Opus 4.7 is the next generation of Anthropic's Opus family, built for long-running, asynchronous agents. Building on the coding and agentic strengths of Opus 4.6, it delivers stronger performance on...
這份候選名單適合什麼用途?
Coding model selection depends on repository size, tool-calling needs, instruction reliability, and the cost of long output. A coding assistant that reads a large codebase needs different economics from a short code-completion feature. NextModel highlights coding candidates with context length, tool support, price, and best-use guidance so teams can choose a primary model and a fallback policy.
來源依據: NextModel use-case taxonomy and OpenRouter supported-parameter metadata when available.
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。
Claude Sonnet 4.5 is Anthropic’s most advanced Sonnet model to date, optimized for real-world agents and coding workflows. It delivers state-of-the-art performance on coding benchmarks such as SWE-bench Verified, with...
DeepSeek R1 is here: Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active in an inference pass....
Doubao Seed 2.0 Mini is an admin-staged public catalog draft sourced from Runtime Routing Provider.
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 |
|---|---|---|---|---|---|---|---|---|---|
| Anthropic: Claude Opus 4.7anthropic/claude-opus-4.7 | Anthropic | $5 / 1M tokens | $25 / 1M tokens | 1M | Tool callingJSON modeLong contextReasoning | frontier reasoning, large codebase review | 2300-6800ms | Catalog | OpenRouter if available |
| Anthropic: Claude Sonnet 4.5anthropic/claude-sonnet-4.5 | Anthropic | $3 / 1M tokens | $15 / 1M tokens | 1M | Tool callingJSON modeLong contextReasoning | coding agents, code review | 1600-4800ms | Catalog | OpenRouter if available |
| DeepSeek: R1deepseek/deepseek-r1 | DeepSeek | $0.7 / 1M tokens | $2.50 / 1M tokens | 163.8k | JSON modeLong contextReasoningStreaming | Chinese reasoning, math | 1800-6000ms | Catalog | OpenRouter if available |
| Doubao Seed 2.0 Minidoubao-seed-2-0-mini | Volcengine | ¥0.2 / 1M tokens | ¥2 / 1M tokens | 128k | StreamingJSON mode | Coding | 900-2600ms | Catalog | Platform curated |
| DeepSeek: DeepSeek V4 Flashdeepseek/deepseek-v4-flash | DeepSeek | $0.112 / 1M tokens | $0.224 / 1M tokens | 1M | Tool callingJSON modeLong contextReasoning | low-cost Chinese tasks, long-context summary | 800-2600ms | Catalog | OpenRouter if available |
| Qwen: Qwen3 Coder Plusqwen/qwen3-coder-plus | Alibaba Cloud / Qwen | $0.65 / 1M tokens | $3.25 / 1M tokens | 1M | Tool callingJSON modeLong contextStreaming | Chinese engineering workflows, code generation | 1200-3900ms | Catalog | OpenRouter if available |
常見問題
Coding models 常見問題
What makes a model good for coding agents?
Long context, reliable tool calling, structured output, and stable instruction following matter more than raw token price alone.
How should teams control coding-agent cost?
Use budget policies, compare output-heavy token cost, and route simple tasks to lower-cost models before escalating difficult tasks.