This model is deprecated. We only continue performance benchmarking for the default 10k input token workload. Results for other workloads are historical and no longer updated.

Z AI has launched a newer model, GLM-4.7. We suggest considering it instead.

For more information, see comparison of GLM-4.7 to other models and API provider benchmarks for GLM-4.7.

GLM-4.6 (Reasoning) logo
Z AI
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Open weights model

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Released September 2025

GLM-4.6 (Reasoning) API 服务商基准测试与分析

模型比较

分析 GLM-4.6 (Reasoning) 各 API 服务商的性能指标，包括延迟（首 Token 延迟）、输出速度（每秒输出 token 数）、价格等。参与基准测试的 API 服务商包括 DeepInfra (FP4)、Together AI和Novita。

速度最快

#1
NovitaNovita
54.8 t/s
#2
DeepInfra (FP4)DeepInfra (FP4)
44.1 t/s

输出速度

共 3 家服务商

延迟最低

#1
NovitaNovita
39.13 s
#2
DeepInfra (FP4)DeepInfra (FP4)
46.34 s

首个答案 Token 延迟

共 3 家服务商

价格最低

#1
DeepInfra (FP4)DeepInfra (FP4)
$0.37
#2
NovitaNovita
$0.72
#3
Together AITogether AI
$0.76

每 100 万 token 的混合价格

共 3 家服务商

可通过 3 家 API 服务商使用 GLM-4.6，每家服务商的性能特征和价格各不相同。下方对比了各服务商的关键指标。

  • 输出速度最快的服务商是 Novita（54.8 t/s）和DeepInfra (FP4)（44.1 t/s）。
  • 延迟方面，Novita（39.13 秒）和DeepInfra (FP4)（46.34 秒） 的首个答案 Token 延迟最低。
  • 价格方面，DeepInfra (FP4)（0.37）、Novita（0.72）和Together AI（0.76） 每 100 万 token 的混合价格最低。
  • Novita 兼具最高速度和最低延迟，性能表现最佳。若要优化成本，DeepInfra (FP4) 的价格最具竞争力。

亮点

速度

Output tokens per second · Higher is better

端到端响应时间

Seconds · Lower is better

价格

USD per 1M tokens (blended) · Lower is better

价格

Pricing: Cache Hit, Input, and Output

Price (USD per M Tokens) · Lower is better · 10,000 input tokens

Price per token for cached prompts (previously processed), typically offering a significant discount compared to regular input price, represented as USD per million tokens. The values shown here are the cache hit price; cache write and cache storage are billed separately and vary by provider — see "Cache pricing by provider" for detail.

Pricing: Blended Price

Blended at 7:2:1 (cache-input-output) · USD per 1M tokens (blended) · Lower is better

Price per token, shown in USD per million tokens. Price is a blend of cache hit, input, and output token prices using the selected ratio (default 7:2:1 cache-input-output).

Pricing: Cache Discount

1 - (cache hit price / input price) · Higher is better

Reduction in input token cost due to cache hit relative to input price. Formula: 1 - (Cache Hit Price per Token / Input Token Price), where cache hit price is the first-party cache hit price or the median provider cache hit price. Note that this discount figure does not account for all costs associated with cache hits, such as cache write and storage costs.

Output Speed vs. Price

Blended at 7:2:1 (cache-input-output) · Output speed: output tokens per second · USD per 1M tokens (blended)
Most attractive quadrant
Pareto line

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

速度

按输出速度（每秒 token 数）衡量

Output Speed: GLM-4.6 (Reasoning)

Output speed: output tokens per second · 10,000 input tokens

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Latency vs. Output Speed

Latency: seconds to first token received · Output speed: output tokens per second · 10,000 input tokens
Most attractive quadrant
Pareto line

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

延迟

按首 Token 延迟（秒）衡量

首个答案 Token 延迟

Seconds to first token received · Lower is better · 10,000 input tokens

Time to first answer token received, in seconds, after API request sent. For reasoning models, this includes the 'thinking' time of the model before providing an answer. For models which do not support streaming, this represents time to receive the completion.

