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GLM-5.2 vs GPT-5.6 Sol

vs

什麼情況選哪一個

兩個模型都是文字輸出的推理模型,上下文約一百萬 token(glm-5.2 為 1,000,000,gpt-5.6-sol 為 1,050,000),都可選思考,所以真正的分野是價格和輸入模態。大批量純文字長上下文工作選 glm-5.2:$1.4 輸入、$4.4 輸出,比 gpt-5.6-sol 的 $5 和 $30 輸入便宜約 3.6 倍、輸出便宜約 6.8 倍,最大輸出也略高,為 131072。需要圖像輸入時選 gpt-5.6-sol,glm-5.2 不接受圖像。

Benchmark 成績

領先高於平均沒有模型更高GLM-5.2225 / 801 / 80GPT-5.6 Sol4095 / 12128 / 121

兩邊都有成績的有 42 項。

GLM-5.2 GPT-5.6 Sol 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
62.1%
64.6%
BioMysteryBench hard
N/A
44.7%
OSWorld-Verified
N/A
83%
Cybergym
77.2%
84.5%
HealthBench Professional
N/A
60.5%
Finance Agent v2
49.7%
53.8%
Harvey Lab-AA
91%
87.2%
GPQA Diamond
91.2%
94.6%
Agents' Last Exam
23.8%
53.6%
LVBench
N/A
82.1%

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

定價

GLM-5.2 GPT-5.6 Sol Δ
輸入 / 1M tokens $1.4 $5 0.28×
輸出 / 1M tokens $4.4 $30 0.15×
快取讀取 / 1M tokens $0.26 $0.5 0.52×
快取寫入 - 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 76 個聊天模型(對數尺度)

GLM-5.2 · $1.4 GPT-5.6 Sol · $5
$0.05 · Qwen3 VL Flash $30 · GPT-5.4 Pro

功能

GLM-5.2 GPT-5.6 Sol
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(自動) 隱式(自動)
快取存活時間 未公布 5-10m, up to 1h
最小快取前綴 未公布 1024 個 token

規格

GLM-5.2 GPT-5.6 Sol
輸入模態 文字 文字 圖像
輸出模態 文字 文字
發布日期 2026-06-16 2026-07-09
知識截止日期 - 2026-02
上下文視窗 1M 1.1M
最大輸出 131K 128K
思考參數
  • thinking.type
  • reasoning_effort
reasoning.effort
可接受的值
thinking.type
  • enabled
  • disabled
reasoning_effort
  • none
  • minimal
  • low
  • medium
  • high
  • xhigh
  • max (none and minimal skip thinking, low and medium map to high, xhigh maps to max)
reasoning.effort
  • none
  • low
  • medium
  • high
  • xhigh
  • max
預設值 enabled, with reasoning_effort at max: the only GLM with an effort dial, and it defaults to the top of it 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: GLM-5.2 · GPT-5.6 Sol

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

GLM-5.2 通過 · 3 sentences

Enabling reasoning means the LLM now generates thousands of invisible "thinking" tokens to systematically work through complex problems before producing a final answer. Because our cloud providers bill for these internal processing steps at the same rate as standard output, our billable token volume per query has doubled. While this increases our direct API costs, it drastically reduces error rates and manual review labor, ultimately lowering our total cost per resolved transaction.

輸出 1223 tok (+1138 思考) 延遲 17.1 s

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

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

GLM-5.2 未通過 · 1/8 cases (fails [1])

```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 […]

輸出 4097 tok (+4036 思考) 延遲 58.4 s

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

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

GLM-5.2 通過 · 5/5 fields, guidance "withheld"

```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "withheld" } ```

輸出 1947 tok (+1893 思考) 延遲 30.9 s

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

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

GLM-5.2 通過 · 120 words, 0 banned, 1 question

We are introducing Caching for our API Gateway, the smartest way to optimize your workflows. Why pay for the exact same response twice? Now, you can automatically store and reuse prompt results across multiple AI providers, drastically reducing latency and overall operational costs. If a user submits a duplicate query, the gateway serves the cached answer instantly, regardless of whether you route to OpenAI, Anthropic, or others. This directly translates to faster applications and significantly lower monthly API bills. You can easily configure your specific caching rules within the developer dashboard and watch your efficiency soar. Stop wasting your valuable tokens on completely redundant computations. Upgrade to the latest gateway version today and experience the future of intelligent prompt management.

輸出 11125 tok (+10984 思考) 延遲 114.8 s

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

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="glm-5.2",
    # model="gpt-5.6-sol",  # 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)

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常見問題

GLM-5.2 和 GPT-5.6 Sol 哪個比較便宜?

以「輸入 / 1M tokens」來看,GLM-5.2 比較便宜($1.4 對 $5,相差 3.6×)。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 GLM-5.2 和 GPT-5.6 Sol 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。

GLM-5.2 與 GPT-5.6 Sol 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