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模型候選名單

Best coding model APIs for agents and code review

Compare coding-oriented model APIs by context length, tool support, JSON output, latency estimate, price, and recommended production role.

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。

AnthropicCatalog

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...

$5 / 1M tokensInput$25 / 1M tokensOutput1MContext
Best forfrontier reasoning, large codebase review, strategy analysis
RoutingConfigured
Tool callingJSON modeLong contextReasoningStreamingVision
OpenRouter if availableOpenRouter public Models API live metadata; public price comes from the registry pricing rule
View details
AnthropicCatalog

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...

$3 / 1M tokensInput$15 / 1M tokensOutput1MContext
Best forcoding agents, code review, complex writing
RoutingConfigured
Tool callingJSON modeLong contextReasoningStreamingVision
OpenRouter if availableOpenRouter public Models API live metadata; public price comes from the registry pricing rule
View details
DeepSeekCatalog

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....

$0.7 / 1M tokensInput$2.50 / 1M tokensOutput163.8kContext
Best forChinese reasoning, math, analysis
RoutingConfigured
JSON modeLong contextReasoningStreamingTool calling
OpenRouter if availableOpenRouter public Models API live metadata; public price comes from the registry pricing rule
View details
VolcengineCatalog

Doubao Seed 2.0 Mini is an admin-staged public catalog draft sourced from Runtime Routing Provider.

¥0.2 / 1M tokensInput¥2 / 1M tokensOutput128kContext
Best forCoding
RoutingConfigured
StreamingJSON mode
Platform curatedNextModel admin-published catalog version; public metadata only, with live routing managed separately.
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
Anthropic: Claude Opus 4.7anthropic/claude-opus-4.7Anthropic$5 / 1M tokens$25 / 1M tokens1M
Tool callingJSON modeLong contextReasoning
frontier reasoning, large codebase review2300-6800msCatalogOpenRouter if available
Anthropic: Claude Sonnet 4.5anthropic/claude-sonnet-4.5Anthropic$3 / 1M tokens$15 / 1M tokens1M
Tool callingJSON modeLong contextReasoning
coding agents, code review1600-4800msCatalogOpenRouter if available
DeepSeek: R1deepseek/deepseek-r1DeepSeek$0.7 / 1M tokens$2.50 / 1M tokens163.8k
JSON modeLong contextReasoningStreaming
Chinese reasoning, math1800-6000msCatalogOpenRouter if available
Doubao Seed 2.0 Minidoubao-seed-2-0-miniVolcengine¥0.2 / 1M tokens¥2 / 1M tokens128k
StreamingJSON mode
Coding900-2600msCatalogPlatform curated
DeepSeek: DeepSeek V4 Flashdeepseek/deepseek-v4-flashDeepSeek$0.112 / 1M tokens$0.224 / 1M tokens1M
Tool callingJSON modeLong contextReasoning
low-cost Chinese tasks, long-context summary800-2600msCatalogOpenRouter if available
Qwen: Qwen3 Coder Plusqwen/qwen3-coder-plusAlibaba Cloud / Qwen$0.65 / 1M tokens$3.25 / 1M tokens1M
Tool callingJSON modeLong contextStreaming
Chinese engineering workflows, code generation1200-3900msCatalogOpenRouter 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.