Figures represent median (P50) measurement over the past 72 hours to reflect sustained changes in performance.

端到端响应时间

Seconds to output 500 tokens, calculated based on time to first token, 'thinking' time for reasoning models, and output speed

端到端响应时间

Seconds to output 500 tokens, including reasoning model 'thinking' time · Lower is better · 10,000 input tokens

Seconds to receive a 500 token response. Key components:

  • Input time: Time to receive the first response token
  • Thinking time (only for reasoning models): Time reasoning models spend outputting tokens to reason prior to providing an answer. Amount of tokens based on the average reasoning tokens across a diverse set of 60 prompts (methodology details).
  • Answer time: Time to generate 500 output tokens, based on output speed

关键比较指标与 API 功能

DeepInfra 标志DeepInfra
203k
开放
--
45
0.97
56.99
44.81
Together AI 标志Together AI
203k
开放
$0.33
--
--
--
--
Novita 标志Novita
205k
开放
$0.30
57
2.74
46.26
34.81

常见问题

关于 GLM-4.6 (Reasoning) 服务商的常见问题

可通过 3 家 API 服务商使用 GLM-4.6 (Reasoning)：DeepInfra (FP4)、Together AI和Novita。每家服务商的性能特征和价格各不相同。

目前可通过我们进行基准测试和跟踪的 3 家 API 服务商使用 GLM-4.6 (Reasoning)。

按输出速度计算，GLM-4.6 (Reasoning) 速度最快的服务商是 Novita（54.8 t/s）和DeepInfra (FP4)（44.1 t/s）。输出速度衡量模型开始响应后生成 token 的速度。

GLM-4.6 (Reasoning) 的首个答案 Token 延迟最低的服务商是 Novita（39.13s）和DeepInfra (FP4)（46.34s）。延迟越低，初始响应越快。

按混合价格计算，GLM-4.6 (Reasoning) 最实惠的服务商是 DeepInfra (FP4)（每 100 万 token $0.37）、Novita（每 100 万 token $0.72）和Together AI（每 100 万 token $0.76）。混合价格采用缓存命中/输入/输出 token 为 7:2:1 的比例。

GLM-4.6 (Reasoning) 输入 token 价格最低的服务商是 DeepInfra (FP4)（每 100 万输入 token $0.50）、Novita（每 100 万输入 token $0.55）和Together AI（每 100 万输入 token $0.60）。

GLM-4.6 (Reasoning) 输出 token 价格最低的服务商是 DeepInfra (FP4)（每 100 万输出 token $2.00）、Together AI（每 100 万输出 token $2.20）和Novita（每 100 万输出 token $2.20）。

GLM-4.6 (Reasoning) 各服务商价格最多相差 2.1 倍。最实惠的是 DeepInfra (FP4)，每 100 万 token 收费 $0.37；Together AI 则收费 $0.76。

提供 GLM-4.6 (Reasoning) 的 3 家服务商中，有 2 家支持 JSON 模式：DeepInfra (FP4)和Novita。

提供 GLM-4.6 (Reasoning) 的全部 3 家服务商均支持函数调用（工具使用）。

对于 GLM-4.6 (Reasoning)，Novita 兼具最高速度和最低延迟，性能表现最佳。若要优化成本，DeepInfra (FP4) 的价格最具竞争力。

选择 GLM-4.6 (Reasoning) 服务商时，请考虑：输出速度（适合吞吐量密集型任务）、延迟（适合需要快速首次响应的交互式应用）、价格（适合成本敏感型工作负载），以及 JSON 模式或函数调用等 API 功能。

会。服务商性能可能因基础设施变化、负载均衡和更新而随时间波动。我们持续对所有服务商进行基准测试，并在“随时间变化”图表中展示历史性能趋势。

有关 GLM-4.6 (Reasoning) 的智能、能力、模态及其与其他模型的比较，请参阅模型概览页面。 查看模型概览