GPT-6.1 Sol vs GPT-6.1 Terra

vs

什么时候选哪个

输入不超过 272000 token 时,gpt-6.1-terra 保留相同的上下文与输出上限,输入输出价格均为一半,推理也可关闭。只有任务实测证明更高档位值得额外费用时,再选 gpt-6.1-sol。

价格

GPT-6.1 Sol GPT-6.1 Terra Δ
输入 / 1M token $2 $1 2×
输出 / 1M token $10 $5 2×
缓存读取 / 1M token $0.1 $0.05 2×
缓存写入 1.25x (30m) / 2.5x (12h) 1.25x (30m) / 2.5x (12h) -

价格取自构建时的实时目录,最新价格见各模型页面。

两个模型所处的位置:全部 80 个按同一单位计费的聊天模型的每 1M token 输入价分布(对数刻度)

$0.05 · Qwen3 VL Flash $30 · GPT-5.4 Pro

能力

GPT-6.1 Sol GPT-6.1 Terra
工具调用 是 是
思考控制 始终开启 可配置
结构化输出 是 是
提示词缓存 隐式 + 显式 隐式 + 显式
缓存有效期 at least 30m by default, 12h option on the Responses API at least 30m by default, 12h option on the Responses API
最小缓存前缀 1,024 个 token 1,024 个 token

规格

GPT-6.1 Sol GPT-6.1 Terra
输入模态 文本 图像 文本 图像
输出模态 文本 文本
发布日期 2026-09-29 2026-10-26
知识截止日期 2026-04 2026-06
上下文窗口 1.1M 1.1M
最大输出 128K 128K
思考参数 reasoning.effort reasoning.effort
可选值
reasoning.effort
  • low
  • medium
  • high
  • xhigh
  • max
reasoning.effort
  • none
  • low
  • medium
  • high
  • xhigh
  • max
默认值 medium medium

规格照录自各供应商的文档;供应商没有公布的项目,对应的行直接省略,不做推断。 完整来源: GPT-6.1 Sol · GPT-6.1 Terra

同一条提示词,两个模型,经网关实测

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

GPT-6.1 Sol 通过 · 3 sentences

Enabling reasoning makes the model do more computational work before answering, and many providers bill the resulting reasoning tokens even though users never see them. That means the same number of requests can cost twice as much because each request consumes more billable tokens, even if the visible answers stay the same length. We should confirm the increase in our usage breakdown and reserve higher reasoning levels for tasks where the improvement justifies the added cost.

输出 144 tok (+54 思考) 延迟 6.6 s

GPT-6.1 Terra 通过 · 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.

输出 196 tok (+115 思考) 延迟 4.6 s

指令遵循(恰好三句,数得出来)、受众适配(对 CFO 说话的口吻),以及下方 token 计数暴露出的隐藏思考计费差额。

提示词 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. 检查 修复通过测试

GPT-6.1 Sol 通过 · 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 counts equal adjacent pairs rather than items in a run: a run of length `n` has only `n - 1` equal pairs, so a single-element input returns `0`, and a run ending at the last index is also undercounted. It does process the last index, so the issue is not a missing final check. Initializing each nonempty run to `1` and updating `best` after every iteration correctly counts items, including singleton and trailing runs; empty inputs still return `0`.

输出 189 tok 延迟 6.3 s

GPT-6.1 Terra 通过 · 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.

输出 331 tok (+172 思考) 延迟 6.3 s

修复是否真的正确(能运行)、解释的信息密度,以及在一个范围明确的任务上的 token 效率。

提示词 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. 检查 合法 JSON,严格符合 schema

GPT-6.1 Sol 通过 · 5/5 fields, guidance "Withheld pending the Q4 close"

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

输出 345 tok (+285 思考) 延迟 10.2 s

GPT-6.1 Terra 通过 · 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" }

输出 317 tok (+261 思考) 延迟 6.9 s

是否严守 schema(不臆造字段)、能否顶住幻觉压力(原文明说暂不给出 guidance),以及结构化输出路径的差异。

提示词 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. 检查 120 词,0 个禁用词

GPT-6.1 Sol 通过 · 120 words, 0 banned, 1 question

Introducing Cross-Provider Prompt Cache, a new API gateway feature that stores reusable prompts and manages caching across your supported AI providers. Why rebuild the same context every time your application switches models? With one configuration, teams can reuse shared instructions, standardize cache policies, and reduce repeated prompt processing wherever provider caching is available. The gateway handles provider-specific requirements while giving you clear visibility into cache hits, usage, and estimated savings. Set expiration windows, isolate cached content by project, and invalidate entries when prompts change. Your existing routing logic stays intact, so you can compare models without rebuilding your caching workflow. […]

输出 588 tok (+435 思考) 延迟 13.9 s

GPT-6.1 Terra 通过 · 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.

输出 579 tok (+440 思考) 延迟 9.1 s

是否守住约束(字数预算、禁用词表、只能有一个问句)、文风特征,以及长度控制。

改一行代码就能在两个模型之间切换

下方每个标签页里都有两个模型 ID,高亮的那两行是唯一要改的地方。端点不变,API key 不变,请求结构也不变。

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-sol",
    # model="gpt-6.1-terra",  # 取消注释此行,注释上一行
    messages=[{"role": "user", "content": "Summarize this diff"}],
    reasoning_effort="medium",
)
print(resp.choices[0].message.content)

获取 API key →

常见问题

GPT-6.1 Sol 和 GPT-6.1 Terra 哪个更便宜?

按「输入 / 1M token」算,GPT-6.1 Terra 更便宜($1 对 $2,相差 2.0×)。其他计费项的结论可能相反,完整价格见上方表格,实际成本取决于你的用量构成。

不用分别集成两次,就能对 GPT-6.1 Sol 和 GPT-6.1 Terra 做 A/B 测试吗?

可以。两个模型走同一个 OpenAI 兼容端点,用同一个 API key,切换时只要改一行里的模型名,所以可以给两个模型各分一部分流量,直接对比账单。

GPT-6.1 Sol 和 GPT-6.1 Terra 支持提示词缓存吗?

支持。两个模型的缓存读取价都低于各自的输入价,所以前缀能反复命中缓存的负载,实际成本会比按官网价估算的低。具体的缓存读取价见上方价格表。

相关对比

我们的实测研究