ernie-5.0
baidu
A natively omni-modal model with unified understanding of text, image, audio, and video, providing a flagship foundation for full-modality capabilities.
- 上下文
- 128K
- 输入 / 1M tokens
- $0.84
- 输出 / 1M tokens
- $3.28
- 缓存读 / 1M
- $0
- 缓存写 / 1M
- $0
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ernie-5.1
baidu
An efficient iteration of the flagship line, delivering major gains in reasoning, agentic handling, and search with far fewer parameters, balancing high performance and low-cost deployment.
- 上下文
- 128K
- 输入 / 1M tokens
- $0.56
- 输出 / 1M tokens
- $2.53
- 缓存读 / 1M
- $0
- 缓存写 / 1M
- $0
seed-2-1-turbo
bytedance
Turbo tier in Seed 2.1: multimodal model for coding and long-horizon agents, suited to multi-step task execution and visual content understanding.
- 上下文
- 262K
- 输入 / 1M tokens
- $0.5
- 输出 / 1M tokens
- $2.5
- 缓存读 / 1M
- $0.1
- 缓存写 / 1M
- $0.008
seed-1.8
bytedance
Agent-oriented foundation model tuned for tool calling and complex instruction following, with use cases spanning GUI agents, search agents, and multi-step task orchestration.
- 上下文
- 256K
- 输入 / 1M tokens
- $0.5
- 输出 / 1M tokens
- $4
- 缓存读 / 1M
- $0.05
- 缓存写 / 1M
- $0.008333
doubao-seed-2.0-code-preview-260215
bytedance
Code-focused preview model for agent workflows, with competitive SWE-Bench and LiveCodeBench scores, aimed at autonomous coding agents and multi-language software tasks.
- 上下文
- 256K
- 输入 / 1M tokens
- $0.5
- 输出 / 1M tokens
- $3
- 缓存读 / 1M
- $0.1
- 缓存写 / 1M
- $0.008333
doubao-seed-2.0-pro-260215
bytedance
Flagship of the Seed 2.0 series for complex reasoning and long-horizon agent workflows with reliable tool use.
- 上下文
- 256K
- 输入 / 1M tokens
- $0.5
- 输出 / 1M tokens
- $3
- 缓存读 / 1M
- $0.1
- 缓存写 / 1M
- $0.008333
deepseek-r1
DeepSeek
Open-source reasoning model with visible chain-of-thought, tuned for math, coding, and multi-step problem solving.
- 上下文
- 164K
- 输入 / 1M tokens
- $0.7
- 输出 / 1M tokens
- $2.5
- 缓存读 / 1M
- $0
- 缓存写 / 1M
- $0
deepseek-v3.1
DeepSeek
Hybrid model that switches between thinking and non-thinking modes on a single endpoint, post-trained for tool use and code agent workflows.
- 上下文
- 164K
- 输入 / 1M tokens
- $0.574
- 输出 / 1M tokens
- $1.721
- 缓存读 / 1M
- $0.13
- 缓存写 / 1M
- $0
deepseek-v4-pro
DeepSeek
DeepSeek V4 flagship with adjustable reasoning depth, built for full-codebase analysis, multi-step automation, and complex reasoning tasks.
- 上下文
- 1M
- 输入 / 1M tokens
- $0.66
- 输出 / 1M tokens
- $1.98
- 缓存读 / 1M
- $0.022
- 缓存写 / 1M
- $0
gemini-3-flash-preview
Speed-tier variant in the Gemini 3 line, pairing Pro-grade reasoning with Flash-level latency and cost, built for agent workflows and high-throughput interactive apps.
- 上下文
- 1M
- 输入 / 1M tokens
- $0.5
- 输出 / 1M tokens
- $3
- 缓存读 / 1M
- $0.05
- 缓存写 / 1M
- $0
minimax-m2.5-highspeed
minimax
A high-throughput variant of M2.5 with matching quality at roughly triple the speed, tuned for coding and agentic tool use under latency-sensitive workloads.
- 上下文
- 200K
- 输入 / 1M tokens
- $0.6
- 输出 / 1M tokens
- $2.4
- 缓存读 / 1M
- $0.03
- 缓存写 / 1M
- $0.375
minimax-m2.7-highspeed
minimax
Speed-optimized variant of M2.7 with identical outputs, tuned for low-latency coding and agent tool-calling workloads.
