Compare coding model APIs by context length, tools, JSON output, latency, price, and what role they should play in production.
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.
พื้นฐานของแหล่งข้อมูล: NextModel use-case taxonomy and OpenRouter supported-parameter metadata when available. · อัปเดตล่าสุด 2026-07-01
Fit score
เริ่มจากรายชื่อโมเดลตัวเลือก จากนั้นทดสอบด้วย prompt จริง และเปรียบเทียบค่าใช้จ่ายรายเดือนก่อนนำไปใช้กับ production routing
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.
Doubao-Seed-2.1-lite via NextModel: 1M-token context; $0.12 / 1M tokens in / $0.4 / 1M tokens out per 1M tokens. Capabilities: streaming.
Whisper Large V3 Turbo via NextModel: context unpublished; $0 / 1M tokens in / $0 / 1M tokens out per 1M tokens.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.
ตารางเปรียบเทียบ
ใช้มุมมองนี้เมื่อคุณกำลังคัด shortlist สำหรับ production สร้างนโยบาย fallback หรือเปรียบเทียบความคุ้มค่าของโมเดล
| 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 |
| Doubao-Seed-2.1-litedoubao-seed-2-1-lite | Volcengine | $0.12 / 1M tokens | $0.4 / 1M tokens | 1M | Streaming | Chinese Q&A, general chat | 2010-3295ms | 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 |
FAQ
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.
Cap budgets per project, watch output tokens, and send simple tasks to cheaper models. Escalate only when quality checks fail.
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