模型候選名單

Best coding model APIs for agents and code review

Compare coding model APIs by context length, tools, JSON output, latency, price, and what role they should play in production.

這份候選名單適合什麼用途?: Coding models

A model that completes a 20-line function is not the same product as a model that reads half a monorepo and calls tools. Output is expensive, tool calls fail in boring ways, and long context burns money. Use this page to pick a primary coding model and a cheaper fallback, then decide budget rules before agents run unsupervised.

來源依據: NextModel use-case taxonomy and OpenRouter supported-parameter metadata when available. · 更新日期 2026-07-01

如何使用这份名单

如何使用这份名单 (Coding models)

  1. 对照任务选模型. 先看「Coding models」短名单是否覆盖你的真实任务,不要只比标价。
  2. 用同一批提示词试跑. 挑 2–3 个候选,用业务提示词对比质量与输出长度。
  3. 估算月费. 用价格页或成本计算器,按预计 token 量估算月度花费。
  4. 定兜底与预算. 定主模型、兜底模型,设项目预算后再接生产流量。

匹配分

推薦候選 coding models

先從候選名單開始,再以真實提示詞測試,並在接入正式環境路由前比較月度成本。

DeepSeekCatalogHealthytypical

DeepSeek-V4-1-Flash-GLB via NextModel: 1M-token context; $0.3 / 1M tokens in / $1.20 / 1M tokens out per 1M tokens. Capabilities: streaming.

$0.3 / 1M tokensInput$1.20 / 1M tokensOutput1MContext
Best forChinese Q&A, general chat
Streaming
Platform curatedCurated and maintained in the NextModel catalog
View details
OpenAICatalog

Whisper Large V3 Turbo via NextModel: context unpublished; $0 / 1M tokens in / $0 / 1M tokens out per 1M tokens. Capabilities: streaming.

$0 / 1M tokensInput—Output—Context
Best forGeneral chat via OpenAI, API workloads
Streaming
Platform curatedCurated and maintained in the NextModel catalog
View details
GoogleCatalogHealthytypical

‌Gemini 4 Argon via NextModel: 1M-token context; $2 / 1M tokens in / $10 / 1M tokens out per 1M tokens. Capabilities: streaming, tool, json.

$2 / 1M tokensInput$10 / 1M tokensOutput1MContext
Best forimage understanding, multimodal chat
StreamingTool callingJSON modeVisionLong context
Platform curatedCurated and maintained in the NextModel catalog
View details
AnthropicCatalogHealthyfast

Claude Haiku 5.5 via NextModel: 1M-token context; Starting at $0.1 / 1M tokens in / $0.5 / 1M tokens out per 1M tokens. Capabilities: streaming, tool, json.

Starting at $0.1 / 1M tokensInput$0.5 / 1M tokensOutput1MContext
Best forimage understanding, multimodal chat
StreamingTool callingJSON modeVisionLong context
Platform curatedCurated and maintained in the NextModel catalog
View details

比較表

按價格、提供者、上下文、能力與來源比較這份候選名單。

當你在縮小正式環境候選名單、建立備援策略或比較模型經濟性時,可使用此視圖。

ModelProviderInputOutputContextCapabilitiesBest forLatencyStatusSource
DeepSeek-V4-1-Flash-GLBdeepseek/deepseek-v4-1-flash-glbDeepSeek$0.3 / 1M tokens$1.20 / 1M tokens1M
Streaming
Chinese Q&A, general chat1557-3097msCatalogPlatform curated
Whisper Large V3 Turboopenai/whisper-large-v3-turboOpenAI$0 / 1M tokens$0 / 1M tokens—
Streaming
General chat via OpenAI, API workloads0-0msCatalogPlatform curated
‌Gemini 4 Argongoogle/gemini-4-argonGoogle$2 / 1M tokens$10 / 1M tokens1M
StreamingTool callingJSON modeVision
image understanding, multimodal chat2974-10589msCatalogPlatform curated
Claude Haiku 5.5anthropic/claude-haiku-5.5AnthropicStarting at $0.1 / 1M tokens$0.5 / 1M tokens1M
StreamingTool callingJSON modeVision
image understanding, multimodal chat1321-2017msCatalogPlatform curated
Doubao Seedance 2.5doubao/doubao-seedance-2-5-260628Doubao$0.225 / s——
StreamingVision
image understanding, multimodal chat0-0msCatalogPlatform curated
Dreamina-Seedance 2.0 Fastdreamina/dreamina-seedance-2-0-fast-260128Dreamina$0.122 / s——
StreamingVision
image understanding, multimodal chat0-0msCatalogPlatform curated
Dreamina Seedance 2.5dreamina/dreamina-seedance-2-5-260628Dreamina$0.234 / s——
StreamingVision
image understanding, multimodal chat0-0msCatalogPlatform curated
Gemini 3 Pro Image (Nano Banana Pro) gemini/gemini-3-pro-imageGemini$0.314 / image——
StreamingVision
image understanding, multimodal chat0-0msCatalogPlatform curated

常見問題

Coding models 常見問題

What makes a model good for coding agents?

Reliable tool calling, structured output, enough context for the repo slice you send, and instructions it actually follows. Token price alone is a poor proxy.

How should teams control coding-agent cost?

Cap budgets per project, watch output tokens, and send simple tasks to cheaper models. Escalate only when quality checks fail.

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相关排行榜