲 7.7 s

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

Today, our API gateway adds prompt caching across major model providers. It stores prompts and responses in one fast cache layer. Teams can lower token spend, reduce latency, and repeat reliable answers. The feature supports OpenAI, Anthropic, Google, and Mistral through one configuration. You can set retention rules, scope access, and invalidate entries quickly. How does your team maintain consistent results during provider outages? Approved cached responses keep applications stable while fallback routes recover. The dashboard shows hit rates, savings, latency, and provider usage. Engineers receive audit trails for every cached prompt, enabling safer testing. Product managers can compare cost trends before and after cache adoption. Start with a small route, then safely expand caching to production traffic right now.

輸出 2744 tok (+2591 思考) 延遲 46.3 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="gpt-5.6-sol",
    # model="qwen3.8-max",  # 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-5.6 Sol 和 Qwen3.8 Max 哪個比較便宜?

以「輸入 / 1M tokens」來看,Qwen3.8 Max 比較便宜($2 對 $5,相差 2.5×)。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-5.6 Sol 和 Qwen3.8 Max 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-5.6 Sol 與 Qwen3.8 Max 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