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.
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)
- 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。
- 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。
- 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。
- 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。
Whisper Large V3 Turbo via NextModel: context unpublished; $0 / 1M tokens in / $0 / 1M tokens out per 1M tokens. Capabilities: streaming.
Gemini 4 Argon via NextModel: 1M-token context; $2 / 1M tokens in / $10 / 1M tokens out per 1M tokens. Capabilities: streaming, tool, json.
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.
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。
| Model | Provider | Input | Output | Context | Capabilities | Best for | Latency | Status | Source |
|---|---|---|---|---|---|---|---|---|---|
| DeepSeek-V4-1-Flash-GLBdeepseek/deepseek-v4-1-flash-glb | DeepSeek | $0.3 / 1M tokens | $1.20 / 1M tokens | 1M | Streaming | Chinese Q&A, general chat | 1557-3097ms | Catalog | Platform curated |
| Whisper Large V3 Turboopenai/whisper-large-v3-turbo | OpenAI | $0 / 1M tokens | $0 / 1M tokens | — | Streaming | General chat via OpenAI, API workloads | 0-0ms | Catalog | Platform curated |
| Gemini 4 Argongoogle/gemini-4-argon | $2 / 1M tokens | $10 / 1M tokens | 1M | StreamingTool callingJSON modeVision | image understanding, multimodal chat | 2974-10589ms | Catalog | Platform curated | |
| Claude Haiku 5.5anthropic/claude-haiku-5.5 | Anthropic | Starting at $0.1 / 1M tokens | $0.5 / 1M tokens | 1M | StreamingTool callingJSON modeVision | image understanding, multimodal chat | 1321-2017ms | Catalog | Platform curated |
| Doubao Seedance 2.5doubao/doubao-seedance-2-5-260628 | Doubao | $0.225 / s | — | — | StreamingVision | image understanding, multimodal chat | 0-0ms | Catalog | Platform curated |
| Dreamina-Seedance 2.0 Fastdreamina/dreamina-seedance-2-0-fast-260128 | Dreamina | $0.122 / s | — | — | StreamingVision | image understanding, multimodal chat | 0-0ms | Catalog | Platform curated |
| Dreamina Seedance 2.5dreamina/dreamina-seedance-2-5-260628 | Dreamina | $0.234 / s | — | — | StreamingVision | image understanding, multimodal chat | 0-0ms | Catalog | Platform curated |
| Gemini 3 Pro Image (Nano Banana Pro) gemini/gemini-3-pro-image | Gemini | $0.314 / image | — | — | StreamingVision | image understanding, multimodal chat | 0-0ms | Catalog | Platform 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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