Databricks:模型智能、性能与价格

Databricks
Databricks

本分析旨在帮助你根据使用场景,选择 Databricks 提供的最佳模型。

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最智能

Updated
#1
GLM-5.3 (max)GLM-5.3 (max)
45
#2
Kimi K3 (max)Kimi K3 (max)
44
#3
GLM-5.3-FlashGLM-5.3-Flash
42
#4
DeepSeek V4.1 Flash (max)DeepSeek V4.1 Flash (max)
40
#5
GLM-5.2 (max)GLM-5.2 (max)
34

Intelligence Index

共 9 个模型

速度最快

#1
GLM-5.2 (max)GLM-5.2 (max)
319 t/s
#2
DeepSeek V4.1 Flash (max)DeepSeek V4.1 Flash (max)
301 t/s
#3
GLM-5.2 (Non-reasoning)GLM-5.2 (Non-reasoning)
276 t/s
#4
GLM-5.3 (max)GLM-5.3 (max)
246 t/s
#5
GLM-5.3-FlashGLM-5.3-Flash
230 t/s

输出速度

共 9 个模型

价格最低

#1
DeepSeek V4.1 Flash (max)DeepSeek V4.1 Flash (max)
$0.08
#2
GLM-5.3-FlashGLM-5.3-Flash
$0.10
#3
Qwen3 Next 80B A3BQwen3 Next 80B A3B
$0.26
#4
InklingInkling
$0.72
#5
GLM-5.3 (max)GLM-5.3 (max)
$0.90

每 100 万 token 的混合价格

共 9 个模型

Databricks 提供 9 个模型,每个模型的智能、性能和价格特征各不相同。 下方对比了各模型的关键指标。

  • 智能方面,Databricks 上表现最好的模型是 GLM-5.3 (max)(45)、Kimi K3 (max)(44)和GLM-5.3-Flash(42)。
  • 输出速度方面,最快的模型是 GLM-5.2 (max)(319 t/s)、DeepSeek V4.1 Flash (max)(301 t/s)和GLM-5.2 (Non-reasoning)(276 t/s)。
  • 延迟方面,GLM-5.2 (Non-reasoning)(0.82 秒)、Qwen3 Next 80B A3B(0.91 秒)和GLM-5.2 (max)(7.08 秒) 的首个答案 Token 延迟最低。
  • 价格方面,DeepSeek V4.1 Flash (max)($0.08)、GLM-5.3-Flash($0.10)和Qwen3 Next 80B A3B($0.26) 每 100 万 token 的混合价格最低。 各模型价格最多相差 11.9 倍。
  • 上下文窗口方面,GLM-5.3 (max)(1M)、GLM-5.3-Flash(1M)和DeepSeek V4.1 Flash (max)(1M) 支持 Databricks 上最大的上下文窗口。

亮点

Updated
Artificial Analysis Intelligence Index · Higher is better
Output tokens per second · Higher is better
USD per 1M tokens (blended) · Lower is better

智能评测

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.3 incorporates 10 evaluations: AA-Briefcase, GDPval-AA v2, AutomationBench-AA, Terminal-Bench v4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1
Estimate (independent evaluation forthcoming)

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better
See more

Agentic knowledge work, (Elo-500)/2000

Agentic real-world work tasks, (Elo-500)/2000

Agentic SaaS workflows

Agentic coding & terminal use

Coding

Reasoning & knowledge

Professional document reasoning, All-pass

Physics reasoning

Long context reasoning

Legal agentic work, criterion pass rate

Agentic business operations

Quantitative analysis on spreadsheets & documents

Instruction following

Agentic tool use

Long-horizon agentic tasks

Kubernetes incident root-cause analysis

Visual reasoning

Intelligence Index vs. Price

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

上下文窗口

Context Window

Context window: tokens limit · Higher is better

价格

Intelligence Index vs. Price

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

性能摘要

Output Speed vs. Price

Output speed: output tokens per second · USD per 1M tokens (blended)
Most attractive quadrant
Pareto line

速度

按输出速度(每秒 token 数)衡量

Output Speed

Output tokens per second · Higher is better

延迟

按首 Token 延迟(秒)衡量

Latency: Time To First Answer Token

Seconds to first answer token received · Accounts for reasoning model 'thinking' time

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

End-to-End Response Time vs. Price

End-to-end response time: end-to-end seconds to output 500 tokens · USD per 1M tokens (blended)
Most attractive quadrant
Pareto line

进一步分析
Z AI 标志
GLM-5.3 (max)
1M
开放
45
$2.01
246
1.02
11.17
8.12
Kimi 标志
Kimi K3 (max)
205k
开放
44
$1.85
161
0.94
16.43
12.40
Z AI 标志
GLM-5.3-Flash
1M
开放
42
$0.28
230
0.70
11.59
8.71
DeepSeek 标志
DeepSeek V4.1 Flash (max)
1M
开放
40
$0.34
301
1.30
9.60
6.64
Z AI 标志
GLM-5.2 (max)
1M
开放
34
$0.87
319
0.80
8.65
6.28
Kimi 标志
Kimi K3 (low)
1M
开放
30
$0.88
154
1.00
17.28
13.02
Thinking Machines 标志
Inkling
1M
开放
26
$0.61
213
0.99
12.75
9.41
Z AI 标志
GLM-5.2 (Non-reasoning)
1M
开放
22*
--
276
0.82
2.63
--
Alibaba 标志
Qwen3 Next 80B A3B
262k
开放
10*
--
102
0.91
5.82
--

关键定义

常见问题

关于 Databricks 的常见问题