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DeepSeek V4 Pro (0813) vs GPT-5.6 Sol

vs

什么时候选哪个

两者都是支持文本输入的推理模型,具有大约一百万 token 的上下文(deepseek-v4-pro-0813 为 1,000,000,而 gpt-5.6-sol 为 1,050,000),因此区别在于成本和 I/O 形态:DeepSeek 的输入和输出收费分别为 $1.32 和 $3.96,而对应模型为 $5 和 $30,这使得 gpt-5.6-sol 的输入价格约为前者的 3.8x,输出价格约为前者的 7.6x,并且它还支持 393216 个输出 token,而后者为 128000。大规模生成长文本时请选择 deepseek-v4-pro-0813;当您需要图像输入或关闭思考功能的选项时请选择 gpt-5.6-sol,这两项均未在 DeepSeek 的规格表中列出。

Benchmark 成绩

领先高于同类均值无更高分DeepSeek V4 Pro (0813)313 / 211 / 21GPT-5.6 Sol1495 / 12128 / 121

17 项两边都有成绩。

DeepSeek V4 Pro (0813) GPT-5.6 Sol 其他参测模型 同类均值 ★ 没有模型得分更高
DeepSWE 1.1
62.7%
72.7%
BioMysteryBench hard
N/A
44.7%
OSWorld-Verified
N/A
83%
Cybergym
83.3%
84.5%
HealthBench Professional
N/A
60.5%
GDPval-AA v2 Elo · 1508-1769 据 Z.ai 公布 · 2026-09-04
1590
1730
Harvey Lab-AA
N/A
87.2%
GPQA Diamond
92.4%
94.6%
Agents' Last Exam
25.7%
53.6%
LVBench
N/A
82.1%

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

价格

DeepSeek V4 Pro (0813) GPT-5.6 Sol Δ
输入 / 1M token $1.32 $5 0.26×
输出 / 1M token $3.96 $30 0.13×
缓存读取 / 1M token $0.132 $0.5 0.26×
缓存写入 不单独收费 不单独收费 -

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 76 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)

能力

DeepSeek V4 Pro (0813) GPT-5.6 Sol
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(自动) 隐式(自动)
缓存有效期 no fixed TTL (evicted when unused) 5-10m, up to 1h
最小缓存前缀 未公布 1024 个 token

规格

DeepSeek V4 Pro (0813) GPT-5.6 Sol
输入模态 文本 文本 图像
输出模态 文本 文本
发布日期 2026-08-13 2026-07-09
知识截止日期 - 2026-02
上下文窗口 1M 1.1M
最大输出 393K 128K
思考参数 reasoning_effort reasoning.effort
可选值
reasoning_effort
  • the model card documents low
  • high
  • max
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: DeepSeek V4 Pro (0813) · GPT-5.6 Sol

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

DeepSeek V4 Pro (0813) 通过 · 3 sentences

Enabling reasoning causes the model to generate a hidden chain-of-thought with many additional tokens before producing the final answer, which sharply increases compute consumption per request. Those extra reasoning tokens are billed at the same or higher rates, so total usage doubled even though the volume of final outputs stayed similar. We can control this by adjusting reasoning effort, setting token limits, or routing only complex requests through reasoning mode.

输出 308 tok (+226 思考) 延迟 5.1 s

GPT-5.6 Sol 通过 · 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.

输出 111 tok (+34 思考) 延迟 2.3 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

DeepSeek V4 Pro (0813) 通过 · 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 else: cur = 1 best = max(best, cur) return best ``` The bug is that `cur` was initialized to `0` and only counted adjacent matching pairs, so a run of length `n` produced `n - 1` (or `0` for a single element) instead of `n`. The code also reset to `0` on a change, when the new run should start at length `1`. Initializing `cur` and `best` to `1` fixes both the single-element case and runs ending at the last index.

输出 1315 tok (+1130 思考) 延迟 16.9 s

GPT-5.6 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 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.

输出 316 tok (+135 思考) 延迟 5.2 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

DeepSeek V4 Pro (0813) 通过 · 5/5 fields, guidance null

{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":11.0,"guidance":null}

输出 2155 tok (+2121 思考) 延迟 26.0 s

GPT-5.6 Sol 通过 · 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"}

输出 194 tok (+147 思考) 延迟 3.2 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

DeepSeek V4 Pro (0813) 通过 · 121 words, 0 banned, 1 question

Introducing cross-provider prompt caching in our API gateway. Send a prompt once, and the gateway stores its computed prefix across Anthropic, OpenAI, and other LLM providers. Subsequent requests with the same prompt hit the cache, cutting latency and token costs while keeping outputs consistent across routing decisions and provider failovers. Teams can route identical prompts between providers without reprocessing shared context or lengthy system instructions. How much could you save on repeated prompt prefixes? The cache respects provider-specific key formats, handles TTLs automatically, and works with streaming and batch requests. Enable it with one configuration flag—no changes to your application code. Available today on all plans. Monitor cache hit rates, token savings, and provider-specific performance metrics in the live dashboard.

输出 2845 tok (+2694 思考) 延迟 25.5 s

GPT-5.6 Sol 通过 · 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. […]

输出 733 tok (+564 思考) 延迟 7.7 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="deepseek-v4-pro-0813",
    # model="gpt-5.6-sol",  # 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

DeepSeek V4 Pro (0813) 和 GPT-5.6 Sol 哪个更便宜?

按「输入 / 1M token」算,DeepSeek V4 Pro (0813) 更便宜($1.32 对 $5,相差 3.8×)。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 DeepSeek V4 Pro (0813) 和 GPT-5.6 Sol 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。

DeepSeek V4 Pro (0813) 和 GPT-5.6 Sol 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