- 上下文
- 200K
- 输入 / 1M tokens
- $0.6
- 输出 / 1M tokens
- $2.4
- 缓存读 / 1M
- $0.06
- 缓存写 / 1M
- $0.375
kimi-k2-instruct
Moonshot
Instruction-tuned for agent workflows with reliable tool calling and multilingual coding.
- 上下文
- 131K
- 输入 / 1M tokens
- $0.574
- 输出 / 1M tokens
- $2.3
- 缓存读 / 1M
- $0.115
- 缓存写 / 1M
- $0
kimi-k2-thinking
Moonshot
Open-source thinking model built for long-horizon reasoning and hundreds of sequential tool calls across autonomous research, coding, and agent workflows.
- 上下文
- 262K
- 输入 / 1M tokens
- $0.6
- 输出 / 1M tokens
- $2.5
- 缓存读 / 1M
- $0.15
- 缓存写 / 1M
- $0
kimi-k2.5
Moonshot
Top Chinese-English bilingual. Strong long context.
- 上下文
- 262K
- 输入 / 1M tokens
- $0.6
- 输出 / 1M tokens
- $3.011
- 缓存读 / 1M
- $0.15
- 缓存写 / 1M
- $0.718
kimi-k2.6
Moonshot
Latest multimodal model in the K2 series, built for long-horizon coding, code-driven UI/UX generation, and multi-agent orchestration.
- 上下文
- 256K
- 输入 / 1M tokens
- $0.8939
- 输出 / 1M tokens
- $3.7131
- 缓存读 / 1M
- $0.34
- 缓存写 / 1M
- $1.1174
kimi-k2.7-code
Moonshot
Coding-focused variant in the Kimi K2 family. Accepts text and image inputs, with reasoning on by default. Suited for long-horizon coding and agentic workflows.
- 上下文
- 262K
- 输入 / 1M tokens
- $0.95
- 输出 / 1M tokens
- $4
- 缓存读 / 1M
- $0.19
- 缓存写 / 1M
- $1.1174
gpt-5.4-mini
OpenAI
Lightweight GPT-5.4 sibling tuned for coding, tool use, and agent workflows at roughly twice the speed of the 5.4 Pro tier.
- 上下文
- 400K
- 输入 / 1M tokens
- $0.75
- 输出 / 1M tokens
- $4.5
- 缓存读 / 1M
- $0.075
- 缓存写 / 1M
- $0
qwen3-max-preview
qwen
Deep-thinking preview that reasons step-by-step before answering, holding up on multi-step math, code, and Chinese-language tasks.
- 上下文
- 256K
- 输入 / 1M tokens
- $0.861
- 输出 / 1M tokens
- $3.441
- 缓存读 / 1M
- $0.173
- 缓存写 / 1M
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glm-4.6
Z.ai
Text-only model in the GLM-4 family with improved coding benchmarks and tool calls during reasoning, suited for software development and agent workflows.
- 上下文
- 203K
- 输入 / 1M tokens
- $0.574
- 输出 / 1M tokens
- $2.294
- 缓存读 / 1M
- $0.115
- 缓存写 / 1M
- $0
glm-4.7
Z.ai
Balanced model with strong Chinese understanding.
- 上下文
- 128K
- 输入 / 1M tokens
- $0.59
- 输出 / 1M tokens
- $2.35
- 缓存读 / 1M
- $0.55
- 缓存写 / 1M
- $0
glm-5
Z.ai
Competitive general-purpose Chinese model.
- 上下文
- 203K
- 输入 / 1M tokens
- $0.88
- 输出 / 1M tokens
- $3.23
- 缓存读 / 1M
- $0.172
- 缓存写 / 1M
- $0
glm-5.1
Z.ai
Offers significantly improved coding and long-horizon task capabilities, autonomously planning, executing, and refining a single task for over eight hours to deliver complete, engineering-grade results.
- 上下文
- 128K
- 输入 / 1M tokens
- $0.826
- 输出 / 1M tokens
- $3.303
- 缓存读 / 1M
- $1.035
- 缓存写 / 1M
- $1.376
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