GPT-6.1 Terra vs Qwen3.8 Max

vs

什么时候选哪个

输入不超过 272000 token 时,gpt-6.1-terra 的输入和输出价格更低。输入超过 272000 token 后,输入价格相同,qwen3.8-max 的输出更便宜;gpt-6.1-terra 的上下文更大,qwen3.8-max 的输出上限则略高。

Benchmark 成绩

GPT-6.1 Terra:供应商没有公布 benchmark 成绩。

高于同类均值无更高分Qwen3.8 Max31 / 408 / 40
GPT-6.1 Terra Qwen3.8 Max 其他参测模型 同类均值 ★ 没有模型得分更高
SWE-Bench Pro
N/A
67.7%
AndroidBench
N/A
75.1%
Cybergym
N/A
78.5%
HealthBench
N/A
没有模型得分更高 60.2%
JobBench
N/A
53.4%
PLawBench
N/A
没有模型得分更高 73.2%
GPQA Diamond
N/A
92.6%
Agents' Last Exam
N/A
27%

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

价格

GPT-6.1 Terra Qwen3.8 Max Δ
输入 / 1M token $1 $2 0.5×
输出 / 1M token $5 $6 0.83×
缓存读取 / 1M token $0.05 $0.25 0.2×
缓存写入 1.25x (30m) / 2.5x (12h) 1.25x -

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 Terra Qwen3.8 Max
工具调用 是 是
思考控制 可配置 是,但供应商未公布调节参数
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 explicit: 5m, reset on hit
最小缓存前缀 1,024 个 token 1,024 个 token

规格

GPT-6.1 Terra Qwen3.8 Max
输入模态 文本 图像 文本 图像
输出模态 文本 文本
发布日期 2026-10-26 2026-08-03
知识截止日期 2026-06 -
上下文窗口 1.1M 984K
最大输出 128K 131K
思考参数 reasoning.effort -
可选值
reasoning.effort
  • none
  • low
  • medium
  • high
  • xhigh
  • max
-
默认值 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 Terra · Qwen3.8 Max

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 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

Qwen3.8 Max 通过 · 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.

输出 378 tok (+305 思考) 延迟 8.6 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 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

Qwen3.8 Max 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 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.

输出 1616 tok (+1411 思考) 延迟 34.7 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 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

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

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

输出 1199 tok (+1141 思考) 延迟 24.4 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 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

Qwen3.8 Max 通过 · 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.

输出 2744 tok (+2591 思考) 延迟 46.3 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-terra",
    # model="qwen3.8-max",  # 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 Terra 和 Qwen3.8 Max 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 Terra 和 Qwen3.8 Max 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 Terra 和 Qwen3.8 Max 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