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GPT-5.6 Sol vs Kimi K3

vs

何時該用哪一個 — 綜合評斷，而非基準測試數據表

兩者的脈絡視窗實質相同（1050000 對 1048576 token），所以取捨在價格、輸出長度和輸入：kimi-k3 每百萬 $3/$15，對比 gpt-5.6-sol 的 $5/$30，輸出便宜 2 倍，在文字和影像之外還接受視訊，並且最多可回傳 1048576 token 而非 128000。想要可以為簡短、廉價的回合關閉的推理，加上它明確的視覺標誌和 $0.5 的快取讀取時選 gpt-5.6-sol；視訊輸入、超長單次回覆，或常開思考也無妨的大批量文字工作則選 kimi-k3。

定價

GPT-5.6 Sol Kimi K3 Δ
輸入 / 1M tokens $5 $3 1.7×
輸出 / 1M tokens $30 $15 2×
快取讀取 / 1M tokens $0.5 $0.3 1.7×
快取寫入 不額外計費 — —

費率取自建置時的即時目錄；各模型頁面皆附有目前的費率卡。

它們的相對位置 — 在此計費單位下，所有 63 個 聊天 模型的 每 1M tokens 的輸入價格（對數尺度）

GPT-5.6 Sol · $5 Kimi K3 · $3
$0.05 · Qwen3 VL Flash $30 · GPT-5.4 Pro

能力

GPT-5.6 Sol Kimi K3
工具使用 是 是
思考控制 可配置 常駐開啟
結構化輸出 是 是
提示快取 隱式（自動） 隱式（自動）
快取生命週期 5–10m, up to 1h 未公開
最小快取前綴 1024 個 token 未公開

規格

GPT-5.6 Sol Kimi K3
輸入模態 文字 影像 文字 影像 影片
輸出模態 文字 文字
發布日期 2026-07-09 —
知識截止日期 2026-02 —
上下文視窗 1.1M 1M
最大輸出 128K 1M
思考參數 reasoning.effort reasoning_effort (top-level; the thinking object is not accepted)
可接受的值
reasoning.effort
  • none
  • low
  • medium
  • high
  • xhigh
  • max
reasoning_effort
  • low
  • high
  • max
預設值 medium max

規格摘錄自各供應商的文件；供應商未發布的資料列會直接省略，而非自行推測。 完整來源： GPT-5.6 Sol · Kimi K3

單一提示詞，兩款模型 — 經由閘道測量

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

Kimi K3 通過 · 3 sentences

Reasoning models don't just answer questions—they "think" first, generating long internal chains of step-by-step logic before producing a response. Those hidden thinking steps are billed as output tokens (the most expensive kind, typically 3–5x the price of input tokens), and a single query can generate thousands of them even when the visible answer is only a paragraph long. So you're paying for dramatically more compute per request: the bill doubled because the model does far more work behind the scenes, not because usage increased.

輸出 755 tok (+637 思考) 延遲 20.8 s

指令遵循（恰好三句，可數）、受眾適配（面向 CFO 的語氣），以及下方 token 計量所暴露的隱藏思考計費缺口。

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 修復通過測試

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

Kimi K3 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 best = max(best, cur) else: cur = 1 return best ``` The bug is a fencepost error: `cur` counts *matching adjacent pairs* rather than *items in the run*, because it starts at 0 and only increments when `xs[i] == xs[i - 1]`. A run of length L contains L−1 equal pairs, so every run was undercounted by exactly one. For a single-element list (a run of length 1) the loop never runs and the function returns 0 instead of 1; likewise any run ending at the last index — e.g. the two `2`s in `[1, 2, 2]` — reported 1 instead of 2, making it look like no run existed at all. […]

輸出 1837 tok (+1547 思考) 延遲 47.2 s

修復是否真的正確（可執行）、解釋的資訊密度，以及在一個邊界明確的任務上的 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 合法 JSON，schema 精確

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

Kimi K3 通過 · 5/5 fields, guidance null

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

輸出 924 tok (+863 思考) 延遲 25.9 s

schema 服從度（不臆造欄位）、幻覺壓力（guidance 明確被暫緩給出），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

Kimi K3 通過 · 120 words, 0 banned, 1 question

Meet Prompt Cache, a new API gateway capability that stores prompt responses and serves them across OpenAI, Anthropic, Google, and Azure endpoints. It matches requests by model, prompt hash, tools, temperature, and tenant policy, so repeated work returns fast while sensitive contexts stay isolated. Teams set TTLs, stale rules, encryption scopes, and bypass flags per route. Analytics show hit rate, latency saved, token spend avoided, and drift risk by provider. What changes for developers? Keep one integration, add cache headers, and watch fallback logic respect consent, residency, and audit needs. During rollout, canary keys compare fresh answers with cached copies before promotion. Prompt Cache cuts vendor calls, steadies p95 latency, and gives platform owners controls for cost, quality, and compliance.

輸出 1527 tok (+1354 思考) 延遲 37.9 s

約束服從度（字數預算、禁用詞表、唯一的那句問句）、文風指紋，以及長度控制。

只需一行程式碼即可在兩者間切換

以下每個頁籤中都有這兩個 ID — 醒目提示的這兩行是唯一的修改處。相同的端點，相同的金鑰，相同的請求結構。

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="kimi-k3",  # 取消註解此行，並註解上一行
    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 Kimi K3 哪個比較便宜？

Kimi K3 在 輸入 / 1m tokens 上較便宜（$3 對比 $5，相差 1.7×）。其他項目可能呈現相反結果 — 上表提供完整資訊，實際成本取決於您的使用組合。

我可以在不進行兩次整合的情況下，對 GPT-5.6 Sol 和 Kimi K3 進行 A/B 測試嗎？

可以。兩者皆透過同一個相容 OpenAI 的端點提供服務，並使用同一把 API 金鑰 — 切換只需更改一行的模型字串，因此您可以將部分流量分別導向兩者並直接比較帳單。

GPT-5.6 Sol 與 Kimi K3 支援提示快取嗎？

是的 — 兩者的快取讀取費率皆低於其輸入費率，因此具有暖前綴的工作負載成本會低於牌價所示。確切的快取讀取列請見上方的定價表。

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